Unlocking Proteomic Heterogeneity in Complex Diseases through Visual Analytics

PubMed Central

Bhavnani, Suresh K.; Dang, Bryant; Bellala, Gowtham; Divekar, Rohit; Visweswaran, Shyam; Brasier, Allan; Kurosky, Alex

2015-01-01

Despite years of preclinical development, biological interventions designed to treat complex diseases like asthma often fail in phase III clinical trials. These failures suggest that current methods to analyze biomedical data might be missing critical aspects of biological complexity such as the assumption that cases and controls come from homogeneous distributions. Here we discuss why and how methods from the rapidly evolving field of visual analytics can help translational teams (consisting of biologists, clinicians, and bioinformaticians) to address the challenge of modeling and inferring heterogeneity in the proteomic and phenotypic profiles of patients with complex diseases. Because a primary goal of visual analytics is to amplify the cognitive capacities of humans for detecting patterns in complex data, we begin with an overview of the cognitive foundations for the field of visual analytics. Next, we organize the primary ways in which a specific form of visual analytics called networks have been used to model and infer biological mechanisms, which help to identify the properties of networks that are particularly useful for the discovery and analysis of proteomic heterogeneity in complex diseases. We describe one such approach called subject-protein networks, and demonstrate its application on two proteomic datasets. This demonstration provides insights to help translational teams overcome theoretical, practical, and pedagogical hurdles for the widespread use of subject-protein networks for analyzing molecular heterogeneities, with the translational goal of designing biomarker-based clinical trials, and accelerating the development of personalized approaches to medicine. PMID:25684269

Transformation of an uncertain video search pipeline to a sketch-based visual analytics loop.

PubMed

Legg, Philip A; Chung, David H S; Parry, Matthew L; Bown, Rhodri; Jones, Mark W; Griffiths, Iwan W; Chen, Min

2013-12-01

Traditional sketch-based image or video search systems rely on machine learning concepts as their core technology. However, in many applications, machine learning alone is impractical since videos may not be semantically annotated sufficiently, there may be a lack of suitable training data, and the search requirements of the user may frequently change for different tasks. In this work, we develop a visual analytics systems that overcomes the shortcomings of the traditional approach. We make use of a sketch-based interface to enable users to specify search requirement in a flexible manner without depending on semantic annotation. We employ active machine learning to train different analytical models for different types of search requirements. We use visualization to facilitate knowledge discovery at the different stages of visual analytics. This includes visualizing the parameter space of the trained model, visualizing the search space to support interactive browsing, visualizing candidature search results to support rapid interaction for active learning while minimizing watching videos, and visualizing aggregated information of the search results. We demonstrate the system for searching spatiotemporal attributes from sports video to identify key instances of the team and player performance.

Analytical Thinking, Analytical Action: Using Prelab Video Demonstrations and e-Quizzes to Improve Undergraduate Preparedness for Analytical Chemistry Practical Classes

ERIC Educational Resources Information Center

Jolley, Dianne F.; Wilson, Stephen R.; Kelso, Celine; O'Brien, Glennys; Mason, Claire E.

2016-01-01

This project utilizes visual and critical thinking approaches to develop a higher-education synergistic prelab training program for a large second-year undergraduate analytical chemistry class, directing more of the cognitive learning to the prelab phase. This enabled students to engage in more analytical thinking prior to engaging in the…

Collaborative Visual Analytics: A Health Analytics Approach to Injury Prevention

PubMed Central

Fisher, Brian; Smith, Jennifer; Pike, Ian

2017-01-01

Background: Accurate understanding of complex health data is critical in order to deal with wicked health problems and make timely decisions. Wicked problems refer to ill-structured and dynamic problems that combine multidimensional elements, which often preclude the conventional problem solving approach. This pilot study introduces visual analytics (VA) methods to multi-stakeholder decision-making sessions about child injury prevention; Methods: Inspired by the Delphi method, we introduced a novel methodology—group analytics (GA). GA was pilot-tested to evaluate the impact of collaborative visual analytics on facilitating problem solving and supporting decision-making. We conducted two GA sessions. Collected data included stakeholders’ observations, audio and video recordings, questionnaires, and follow up interviews. The GA sessions were analyzed using the Joint Activity Theory protocol analysis methods; Results: The GA methodology triggered the emergence of ‘common ground’ among stakeholders. This common ground evolved throughout the sessions to enhance stakeholders’ verbal and non-verbal communication, as well as coordination of joint activities and ultimately collaboration on problem solving and decision-making; Conclusions: Understanding complex health data is necessary for informed decisions. Equally important, in this case, is the use of the group analytics methodology to achieve ‘common ground’ among diverse stakeholders about health data and their implications. PMID:28895928

Collaborative Visual Analytics: A Health Analytics Approach to Injury Prevention.

PubMed

Al-Hajj, Samar; Fisher, Brian; Smith, Jennifer; Pike, Ian

2017-09-12

Background : Accurate understanding of complex health data is critical in order to deal with wicked health problems and make timely decisions. Wicked problems refer to ill-structured and dynamic problems that combine multidimensional elements, which often preclude the conventional problem solving approach. This pilot study introduces visual analytics (VA) methods to multi-stakeholder decision-making sessions about child injury prevention; Methods : Inspired by the Delphi method, we introduced a novel methodology-group analytics (GA). GA was pilot-tested to evaluate the impact of collaborative visual analytics on facilitating problem solving and supporting decision-making. We conducted two GA sessions. Collected data included stakeholders' observations, audio and video recordings, questionnaires, and follow up interviews. The GA sessions were analyzed using the Joint Activity Theory protocol analysis methods; Results : The GA methodology triggered the emergence of ' common g round ' among stakeholders. This common ground evolved throughout the sessions to enhance stakeholders' verbal and non-verbal communication, as well as coordination of joint activities and ultimately collaboration on problem solving and decision-making; Conclusion s : Understanding complex health data is necessary for informed decisions. Equally important, in this case, is the use of the group analytics methodology to achieve ' common ground' among diverse stakeholders about health data and their implications.

VAUD: A Visual Analysis Approach for Exploring Spatio-Temporal Urban Data.

PubMed

Chen, Wei; Huang, Zhaosong; Wu, Feiran; Zhu, Minfeng; Guan, Huihua; Maciejewski, Ross

2017-10-02

Urban data is massive, heterogeneous, and spatio-temporal, posing a substantial challenge for visualization and analysis. In this paper, we design and implement a novel visual analytics approach, Visual Analyzer for Urban Data (VAUD), that supports the visualization, querying, and exploration of urban data. Our approach allows for cross-domain correlation from multiple data sources by leveraging spatial-temporal and social inter-connectedness features. Through our approach, the analyst is able to select, filter, aggregate across multiple data sources and extract information that would be hidden to a single data subset. To illustrate the effectiveness of our approach, we provide case studies on a real urban dataset that contains the cyber-, physical-, and socialinformation of 14 million citizens over 22 days.

VAP/VAT: video analytics platform and test bed for testing and deploying video analytics

NASA Astrophysics Data System (ADS)

Gorodnichy, Dmitry O.; Dubrofsky, Elan

2010-04-01

Deploying Video Analytics in operational environments is extremely challenging. This paper presents a methodological approach developed by the Video Surveillance and Biometrics Section (VSB) of the Science and Engineering Directorate (S&E) of the Canada Border Services Agency (CBSA) to resolve these problems. A three-phase approach to enable VA deployment within an operational agency is presented and the Video Analytics Platform and Testbed (VAP/VAT) developed by the VSB section is introduced. In addition to allowing the integration of third party and in-house built VA codes into an existing video surveillance infrastructure, VAP/VAT also allows the agency to conduct an unbiased performance evaluation of the cameras and VA software available on the market. VAP/VAT consists of two components: EventCapture, which serves to Automatically detect a "Visual Event", and EventBrowser, which serves to Display & Peruse of "Visual Details" captured at the "Visual Event". To deal with Open architecture as well as with Closed architecture cameras, two video-feed capture mechanisms have been developed within the EventCapture component: IPCamCapture and ScreenCapture.

Visual Analytics of Surveillance Data on Foodborne Vibriosis, United States, 1973–2010

PubMed Central

Sims, Jennifer N.; Isokpehi, Raphael D.; Cooper, Gabrielle A.; Bass, Michael P.; Brown, Shyretha D.; St John, Alison L.; Gulig, Paul A.; Cohly, Hari H.P.

2011-01-01

Foodborne illnesses caused by microbial and chemical contaminants in food are a substantial health burden worldwide. In 2007, human vibriosis (non-cholera Vibrio infections) became a notifiable disease in the United States. In addition, Vibrio species are among the 31 major known pathogens transmitted through food in the United States. Diverse surveillance systems for foodborne pathogens also track outbreaks, illnesses, hospitalization and deaths due to non-cholera vibrios. Considering the recognition of vibriosis as a notifiable disease in the United States and the availability of diverse surveillance systems, there is a need for the development of easily deployed visualization and analysis approaches that can combine diverse data sources in an interactive manner. Current efforts to address this need are still limited. Visual analytics is an iterative process conducted via visual interfaces that involves collecting information, data preprocessing, knowledge representation, interaction, and decision making. We have utilized public domain outbreak and surveillance data sources covering 1973 to 2010, as well as visual analytics software to demonstrate integrated and interactive visualizations of data on foodborne outbreaks and surveillance of Vibrio species. Through the data visualization, we were able to identify unique patterns and/or novel relationships within and across datasets regarding (i) causative agent; (ii) foodborne outbreaks and illness per state; (iii) location of infection; (iv) vehicle (food) of infection; (v) anatomical site of isolation of Vibrio species; (vi) patients and complications of vibriosis; (vii) incidence of laboratory-confirmed vibriosis and V. parahaemolyticus outbreaks. The additional use of emerging visual analytics approaches for interaction with data on vibriosis, including non-foodborne related disease, can guide disease control and prevention as well as ongoing outbreak investigations. PMID:22174586

SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance.

PubMed

Sacha, Dominik; Kraus, Matthias; Bernard, Jurgen; Behrisch, Michael; Schreck, Tobias; Asano, Yuki; Keim, Daniel A

2018-01-01

Clustering is a core building block for data analysis, aiming to extract otherwise hidden structures and relations from raw datasets, such as particular groups that can be effectively related, compared, and interpreted. A plethora of visual-interactive cluster analysis techniques has been proposed to date, however, arriving at useful clusterings often requires several rounds of user interactions to fine-tune the data preprocessing and algorithms. We present a multi-stage Visual Analytics (VA) approach for iterative cluster refinement together with an implementation (SOMFlow) that uses Self-Organizing Maps (SOM) to analyze time series data. It supports exploration by offering the analyst a visual platform to analyze intermediate results, adapt the underlying computations, iteratively partition the data, and to reflect previous analytical activities. The history of previous decisions is explicitly visualized within a flow graph, allowing to compare earlier cluster refinements and to explore relations. We further leverage quality and interestingness measures to guide the analyst in the discovery of useful patterns, relations, and data partitions. We conducted two pair analytics experiments together with a subject matter expert in speech intonation research to demonstrate that the approach is effective for interactive data analysis, supporting enhanced understanding of clustering results as well as the interactive process itself.

Geovisual analytics to enhance spatial scan statistic interpretation: an analysis of U.S. cervical cancer mortality

PubMed Central

Chen, Jin; Roth, Robert E; Naito, Adam T; Lengerich, Eugene J; MacEachren, Alan M

2008-01-01

Background Kulldorff's spatial scan statistic and its software implementation – SaTScan – are widely used for detecting and evaluating geographic clusters. However, two issues make using the method and interpreting its results non-trivial: (1) the method lacks cartographic support for understanding the clusters in geographic context and (2) results from the method are sensitive to parameter choices related to cluster scaling (abbreviated as scaling parameters), but the system provides no direct support for making these choices. We employ both established and novel geovisual analytics methods to address these issues and to enhance the interpretation of SaTScan results. We demonstrate our geovisual analytics approach in a case study analysis of cervical cancer mortality in the U.S. Results We address the first issue by providing an interactive visual interface to support the interpretation of SaTScan results. Our research to address the second issue prompted a broader discussion about the sensitivity of SaTScan results to parameter choices. Sensitivity has two components: (1) the method can identify clusters that, while being statistically significant, have heterogeneous contents comprised of both high-risk and low-risk locations and (2) the method can identify clusters that are unstable in location and size as the spatial scan scaling parameter is varied. To investigate cluster result stability, we conducted multiple SaTScan runs with systematically selected parameters. The results, when scanning a large spatial dataset (e.g., U.S. data aggregated by county), demonstrate that no single spatial scan scaling value is known to be optimal to identify clusters that exist at different scales; instead, multiple scans that vary the parameters are necessary. We introduce a novel method of measuring and visualizing reliability that facilitates identification of homogeneous clusters that are stable across analysis scales. Finally, we propose a logical approach to proceed through the analysis of SaTScan results. Conclusion The geovisual analytics approach described in this manuscript facilitates the interpretation of spatial cluster detection methods by providing cartographic representation of SaTScan results and by providing visualization methods and tools that support selection of SaTScan parameters. Our methods distinguish between heterogeneous and homogeneous clusters and assess the stability of clusters across analytic scales. Method We analyzed the cervical cancer mortality data for the United States aggregated by county between 2000 and 2004. We ran SaTScan on the dataset fifty times with different parameter choices. Our geovisual analytics approach couples SaTScan with our visual analytic platform, allowing users to interactively explore and compare SaTScan results produced by different parameter choices. The Standardized Mortality Ratio and reliability scores are visualized for all the counties to identify stable, homogeneous clusters. We evaluated our analysis result by comparing it to that produced by other independent techniques including the Empirical Bayes Smoothing and Kafadar spatial smoother methods. The geovisual analytics approach introduced here is developed and implemented in our Java-based Visual Inquiry Toolkit. PMID:18992163

Geovisual analytics to enhance spatial scan statistic interpretation: an analysis of U.S. cervical cancer mortality.

PubMed

Chen, Jin; Roth, Robert E; Naito, Adam T; Lengerich, Eugene J; Maceachren, Alan M

2008-11-07

Kulldorff's spatial scan statistic and its software implementation - SaTScan - are widely used for detecting and evaluating geographic clusters. However, two issues make using the method and interpreting its results non-trivial: (1) the method lacks cartographic support for understanding the clusters in geographic context and (2) results from the method are sensitive to parameter choices related to cluster scaling (abbreviated as scaling parameters), but the system provides no direct support for making these choices. We employ both established and novel geovisual analytics methods to address these issues and to enhance the interpretation of SaTScan results. We demonstrate our geovisual analytics approach in a case study analysis of cervical cancer mortality in the U.S. We address the first issue by providing an interactive visual interface to support the interpretation of SaTScan results. Our research to address the second issue prompted a broader discussion about the sensitivity of SaTScan results to parameter choices. Sensitivity has two components: (1) the method can identify clusters that, while being statistically significant, have heterogeneous contents comprised of both high-risk and low-risk locations and (2) the method can identify clusters that are unstable in location and size as the spatial scan scaling parameter is varied. To investigate cluster result stability, we conducted multiple SaTScan runs with systematically selected parameters. The results, when scanning a large spatial dataset (e.g., U.S. data aggregated by county), demonstrate that no single spatial scan scaling value is known to be optimal to identify clusters that exist at different scales; instead, multiple scans that vary the parameters are necessary. We introduce a novel method of measuring and visualizing reliability that facilitates identification of homogeneous clusters that are stable across analysis scales. Finally, we propose a logical approach to proceed through the analysis of SaTScan results. The geovisual analytics approach described in this manuscript facilitates the interpretation of spatial cluster detection methods by providing cartographic representation of SaTScan results and by providing visualization methods and tools that support selection of SaTScan parameters. Our methods distinguish between heterogeneous and homogeneous clusters and assess the stability of clusters across analytic scales. We analyzed the cervical cancer mortality data for the United States aggregated by county between 2000 and 2004. We ran SaTScan on the dataset fifty times with different parameter choices. Our geovisual analytics approach couples SaTScan with our visual analytic platform, allowing users to interactively explore and compare SaTScan results produced by different parameter choices. The Standardized Mortality Ratio and reliability scores are visualized for all the counties to identify stable, homogeneous clusters. We evaluated our analysis result by comparing it to that produced by other independent techniques including the Empirical Bayes Smoothing and Kafadar spatial smoother methods. The geovisual analytics approach introduced here is developed and implemented in our Java-based Visual Inquiry Toolkit.

A results-based process for evaluation of diverse visual analytics tools

NASA Astrophysics Data System (ADS)

Rubin, Gary; Berger, David H.

2013-05-01

With the pervasiveness of still and full-motion imagery in commercial and military applications, the need to ingest and analyze these media has grown rapidly in recent years. Additionally, video hosting and live camera websites provide a near real-time view of our changing world with unprecedented spatial coverage. To take advantage of these controlled and crowd-sourced opportunities, sophisticated visual analytics (VA) tools are required to accurately and efficiently convert raw imagery into usable information. Whether investing in VA products or evaluating algorithms for potential development, it is important for stakeholders to understand the capabilities and limitations of visual analytics tools. Visual analytics algorithms are being applied to problems related to Intelligence, Surveillance, and Reconnaissance (ISR), facility security, and public safety monitoring, to name a few. The diversity of requirements means that a onesize- fits-all approach to performance assessment will not work. We present a process for evaluating the efficacy of algorithms in real-world conditions, thereby allowing users and developers of video analytics software to understand software capabilities and identify potential shortcomings. The results-based approach described in this paper uses an analysis of end-user requirements and Concept of Operations (CONOPS) to define Measures of Effectiveness (MOEs), test data requirements, and evaluation strategies. We define metrics that individually do not fully characterize a system, but when used together, are a powerful way to reveal both strengths and weaknesses. We provide examples of data products, such as heatmaps, performance maps, detection timelines, and rank-based probability-of-detection curves.

TrajGraph: A Graph-Based Visual Analytics Approach to Studying Urban Network Centralities Using Taxi Trajectory Data.

PubMed

Huang, Xiaoke; Zhao, Ye; Yang, Jing; Zhang, Chong; Ma, Chao; Ye, Xinyue

2016-01-01

We propose TrajGraph, a new visual analytics method, for studying urban mobility patterns by integrating graph modeling and visual analysis with taxi trajectory data. A special graph is created to store and manifest real traffic information recorded by taxi trajectories over city streets. It conveys urban transportation dynamics which can be discovered by applying graph analysis algorithms. To support interactive, multiscale visual analytics, a graph partitioning algorithm is applied to create region-level graphs which have smaller size than the original street-level graph. Graph centralities, including Pagerank and betweenness, are computed to characterize the time-varying importance of different urban regions. The centralities are visualized by three coordinated views including a node-link graph view, a map view and a temporal information view. Users can interactively examine the importance of streets to discover and assess city traffic patterns. We have implemented a fully working prototype of this approach and evaluated it using massive taxi trajectories of Shenzhen, China. TrajGraph's capability in revealing the importance of city streets was evaluated by comparing the calculated centralities with the subjective evaluations from a group of drivers in Shenzhen. Feedback from a domain expert was collected. The effectiveness of the visual interface was evaluated through a formal user study. We also present several examples and a case study to demonstrate the usefulness of TrajGraph in urban transportation analysis.

Semantic Interaction for Sensemaking: Inferring Analytical Reasoning for Model Steering.

PubMed

Endert, A; Fiaux, P; North, C

2012-12-01

Visual analytic tools aim to support the cognitively demanding task of sensemaking. Their success often depends on the ability to leverage capabilities of mathematical models, visualization, and human intuition through flexible, usable, and expressive interactions. Spatially clustering data is one effective metaphor for users to explore similarity and relationships between information, adjusting the weighting of dimensions or characteristics of the dataset to observe the change in the spatial layout. Semantic interaction is an approach to user interaction in such spatializations that couples these parametric modifications of the clustering model with users' analytic operations on the data (e.g., direct document movement in the spatialization, highlighting text, search, etc.). In this paper, we present results of a user study exploring the ability of semantic interaction in a visual analytic prototype, ForceSPIRE, to support sensemaking. We found that semantic interaction captures the analytical reasoning of the user through keyword weighting, and aids the user in co-creating a spatialization based on the user's reasoning and intuition.

A Visual Analytics Approach to Structured Data Analysis to Enhance Nonproliferation and Arms Control Verification Activities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gillen, David S.

Analysis activities for Nonproliferation and Arms Control verification require the use of many types of data. Tabular structured data, such as Excel spreadsheets and relational databases, have traditionally been used for data mining activities, where specific queries are issued against data to look for matching results. The application of visual analytics tools to structured data enables further exploration of datasets to promote discovery of previously unknown results. This paper discusses the application of a specific visual analytics tool to datasets related to the field of Arms Control and Nonproliferation to promote the use of visual analytics more broadly in thismore » domain. Visual analytics focuses on analytical reasoning facilitated by interactive visual interfaces (Wong and Thomas 2004). It promotes exploratory analysis of data, and complements data mining technologies where known patterns can be mined for. Also with a human in the loop, they can bring in domain knowledge and subject matter expertise. Visual analytics has not widely been applied to this domain. In this paper, we will focus on one type of data: structured data, and show the results of applying a specific visual analytics tool to answer questions in the Arms Control and Nonproliferation domain. We chose to use the T.Rex tool, a visual analytics tool developed at PNNL, which uses a variety of visual exploration patterns to discover relationships in structured datasets, including a facet view, graph view, matrix view, and timeline view. The facet view enables discovery of relationships between categorical information, such as countries and locations. The graph tool visualizes node-link relationship patterns, such as the flow of materials being shipped between parties. The matrix visualization shows highly correlated categories of information. The timeline view shows temporal patterns in data. In this paper, we will use T.Rex with two different datasets to demonstrate how interactive exploration of the data can aid an analyst with arms control and nonproliferation verification activities. Using a dataset from PIERS (PIERS 2014), we will show how container shipment imports and exports can aid an analyst in understanding the shipping patterns between two countries. We will also use T.Rex to examine a collection of research publications from the IAEA International Nuclear Information System (IAEA 2014) to discover collaborations of concern. We hope this paper will encourage the use of visual analytics structured data analytics in the field of nonproliferation and arms control verification. Our paper outlines some of the challenges that exist before broad adoption of these kinds of tools can occur and offers next steps to overcome these challenges.« less

Report: New analytical and statistical approaches for interpreting the relationships among environmental stressors and biomarkers

EPA Science Inventory

The broad topic of biomarker research has an often-overlooked component: the documentation and interpretation of the surrounding chemical environment and other meta-data, especially from visualization, analytical, and statistical perspectives (Pleil et al. 2014; Sobus et al. 2011...

Combining data visualization and statistical approaches for interpreting measurements and meta-data: Integrating heatmaps, variable clustering, and mixed regression models

EPA Science Inventory

The advent of new higher throughput analytical instrumentation has put a strain on interpreting and explaining the results from complex studies. Contemporary human, environmental, and biomonitoring data sets are comprised of tens or hundreds of analytes, multiple repeat measures...

SmartAdP: Visual Analytics of Large-scale Taxi Trajectories for Selecting Billboard Locations.

PubMed

Liu, Dongyu; Weng, Di; Li, Yuhong; Bao, Jie; Zheng, Yu; Qu, Huamin; Wu, Yingcai

2017-01-01

The problem of formulating solutions immediately and comparing them rapidly for billboard placements has plagued advertising planners for a long time, owing to the lack of efficient tools for in-depth analyses to make informed decisions. In this study, we attempt to employ visual analytics that combines the state-of-the-art mining and visualization techniques to tackle this problem using large-scale GPS trajectory data. In particular, we present SmartAdP, an interactive visual analytics system that deals with the two major challenges including finding good solutions in a huge solution space and comparing the solutions in a visual and intuitive manner. An interactive framework that integrates a novel visualization-driven data mining model enables advertising planners to effectively and efficiently formulate good candidate solutions. In addition, we propose a set of coupled visualizations: a solution view with metaphor-based glyphs to visualize the correlation between different solutions; a location view to display billboard locations in a compact manner; and a ranking view to present multi-typed rankings of the solutions. This system has been demonstrated using case studies with a real-world dataset and domain-expert interviews. Our approach can be adapted for other location selection problems such as selecting locations of retail stores or restaurants using trajectory data.

Matisse: A Visual Analytics System for Exploring Emotion Trends in Social Media Text Streams

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steed, Chad A; Drouhard, Margaret MEG G; Beaver, Justin M

Dynamically mining textual information streams to gain real-time situational awareness is especially challenging with social media systems where throughput and velocity properties push the limits of a static analytical approach. In this paper, we describe an interactive visual analytics system, called Matisse, that aids with the discovery and investigation of trends in streaming text. Matisse addresses the challenges inherent to text stream mining through the following technical contributions: (1) robust stream data management, (2) automated sentiment/emotion analytics, (3) interactive coordinated visualizations, and (4) a flexible drill-down interaction scheme that accesses multiple levels of detail. In addition to positive/negative sentiment prediction,more » Matisse provides fine-grained emotion classification based on Valence, Arousal, and Dominance dimensions and a novel machine learning process. Information from the sentiment/emotion analytics are fused with raw data and summary information to feed temporal, geospatial, term frequency, and scatterplot visualizations using a multi-scale, coordinated interaction model. After describing these techniques, we conclude with a practical case study focused on analyzing the Twitter sample stream during the week of the 2013 Boston Marathon bombings. The case study demonstrates the effectiveness of Matisse at providing guided situational awareness of significant trends in social media streams by orchestrating computational power and human cognition.« less

Visualizing the Solute Vaporization Interference in Flame Atomic Absorption Spectroscopy

ERIC Educational Resources Information Center

Dockery, Christopher R.; Blew, Michael J.; Goode, Scott R.

2008-01-01

Every day, tens of thousands of chemists use analytical atomic spectroscopy in their work, often without knowledge of possible interferences. We present a unique approach to study these interferences by using modern response surface methods to visualize an interference in which aluminum depresses the calcium atomic absorption signal. Calcium…

Use of multiple colorimetric indicators for paper-based microfluidic devices.

PubMed

Dungchai, Wijitar; Chailapakul, Orawon; Henry, Charles S

2010-08-03

We report here the use of multiple indicators for a single analyte for paper-based microfluidic devices (microPAD) in an effort to improve the ability to visually discriminate between analyte concentrations. In existing microPADs, a single dye system is used for the measurement of a single analyte. In our approach, devices are designed to simultaneously quantify analytes using multiple indicators for each analyte improving the accuracy of the assay. The use of multiple indicators for a single analyte allows for different indicator colors to be generated at different analyte concentration ranges as well as increasing the ability to better visually discriminate colors. The principle of our devices is based on the oxidation of indicators by hydrogen peroxide produced by oxidase enzymes specific for each analyte. Each indicator reacts at different peroxide concentrations and therefore analyte concentrations, giving an extended range of operation. To demonstrate the utility of our approach, the mixture of 4-aminoantipyrine and 3,5-dichloro-2-hydroxy-benzenesulfonic acid, o-dianisidine dihydrochloride, potassium iodide, acid black, and acid yellow were chosen as the indicators for simultaneous semi-quantitative measurement of glucose, lactate, and uric acid on a microPAD. Our approach was successfully applied to quantify glucose (0.5-20 mM), lactate (1-25 mM), and uric acid (0.1-7 mM) in clinically relevant ranges. The determination of glucose, lactate, and uric acid in control serum and urine samples was also performed to demonstrate the applicability of this device for biological sample analysis. Finally results for the multi-indicator and single indicator system were compared using untrained readers to demonstrate the improvements in accuracy achieved with the new system. 2010 Elsevier B.V. All rights reserved.

IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics

PubMed Central

2016-01-01

Background We live in an era of explosive data generation that will continue to grow and involve all industries. One of the results of this explosion is the need for newer and more efficient data analytics procedures. Traditionally, data analytics required a substantial background in statistics and computer science. In 2015, International Business Machines Corporation (IBM) released the IBM Watson Analytics (IBMWA) software that delivered advanced statistical procedures based on the Statistical Package for the Social Sciences (SPSS). The latest entry of Watson Analytics into the field of analytical software products provides users with enhanced functions that are not available in many existing programs. For example, Watson Analytics automatically analyzes datasets, examines data quality, and determines the optimal statistical approach. Users can request exploratory, predictive, and visual analytics. Using natural language processing (NLP), users are able to submit additional questions for analyses in a quick response format. This analytical package is available free to academic institutions (faculty and students) that plan to use the tools for noncommercial purposes. Objective To report the features of IBMWA and discuss how this software subjectively and objectively compares to other data mining programs. Methods The salient features of the IBMWA program were examined and compared with other common analytical platforms, using validated health datasets. Results Using a validated dataset, IBMWA delivered similar predictions compared with several commercial and open source data mining software applications. The visual analytics generated by IBMWA were similar to results from programs such as Microsoft Excel and Tableau Software. In addition, assistance with data preprocessing and data exploration was an inherent component of the IBMWA application. Sensitivity and specificity were not included in the IBMWA predictive analytics results, nor were odds ratios, confidence intervals, or a confusion matrix. Conclusions IBMWA is a new alternative for data analytics software that automates descriptive, predictive, and visual analytics. This program is very user-friendly but requires data preprocessing, statistical conceptual understanding, and domain expertise. PMID:27729304

IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics.

PubMed

Hoyt, Robert Eugene; Snider, Dallas; Thompson, Carla; Mantravadi, Sarita

2016-10-11

We live in an era of explosive data generation that will continue to grow and involve all industries. One of the results of this explosion is the need for newer and more efficient data analytics procedures. Traditionally, data analytics required a substantial background in statistics and computer science. In 2015, International Business Machines Corporation (IBM) released the IBM Watson Analytics (IBMWA) software that delivered advanced statistical procedures based on the Statistical Package for the Social Sciences (SPSS). The latest entry of Watson Analytics into the field of analytical software products provides users with enhanced functions that are not available in many existing programs. For example, Watson Analytics automatically analyzes datasets, examines data quality, and determines the optimal statistical approach. Users can request exploratory, predictive, and visual analytics. Using natural language processing (NLP), users are able to submit additional questions for analyses in a quick response format. This analytical package is available free to academic institutions (faculty and students) that plan to use the tools for noncommercial purposes. To report the features of IBMWA and discuss how this software subjectively and objectively compares to other data mining programs. The salient features of the IBMWA program were examined and compared with other common analytical platforms, using validated health datasets. Using a validated dataset, IBMWA delivered similar predictions compared with several commercial and open source data mining software applications. The visual analytics generated by IBMWA were similar to results from programs such as Microsoft Excel and Tableau Software. In addition, assistance with data preprocessing and data exploration was an inherent component of the IBMWA application. Sensitivity and specificity were not included in the IBMWA predictive analytics results, nor were odds ratios, confidence intervals, or a confusion matrix. IBMWA is a new alternative for data analytics software that automates descriptive, predictive, and visual analytics. This program is very user-friendly but requires data preprocessing, statistical conceptual understanding, and domain expertise.

DIVE: A Graph-based Visual Analytics Framework for Big Data

PubMed Central

Rysavy, Steven J.; Bromley, Dennis; Daggett, Valerie

2014-01-01

The need for data-centric scientific tools is growing; domains like biology, chemistry, and physics are increasingly adopting computational approaches. As a result, scientists must now deal with the challenges of big data. To address these challenges, we built a visual analytics platform named DIVE: Data Intensive Visualization Engine. DIVE is a data-agnostic, ontologically-expressive software framework capable of streaming large datasets at interactive speeds. Here we present the technical details of the DIVE platform, multiple usage examples, and a case study from the Dynameomics molecular dynamics project. We specifically highlight our novel contributions to structured data model manipulation and high-throughput streaming of large, structured datasets. PMID:24808197

Visual analytics as a translational cognitive science.

PubMed

Fisher, Brian; Green, Tera Marie; Arias-Hernández, Richard

2011-07-01

Visual analytics is a new interdisciplinary field of study that calls for a more structured scientific approach to understanding the effects of interaction with complex graphical displays on human cognitive processes. Its primary goal is to support the design and evaluation of graphical information systems that better support cognitive processes in areas as diverse as scientific research and emergency management. The methodologies that make up this new field are as yet ill defined. This paper proposes a pathway for development of visual analytics as a translational cognitive science that bridges fundamental research in human/computer cognitive systems and design and evaluation of information systems in situ. Achieving this goal will require the development of enhanced field methods for conceptual decomposition of human/computer cognitive systems that maps onto laboratory studies, and improved methods for conducting laboratory investigations that might better map onto real-world cognitive processes in technology-rich environments. Copyright © 2011 Cognitive Science Society, Inc.

Liquid-to-gel transition for visual and tactile detection of biological analytes.

PubMed

Fedotova, Tatiana A; Kolpashchikov, Dmitry M

2017-11-23

So far all visual and instrument-free methods have been based on a color change. However, colorimetric assays cannot be used by blind or color-blind people. Here we introduce a liquid-to-gel transition as a general output platform. The signal output (a piece of gel) can be unambiguously distinguished from liquid both visually and by touch. This approach promises to contribute to the development of an accessible environment for visually impaired persons.

Combining computational analyses and interactive visualization for document exploration and sensemaking in jigsaw.

PubMed

Görg, Carsten; Liu, Zhicheng; Kihm, Jaeyeon; Choo, Jaegul; Park, Haesun; Stasko, John

2013-10-01

Investigators across many disciplines and organizations must sift through large collections of text documents to understand and piece together information. Whether they are fighting crime, curing diseases, deciding what car to buy, or researching a new field, inevitably investigators will encounter text documents. Taking a visual analytics approach, we integrate multiple text analysis algorithms with a suite of interactive visualizations to provide a flexible and powerful environment that allows analysts to explore collections of documents while sensemaking. Our particular focus is on the process of integrating automated analyses with interactive visualizations in a smooth and fluid manner. We illustrate this integration through two example scenarios: an academic researcher examining InfoVis and VAST conference papers and a consumer exploring car reviews while pondering a purchase decision. Finally, we provide lessons learned toward the design and implementation of visual analytics systems for document exploration and understanding.

Progressive Visual Analytics: User-Driven Visual Exploration of In-Progress Analytics.

PubMed

Stolper, Charles D; Perer, Adam; Gotz, David

2014-12-01

As datasets grow and analytic algorithms become more complex, the typical workflow of analysts launching an analytic, waiting for it to complete, inspecting the results, and then re-Iaunching the computation with adjusted parameters is not realistic for many real-world tasks. This paper presents an alternative workflow, progressive visual analytics, which enables an analyst to inspect partial results of an algorithm as they become available and interact with the algorithm to prioritize subspaces of interest. Progressive visual analytics depends on adapting analytical algorithms to produce meaningful partial results and enable analyst intervention without sacrificing computational speed. The paradigm also depends on adapting information visualization techniques to incorporate the constantly refining results without overwhelming analysts and provide interactions to support an analyst directing the analytic. The contributions of this paper include: a description of the progressive visual analytics paradigm; design goals for both the algorithms and visualizations in progressive visual analytics systems; an example progressive visual analytics system (Progressive Insights) for analyzing common patterns in a collection of event sequences; and an evaluation of Progressive Insights and the progressive visual analytics paradigm by clinical researchers analyzing electronic medical records.

Uncertainty Representation in Visualizations of Learning Analytics for Learners: Current Approaches and Opportunities

ERIC Educational Resources Information Center

Demmans Epp, Carrie; Bull, Susan

2015-01-01

Adding uncertainty information to visualizations is becoming increasingly common across domains since its addition helps ensure that informed decisions are made. This work has shown the difficulty that is inherent to representing uncertainty. Moreover, the representation of uncertainty has yet to be thoroughly explored in educational domains even…

Single Cell Proteomics in Biomedicine: High-dimensional Data Acquisition, Visualization and Analysis

PubMed Central

Su, Yapeng; Shi, Qihui; Wei, Wei

2017-01-01

New insights on cellular heterogeneity in the last decade provoke the development of a variety of single cell omics tools at a lightning pace. The resultant high-dimensional single cell data generated by these tools require new theoretical approaches and analytical algorithms for effective visualization and interpretation. In this review, we briefly survey the state-of-the-art single cell proteomic tools with a particular focus on data acquisition and quantification, followed by an elaboration of a number of statistical and computational approaches developed to date for dissecting the high-dimensional single cell data. The underlying assumptions, unique features and limitations of the analytical methods with the designated biological questions they seek to answer will be discussed. Particular attention will be given to those information theoretical approaches that are anchored in a set of first principles of physics and can yield detailed (and often surprising) predictions. PMID:28128880

Review: visual analytics of climate networks

NASA Astrophysics Data System (ADS)

Nocke, T.; Buschmann, S.; Donges, J. F.; Marwan, N.; Schulz, H.-J.; Tominski, C.

2015-09-01

Network analysis has become an important approach in studying complex spatiotemporal behaviour within geophysical observation and simulation data. This new field produces increasing numbers of large geo-referenced networks to be analysed. Particular focus lies currently on the network analysis of the complex statistical interrelationship structure within climatological fields. The standard procedure for such network analyses is the extraction of network measures in combination with static standard visualisation methods. Existing interactive visualisation methods and tools for geo-referenced network exploration are often either not known to the analyst or their potential is not fully exploited. To fill this gap, we illustrate how interactive visual analytics methods in combination with geovisualisation can be tailored for visual climate network investigation. Therefore, the paper provides a problem analysis relating the multiple visualisation challenges to a survey undertaken with network analysts from the research fields of climate and complex systems science. Then, as an overview for the interested practitioner, we review the state-of-the-art in climate network visualisation and provide an overview of existing tools. As a further contribution, we introduce the visual network analytics tools CGV and GTX, providing tailored solutions for climate network analysis, including alternative geographic projections, edge bundling, and 3-D network support. Using these tools, the paper illustrates the application potentials of visual analytics for climate networks based on several use cases including examples from global, regional, and multi-layered climate networks.

Review: visual analytics of climate networks

NASA Astrophysics Data System (ADS)

Nocke, T.; Buschmann, S.; Donges, J. F.; Marwan, N.; Schulz, H.-J.; Tominski, C.

2015-04-01

Network analysis has become an important approach in studying complex spatiotemporal behaviour within geophysical observation and simulation data. This new field produces increasing amounts of large geo-referenced networks to be analysed. Particular focus lies currently on the network analysis of the complex statistical interrelationship structure within climatological fields. The standard procedure for such network analyses is the extraction of network measures in combination with static standard visualisation methods. Existing interactive visualisation methods and tools for geo-referenced network exploration are often either not known to the analyst or their potential is not fully exploited. To fill this gap, we illustrate how interactive visual analytics methods in combination with geovisualisation can be tailored for visual climate network investigation. Therefore, the paper provides a problem analysis, relating the multiple visualisation challenges with a survey undertaken with network analysts from the research fields of climate and complex systems science. Then, as an overview for the interested practitioner, we review the state-of-the-art in climate network visualisation and provide an overview of existing tools. As a further contribution, we introduce the visual network analytics tools CGV and GTX, providing tailored solutions for climate network analysis, including alternative geographic projections, edge bundling, and 3-D network support. Using these tools, the paper illustrates the application potentials of visual analytics for climate networks based on several use cases including examples from global, regional, and multi-layered climate networks.

The Development of Verbal and Visual Working Memory Processes: A Latent Variable Approach

ERIC Educational Resources Information Center

Koppenol-Gonzalez, Gabriela V.; Bouwmeester, Samantha; Vermunt, Jeroen K.

2012-01-01

Working memory (WM) processing in children has been studied with different approaches, focusing on either the organizational structure of WM processing during development (factor analytic) or the influence of different task conditions on WM processing (experimental). The current study combined both approaches, aiming to distinguish verbal and…

Visual analytics in healthcare education: exploring novel ways to analyze and represent big data in undergraduate medical education

PubMed Central

Nilsson, Gunnar; Zary, Nabil

2014-01-01

Introduction. The big data present in the medical curriculum that informs undergraduate medical education is beyond human abilities to perceive and analyze. The medical curriculum is the main tool used by teachers and directors to plan, design, and deliver teaching and assessment activities and student evaluations in medical education in a continuous effort to improve it. Big data remains largely unexploited for medical education improvement purposes. The emerging research field of visual analytics has the advantage of combining data analysis and manipulation techniques, information and knowledge representation, and human cognitive strength to perceive and recognize visual patterns. Nevertheless, there is a lack of research on the use and benefits of visual analytics in medical education. Methods. The present study is based on analyzing the data in the medical curriculum of an undergraduate medical program as it concerns teaching activities, assessment methods and learning outcomes in order to explore visual analytics as a tool for finding ways of representing big data from undergraduate medical education for improvement purposes. Cytoscape software was employed to build networks of the identified aspects and visualize them. Results. After the analysis of the curriculum data, eleven aspects were identified. Further analysis and visualization of the identified aspects with Cytoscape resulted in building an abstract model of the examined data that presented three different approaches; (i) learning outcomes and teaching methods, (ii) examination and learning outcomes, and (iii) teaching methods, learning outcomes, examination results, and gap analysis. Discussion. This study identified aspects of medical curriculum that play an important role in how medical education is conducted. The implementation of visual analytics revealed three novel ways of representing big data in the undergraduate medical education context. It appears to be a useful tool to explore such data with possible future implications on healthcare education. It also opens a new direction in medical education informatics research. PMID:25469323

Visual analytics in healthcare education: exploring novel ways to analyze and represent big data in undergraduate medical education.

PubMed

Vaitsis, Christos; Nilsson, Gunnar; Zary, Nabil

2014-01-01

Introduction. The big data present in the medical curriculum that informs undergraduate medical education is beyond human abilities to perceive and analyze. The medical curriculum is the main tool used by teachers and directors to plan, design, and deliver teaching and assessment activities and student evaluations in medical education in a continuous effort to improve it. Big data remains largely unexploited for medical education improvement purposes. The emerging research field of visual analytics has the advantage of combining data analysis and manipulation techniques, information and knowledge representation, and human cognitive strength to perceive and recognize visual patterns. Nevertheless, there is a lack of research on the use and benefits of visual analytics in medical education. Methods. The present study is based on analyzing the data in the medical curriculum of an undergraduate medical program as it concerns teaching activities, assessment methods and learning outcomes in order to explore visual analytics as a tool for finding ways of representing big data from undergraduate medical education for improvement purposes. Cytoscape software was employed to build networks of the identified aspects and visualize them. Results. After the analysis of the curriculum data, eleven aspects were identified. Further analysis and visualization of the identified aspects with Cytoscape resulted in building an abstract model of the examined data that presented three different approaches; (i) learning outcomes and teaching methods, (ii) examination and learning outcomes, and (iii) teaching methods, learning outcomes, examination results, and gap analysis. Discussion. This study identified aspects of medical curriculum that play an important role in how medical education is conducted. The implementation of visual analytics revealed three novel ways of representing big data in the undergraduate medical education context. It appears to be a useful tool to explore such data with possible future implications on healthcare education. It also opens a new direction in medical education informatics research.

A Digital Mixed Methods Research Design: Integrating Multimodal Analysis with Data Mining and Information Visualization for Big Data Analytics

ERIC Educational Resources Information Center

O'Halloran, Kay L.; Tan, Sabine; Pham, Duc-Son; Bateman, John; Vande Moere, Andrew

2018-01-01

This article demonstrates how a digital environment offers new opportunities for transforming qualitative data into quantitative data in order to use data mining and information visualization for mixed methods research. The digital approach to mixed methods research is illustrated by a framework which combines qualitative methods of multimodal…

Cultural Parallax and Content Analysis: Images of Black Women in High School History Textbooks

ERIC Educational Resources Information Center

Woyshner, Christine; Schocker, Jessica B.

2015-01-01

This study investigates the representation of Black women in high school history textbooks. To examine the extent to which Black women are represented visually and to explore how they are portrayed, the authors use a mixed-methods approach that draws on analytical techniques in content analysis and from visual culture studies. Their findings…

Analytic Steering: Inserting Context into the Information Dialog

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bohn, Shawn J.; Calapristi, Augustin J.; Brown, Shyretha D.

2011-10-23

An analyst’s intrinsic domain knowledge is a primary asset in almost any analysis task. Unstructured text analysis systems that apply un-supervised content analysis approaches can be more effective if they can leverage this domain knowledge in a manner that augments the information discovery process without obfuscating new or unexpected content. Current unsupervised approaches rely upon the prowess of the analyst to submit the right queries or observe generalized document and term relationships from ranked or visual results. We propose a new approach which allows the user to control or steer the analytic view within the unsupervised space. This process ismore » controlled through the data characterization process via user supplied context in the form of a collection of key terms. We show that steering with an appropriate choice of key terms can provide better relevance to the analytic domain and still enable the analyst to uncover un-expected relationships; this paper discusses cases where various analytic steering approaches can provide enhanced analysis results and cases where analytic steering can have a negative impact on the analysis process.« less

The Human is the Loop: New Directions for Visual Analytics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Endert, Alexander; Hossain, Shahriar H.; Ramakrishnan, Naren

2014-01-28

Visual analytics is the science of marrying interactive visualizations and analytic algorithms to support exploratory knowledge discovery in large datasets. We argue for a shift from a ‘human in the loop’ philosophy for visual analytics to a ‘human is the loop’ viewpoint, where the focus is on recognizing analysts’ work processes, and seamlessly fitting analytics into that existing interactive process. We survey a range of projects that provide visual analytic support contextually in the sensemaking loop, and outline a research agenda along with future challenges.

AUVA - Augmented Reality Empowers Visual Analytics to explore Medical Curriculum Data.

PubMed

Nifakos, Sokratis; Vaitsis, Christos; Zary, Nabil

2015-01-01

Medical curriculum data play a key role in the structure and the organization of medical programs in Universities around the world. The effective processing and usage of these data may improve the educational environment of medical students. As a consequence, the new generation of health professionals would have improved skills from the previous ones. This study introduces the process of enhancing curriculum data by the use of augmented reality technology as a management and presentation tool. The final goal is to enrich the information presented from a visual analytics approach applied on medical curriculum data and to sustain low levels of complexity of understanding these data.

A Strategy for Incorporating Learning Analytics into the Design and Evaluation of a K-12 Science Curriculum

ERIC Educational Resources Information Center

Monroy, Carlos; Rangel, Virginia Snodgrass; Whitaker, Reid

2014-01-01

In this paper, we discuss a scalable approach for integrating learning analytics into an online K-12 science curriculum. A description of the curriculum and the underlying pedagogical framework is followed by a discussion of the challenges to be tackled as part of this integration. We include examples of data visualization based on teacher usage…

Single cell proteomics in biomedicine: High-dimensional data acquisition, visualization, and analysis.

PubMed

Su, Yapeng; Shi, Qihui; Wei, Wei

2017-02-01

New insights on cellular heterogeneity in the last decade provoke the development of a variety of single cell omics tools at a lightning pace. The resultant high-dimensional single cell data generated by these tools require new theoretical approaches and analytical algorithms for effective visualization and interpretation. In this review, we briefly survey the state-of-the-art single cell proteomic tools with a particular focus on data acquisition and quantification, followed by an elaboration of a number of statistical and computational approaches developed to date for dissecting the high-dimensional single cell data. The underlying assumptions, unique features, and limitations of the analytical methods with the designated biological questions they seek to answer will be discussed. Particular attention will be given to those information theoretical approaches that are anchored in a set of first principles of physics and can yield detailed (and often surprising) predictions. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Falcon: A Temporal Visual Analysis System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steed, Chad A.

2016-09-05

Flexible visible exploration of long, high-resolution time series from multiple sensor streams is a challenge in several domains. Falcon is a visual analytics approach that helps researchers acquire a deep understanding of patterns in log and imagery data. Falcon allows users to interactively explore large, time-oriented data sets from multiple linked perspectives. Falcon provides overviews, detailed views, and unique segmented time series visualizations with multiple levels of detail. These capabilities are applicable to the analysis of any quantitative time series.

A Grammar-based Approach for Modeling User Interactions and Generating Suggestions During the Data Exploration Process.

PubMed

Dabek, Filip; Caban, Jesus J

2017-01-01

Despite the recent popularity of visual analytics focusing on big data, little is known about how to support users that use visualization techniques to explore multi-dimensional datasets and accomplish specific tasks. Our lack of models that can assist end-users during the data exploration process has made it challenging to learn from the user's interactive and analytical process. The ability to model how a user interacts with a specific visualization technique and what difficulties they face are paramount in supporting individuals with discovering new patterns within their complex datasets. This paper introduces the notion of visualization systems understanding and modeling user interactions with the intent of guiding a user through a task thereby enhancing visual data exploration. The challenges faced and the necessary future steps to take are discussed; and to provide a working example, a grammar-based model is presented that can learn from user interactions, determine the common patterns among a number of subjects using a K-Reversible algorithm, build a set of rules, and apply those rules in the form of suggestions to new users with the goal of guiding them along their visual analytic process. A formal evaluation study with 300 subjects was performed showing that our grammar-based model is effective at capturing the interactive process followed by users and that further research in this area has the potential to positively impact how users interact with a visualization system.

Equilibrium relations and bipolar cognitive mapping for online analytical processing with applications in international relations and strategic decision support.

PubMed

Zhang, Wen-Ran

2003-01-01

Bipolar logic, bipolar sets, and equilibrium relations are proposed for bipolar cognitive mapping and visualization in online analytical processing (OLAP) and online analytical mining (OLAM). As cognitive models, cognitive maps (CMs) hold great potential for clustering and visualization. Due to the lack of a formal mathematical basis, however, CM-based OLAP and OLAM have not gained popularity. Compared with existing approaches, bipolar cognitive mapping has a number of advantages. First, bipolar CMs are formal logical models as well as cognitive models. Second, equilibrium relations (with polarized reflexivity, symmetry, and transitivity), as bipolar generalizations and fusions of equivalence relations, provide a theoretical basis for bipolar visualization and coordination. Third, an equilibrium relation or CM induces bipolar partitions that distinguish disjoint coalition subsets not involved in any conflict, disjoint coalition subsets involved in a conflict, disjoint conflict subsets, and disjoint harmony subsets. Finally, equilibrium energy analysis leads to harmony and stability measures for strategic decision and multiagent coordination. Thus, this work bridges a gap for CM-based clustering and visualization in OLAP and OLAM. Basic ideas are illustrated with example CMs in international relations.

Developing Guidelines for Assessing Visual Analytics Environments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scholtz, Jean

2011-07-01

In this paper, we develop guidelines for evaluating visual analytic environments based on a synthesis of reviews for the entries to the 2009 Visual Analytics Science and Technology (VAST) Symposium Challenge and from a user study with professional intelligence analysts. By analyzing the 2009 VAST Challenge reviews we gained a better understanding of what is important to our reviewers, both visualization researchers and professional analysts. We also report on a small user study with professional analysts to determine the important factors that they use in evaluating visual analysis systems. We then looked at guidelines developed by researchers in various domainsmore » and synthesized these into an initial set for use by others in the community. In a second part of the user study, we looked at guidelines for a new aspect of visual analytic systems – the generation of reports. Future visual analytic systems have been challenged to help analysts generate their reports. In our study we worked with analysts to understand the criteria they used to evaluate the quality of analytic reports. We propose that this knowledge will be useful as researchers look at systems to automate some of the report generation.1 Based on these efforts, we produced some initial guidelines for evaluating visual analytic environment and for evaluation of analytic reports. It is important to understand that these guidelines are initial drafts and are limited in scope because of the type of tasks for which the visual analytic systems used in the studies in this paper were designed. More research and refinement is needed by the Visual Analytics Community to provide additional evaluation guidelines for different types of visual analytic environments.« less

Variability and Correlations in Primary Visual Cortical Neurons Driven by Fixational Eye Movements

PubMed Central

McFarland, James M.; Cumming, Bruce G.

2016-01-01

The ability to distinguish between elements of a sensory neuron's activity that are stimulus independent versus driven by the stimulus is critical for addressing many questions in systems neuroscience. This is typically accomplished by measuring neural responses to repeated presentations of identical stimuli and identifying the trial-variable components of the response as noise. In awake primates, however, small “fixational” eye movements (FEMs) introduce uncontrolled trial-to-trial differences in the visual stimulus itself, potentially confounding this distinction. Here, we describe novel analytical methods that directly quantify the stimulus-driven and stimulus-independent components of visual neuron responses in the presence of FEMs. We apply this approach, combined with precise model-based eye tracking, to recordings from primary visual cortex (V1), finding that standard approaches that ignore FEMs typically miss more than half of the stimulus-driven neural response variance, creating substantial biases in measures of response reliability. We show that these effects are likely not isolated to the particular experimental conditions used here, such as the choice of visual stimulus or spike measurement time window, and thus will be a more general problem for V1 recordings in awake primates. We also demonstrate that measurements of the stimulus-driven and stimulus-independent correlations among pairs of V1 neurons can be greatly biased by FEMs. These results thus illustrate the potentially dramatic impact of FEMs on measures of signal and noise in visual neuron activity and also demonstrate a novel approach for controlling for these eye-movement-induced effects. SIGNIFICANCE STATEMENT Distinguishing between the signal and noise in a sensory neuron's activity is typically accomplished by measuring neural responses to repeated presentations of an identical stimulus. For recordings from the visual cortex of awake animals, small “fixational” eye movements (FEMs) inevitably introduce trial-to-trial variability in the visual stimulus, potentially confounding such measures. Here, we show that FEMs often have a dramatic impact on several important measures of response variability for neurons in primary visual cortex. We also present an analytical approach for quantifying signal and noise in visual neuron activity in the presence of FEMs. These results thus highlight the importance of controlling for FEMs in studies of visual neuron function, and demonstrate novel methods for doing so. PMID:27277801

Web GIS in practice IX: a demonstration of geospatial visual analytics using Microsoft Live Labs Pivot technology and WHO mortality data

PubMed Central

2011-01-01

The goal of visual analytics is to facilitate the discourse between the user and the data by providing dynamic displays and versatile visual interaction opportunities with the data that can support analytical reasoning and the exploration of data from multiple user-customisable aspects. This paper introduces geospatial visual analytics, a specialised subtype of visual analytics, and provides pointers to a number of learning resources about the subject, as well as some examples of human health, surveillance, emergency management and epidemiology-related geospatial visual analytics applications and examples of free software tools that readers can experiment with, such as Google Public Data Explorer. The authors also present a practical demonstration of geospatial visual analytics using partial data for 35 countries from a publicly available World Health Organization (WHO) mortality dataset and Microsoft Live Labs Pivot technology, a free, general purpose visual analytics tool that offers a fresh way to visually browse and arrange massive amounts of data and images online and also supports geographic and temporal classifications of datasets featuring geospatial and temporal components. Interested readers can download a Zip archive (included with the manuscript as an additional file) containing all files, modules and library functions used to deploy the WHO mortality data Pivot collection described in this paper. PMID:21410968

Web GIS in practice IX: a demonstration of geospatial visual analytics using Microsoft Live Labs Pivot technology and WHO mortality data.

PubMed

Kamel Boulos, Maged N; Viangteeravat, Teeradache; Anyanwu, Matthew N; Ra Nagisetty, Venkateswara; Kuscu, Emin

2011-03-16

The goal of visual analytics is to facilitate the discourse between the user and the data by providing dynamic displays and versatile visual interaction opportunities with the data that can support analytical reasoning and the exploration of data from multiple user-customisable aspects. This paper introduces geospatial visual analytics, a specialised subtype of visual analytics, and provides pointers to a number of learning resources about the subject, as well as some examples of human health, surveillance, emergency management and epidemiology-related geospatial visual analytics applications and examples of free software tools that readers can experiment with, such as Google Public Data Explorer. The authors also present a practical demonstration of geospatial visual analytics using partial data for 35 countries from a publicly available World Health Organization (WHO) mortality dataset and Microsoft Live Labs Pivot technology, a free, general purpose visual analytics tool that offers a fresh way to visually browse and arrange massive amounts of data and images online and also supports geographic and temporal classifications of datasets featuring geospatial and temporal components. Interested readers can download a Zip archive (included with the manuscript as an additional file) containing all files, modules and library functions used to deploy the WHO mortality data Pivot collection described in this paper.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scholtz, Jean

A new field of research, visual analytics, has recently been introduced. This has been defined as “the science of analytical reasoning facilitated by visual interfaces." Visual analytic environments, therefore, support analytical reasoning using visual representations and interactions, with data representations and transformation capabilities, to support production, presentation and dissemination. As researchers begin to develop visual analytic environments, it will be advantageous to develop metrics and methodologies to help researchers measure the progress of their work and understand the impact their work will have on the users who will work in such environments. This paper presents five areas or aspects ofmore » visual analytic environments that should be considered as metrics and methodologies for evaluation are developed. Evaluation aspects need to include usability, but it is necessary to go beyond basic usability. The areas of situation awareness, collaboration, interaction, creativity, and utility are proposed as areas for initial consideration. The steps that need to be undertaken to develop systematic evaluation methodologies and metrics for visual analytic environments are outlined.« less

The Effect of Multispectral Image Fusion Enhancement on Human Efficiency

DTIC Science & Technology

2017-03-20

human visual system by applying a technique commonly used in visual percep- tion research : ideal observer analysis. Using this approach, we establish...applications, analytic tech- niques, and procedural methods used across studies. This paper uses ideal observer analysis to establish a frame- work that allows...augmented similarly to incorpo- rate research involving more complex stimulus content. Additionally, the ideal observer can be adapted for a number of

Technosocial Predictive Analytics in Support of Naturalistic Decision Making

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sanfilippo, Antonio P.; Cowell, Andrew J.; Malone, Elizabeth L.

2009-06-23

A main challenge we face in fostering sustainable growth is to anticipate outcomes through predictive and proactive across domains as diverse as energy, security, the environment, health and finance in order to maximize opportunities, influence outcomes and counter adversities. The goal of this paper is to present new methods for anticipatory analytical thinking which address this challenge through the development of a multi-perspective approach to predictive modeling as a core to a creative decision making process. This approach is uniquely multidisciplinary in that it strives to create decision advantage through the integration of human and physical models, and leverages knowledgemore » management and visual analytics to support creative thinking by facilitating the achievement of interoperable knowledge inputs and enhancing the user’s cognitive access. We describe a prototype system which implements this approach and exemplify its functionality with reference to a use case in which predictive modeling is paired with analytic gaming to support collaborative decision-making in the domain of agricultural land management.« less

An Investigation of Visual, Aural, Motion and Control Movement Cues.

ERIC Educational Resources Information Center

Matheny, W. G.; And Others

A study was conducted to determine the ways in which multi-sensory cues can be simulated and effectively used in the training of pilots. Two analytical bases, one called the stimulus environment approach and the other an information array approach, are developed along with a cue taxonomy. Cues are postulated on the basis of information gained from…

Data Analytics and Visualization for Large Army Testing Data

DTIC Science & Technology

2013-09-01

and relationships in the data that would otherwise remain hidden. 7 Bibliography 1. Goodall , J. R.; Tesone, D. R. Visual Analytics for Network...Software Visualization, 2003, pp 143–149. 3. Goodall , J. R.; Sowul, M. VIAssist: Visual Analytics for Cyber Defense, IEEE Conference on Technologies

Familiarity Vs Trust: A Comparative Study of Domain Scientists' Trust in Visual Analytics and Conventional Analysis Methods.

PubMed

Dasgupta, Aritra; Lee, Joon-Yong; Wilson, Ryan; Lafrance, Robert A; Cramer, Nick; Cook, Kristin; Payne, Samuel

2017-01-01

Combining interactive visualization with automated analytical methods like statistics and data mining facilitates data-driven discovery. These visual analytic methods are beginning to be instantiated within mixed-initiative systems, where humans and machines collaboratively influence evidence-gathering and decision-making. But an open research question is that, when domain experts analyze their data, can they completely trust the outputs and operations on the machine-side? Visualization potentially leads to a transparent analysis process, but do domain experts always trust what they see? To address these questions, we present results from the design and evaluation of a mixed-initiative, visual analytics system for biologists, focusing on analyzing the relationships between familiarity of an analysis medium and domain experts' trust. We propose a trust-augmented design of the visual analytics system, that explicitly takes into account domain-specific tasks, conventions, and preferences. For evaluating the system, we present the results of a controlled user study with 34 biologists where we compare the variation of the level of trust across conventional and visual analytic mediums and explore the influence of familiarity and task complexity on trust. We find that despite being unfamiliar with a visual analytic medium, scientists seem to have an average level of trust that is comparable with the same in conventional analysis medium. In fact, for complex sense-making tasks, we find that the visual analytic system is able to inspire greater trust than other mediums. We summarize the implications of our findings with directions for future research on trustworthiness of visual analytic systems.

Visual analytics for semantic queries of TerraSAR-X image content

NASA Astrophysics Data System (ADS)

Espinoza-Molina, Daniela; Alonso, Kevin; Datcu, Mihai

2015-10-01

With the continuous image product acquisition of satellite missions, the size of the image archives is considerably increasing every day as well as the variety and complexity of their content, surpassing the end-user capacity to analyse and exploit them. Advances in the image retrieval field have contributed to the development of tools for interactive exploration and extraction of the images from huge archives using different parameters like metadata, key-words, and basic image descriptors. Even though we count on more powerful tools for automated image retrieval and data analysis, we still face the problem of understanding and analyzing the results. Thus, a systematic computational analysis of these results is required in order to provide to the end-user a summary of the archive content in comprehensible terms. In this context, visual analytics combines automated analysis with interactive visualizations analysis techniques for an effective understanding, reasoning and decision making on the basis of very large and complex datasets. Moreover, currently several researches are focused on associating the content of the images with semantic definitions for describing the data in a format to be easily understood by the end-user. In this paper, we present our approach for computing visual analytics and semantically querying the TerraSAR-X archive. Our approach is mainly composed of four steps: 1) the generation of a data model that explains the information contained in a TerraSAR-X product. The model is formed by primitive descriptors and metadata entries, 2) the storage of this model in a database system, 3) the semantic definition of the image content based on machine learning algorithms and relevance feedback, and 4) querying the image archive using semantic descriptors as query parameters and computing the statistical analysis of the query results. The experimental results shows that with the help of visual analytics and semantic definitions we are able to explain the image content using semantic terms and the relations between them answering questions such as what is the percentage of urban area in a region? or what is the distribution of water bodies in a city?

An Affordance-Based Framework for Human Computation and Human-Computer Collaboration.

PubMed

Crouser, R J; Chang, R

2012-12-01

Visual Analytics is "the science of analytical reasoning facilitated by visual interactive interfaces". The goal of this field is to develop tools and methodologies for approaching problems whose size and complexity render them intractable without the close coupling of both human and machine analysis. Researchers have explored this coupling in many venues: VAST, Vis, InfoVis, CHI, KDD, IUI, and more. While there have been myriad promising examples of human-computer collaboration, there exists no common language for comparing systems or describing the benefits afforded by designing for such collaboration. We argue that this area would benefit significantly from consensus about the design attributes that define and distinguish existing techniques. In this work, we have reviewed 1,271 papers from many of the top-ranking conferences in visual analytics, human-computer interaction, and visualization. From these, we have identified 49 papers that are representative of the study of human-computer collaborative problem-solving, and provide a thorough overview of the current state-of-the-art. Our analysis has uncovered key patterns of design hinging on human and machine-intelligence affordances, and also indicates unexplored avenues in the study of this area. The results of this analysis provide a common framework for understanding these seemingly disparate branches of inquiry, which we hope will motivate future work in the field.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steed, Chad A

Interactive data visualization leverages human visual perception and cognition to improve the accuracy and effectiveness of data analysis. When combined with automated data analytics, data visualization systems orchestrate the strengths of humans with the computational power of machines to solve problems neither approach can manage in isolation. In the intelligent transportation system domain, such systems are necessary to support decision making in large and complex data streams. In this chapter, we provide an introduction to several key topics related to the design of data visualization systems. In addition to an overview of key techniques and strategies, we will describe practicalmore » design principles. The chapter is concluded with a detailed case study involving the design of a multivariate visualization tool.« less

Multimedia Analysis plus Visual Analytics = Multimedia Analytics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chinchor, Nancy; Thomas, James J.; Wong, Pak C.

2010-10-01

Multimedia analysis has focused on images, video, and to some extent audio and has made progress in single channels excluding text. Visual analytics has focused on the user interaction with data during the analytic process plus the fundamental mathematics and has continued to treat text as did its precursor, information visualization. The general problem we address in this tutorial is the combining of multimedia analysis and visual analytics to deal with multimedia information gathered from different sources, with different goals or objectives, and containing all media types and combinations in common usage.

Visual Analytics 101

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scholtz, Jean; Burtner, Edwin R.; Cook, Kristin A.

This course will introduce the field of Visual Analytics to HCI researchers and practitioners highlighting the contributions they can make to this field. Topics will include a definition of visual analytics along with examples of current systems, types of tasks and end users, issues in defining user requirements, design of visualizations and interactions, guidelines and heuristics, the current state of user-centered evaluations, and metrics for evaluation. We encourage designers, HCI researchers, and HCI practitioners to attend to learn how their skills can contribute to advancing the state of the art of visual analytics

Using Interactive Data Visualizations for Exploratory Analysis in Undergraduate Genomics Coursework: Field Study Findings and Guidelines

NASA Astrophysics Data System (ADS)

Mirel, Barbara; Kumar, Anuj; Nong, Paige; Su, Gang; Meng, Fan

2016-02-01

Life scientists increasingly use visual analytics to explore large data sets and generate hypotheses. Undergraduate biology majors should be learning these same methods. Yet visual analytics is one of the most underdeveloped areas of undergraduate biology education. This study sought to determine the feasibility of undergraduate biology majors conducting exploratory analysis using the same interactive data visualizations as practicing scientists. We examined 22 upper level undergraduates in a genomics course as they engaged in a case-based inquiry with an interactive heat map. We qualitatively and quantitatively analyzed students' visual analytic behaviors, reasoning and outcomes to identify student performance patterns, commonly shared efficiencies and task completion. We analyzed students' successes and difficulties in applying knowledge and skills relevant to the visual analytics case and related gaps in knowledge and skill to associated tool designs. Findings show that undergraduate engagement in visual analytics is feasible and could be further strengthened through tool usability improvements. We identify these improvements. We speculate, as well, on instructional considerations that our findings suggested may also enhance visual analytics in case-based modules.

Using Interactive Data Visualizations for Exploratory Analysis in Undergraduate Genomics Coursework: Field Study Findings and Guidelines

PubMed Central

Kumar, Anuj; Nong, Paige; Su, Gang; Meng, Fan

2016-01-01

Life scientists increasingly use visual analytics to explore large data sets and generate hypotheses. Undergraduate biology majors should be learning these same methods. Yet visual analytics is one of the most underdeveloped areas of undergraduate biology education. This study sought to determine the feasibility of undergraduate biology majors conducting exploratory analysis using the same interactive data visualizations as practicing scientists. We examined 22 upper level undergraduates in a genomics course as they engaged in a case-based inquiry with an interactive heat map. We qualitatively and quantitatively analyzed students’ visual analytic behaviors, reasoning and outcomes to identify student performance patterns, commonly shared efficiencies and task completion. We analyzed students’ successes and difficulties in applying knowledge and skills relevant to the visual analytics case and related gaps in knowledge and skill to associated tool designs. Findings show that undergraduate engagement in visual analytics is feasible and could be further strengthened through tool usability improvements. We identify these improvements. We speculate, as well, on instructional considerations that our findings suggested may also enhance visual analytics in case-based modules. PMID:26877625

Changes in Visual/Spatial and Analytic Strategy Use in Organic Chemistry with the Development of Expertise

ERIC Educational Resources Information Center

Vlacholia, Maria; Vosniadou, Stella; Roussos, Petros; Salta, Katerina; Kazi, Smaragda; Sigalas, Michael; Tzougraki, Chryssa

2017-01-01

We present two studies that investigated the adoption of visual/spatial and analytic strategies by individuals at different levels of expertise in the area of organic chemistry, using the Visual Analytic Chemistry Task (VACT). The VACT allows the direct detection of analytic strategy use without drawing inferences about underlying mental…

Visualizing statistical significance of disease clusters using cartograms.

PubMed

Kronenfeld, Barry J; Wong, David W S

2017-05-15

Health officials and epidemiological researchers often use maps of disease rates to identify potential disease clusters. Because these maps exaggerate the prominence of low-density districts and hide potential clusters in urban (high-density) areas, many researchers have used density-equalizing maps (cartograms) as a basis for epidemiological mapping. However, we do not have existing guidelines for visual assessment of statistical uncertainty. To address this shortcoming, we develop techniques for visual determination of statistical significance of clusters spanning one or more districts on a cartogram. We developed the techniques within a geovisual analytics framework that does not rely on automated significance testing, and can therefore facilitate visual analysis to detect clusters that automated techniques might miss. On a cartogram of the at-risk population, the statistical significance of a disease cluster is determinate from the rate, area and shape of the cluster under standard hypothesis testing scenarios. We develop formulae to determine, for a given rate, the area required for statistical significance of a priori and a posteriori designated regions under certain test assumptions. Uniquely, our approach enables dynamic inference of aggregate regions formed by combining individual districts. The method is implemented in interactive tools that provide choropleth mapping, automated legend construction and dynamic search tools to facilitate cluster detection and assessment of the validity of tested assumptions. A case study of leukemia incidence analysis in California demonstrates the ability to visually distinguish between statistically significant and insignificant regions. The proposed geovisual analytics approach enables intuitive visual assessment of statistical significance of arbitrarily defined regions on a cartogram. Our research prompts a broader discussion of the role of geovisual exploratory analyses in disease mapping and the appropriate framework for visually assessing the statistical significance of spatial clusters.

User-Centered Evaluation of Visual Analytics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scholtz, Jean C.

Visual analytics systems are becoming very popular. More domains now use interactive visualizations to analyze the ever-increasing amount and heterogeneity of data. More novel visualizations are being developed for more tasks and users. We need to ensure that these systems can be evaluated to determine that they are both useful and usable. A user-centered evaluation for visual analytics needs to be developed for these systems. While many of the typical human-computer interaction (HCI) evaluation methodologies can be applied as is, others will need modification. Additionally, new functionality in visual analytics systems needs new evaluation methodologies. There is a difference betweenmore » usability evaluations and user-centered evaluations. Usability looks at the efficiency, effectiveness, and user satisfaction of users carrying out tasks with software applications. User-centered evaluation looks more specifically at the utility provided to the users by the software. This is reflected in the evaluations done and in the metrics used. In the visual analytics domain this is very challenging as users are most likely experts in a particular domain, the tasks they do are often not well defined, the software they use needs to support large amounts of different kinds of data, and often the tasks last for months. These difficulties are discussed more in the section on User-centered Evaluation. Our goal is to provide a discussion of user-centered evaluation practices for visual analytics, including existing practices that can be carried out and new methodologies and metrics that need to be developed and agreed upon by the visual analytics community. The material provided here should be of use for both researchers and practitioners in the field of visual analytics. Researchers and practitioners in HCI and interested in visual analytics will find this information useful as well as a discussion on changes that need to be made to current HCI practices to make them more suitable to visual analytics. A history of analysis and analysis techniques and problems is provided as well as an introduction to user-centered evaluation and various evaluation techniques for readers from different disciplines. The understanding of these techniques is imperative if we wish to support analysis in the visual analytics software we develop. Currently the evaluations that are conducted and published for visual analytics software are very informal and consist mainly of comments from users or potential users. Our goal is to help researchers in visual analytics to conduct more formal user-centered evaluations. While these are time-consuming and expensive to carryout, the outcomes of these studies will have a defining impact on the field of visual analytics and help point the direction for future features and visualizations to incorporate. While many researchers view work in user-centered evaluation as a less-than-exciting area to work, the opposite is true. First of all, the goal is user-centered evaluation is to help visual analytics software developers, researchers, and designers improve their solutions and discover creative ways to better accommodate their users. Working with the users is extremely rewarding as well. While we use the term “users” in almost all situations there are a wide variety of users that all need to be accommodated. Moreover, the domains that use visual analytics are varied and expanding. Just understanding the complexities of a number of these domains is exciting. Researchers are trying out different visualizations and interactions as well. And of course, the size and variety of data are expanding rapidly. User-centered evaluation in this context is rapidly changing. There are no standard processes and metrics and thus those of us working on user-centered evaluation must be creative in our work with both the users and with the researchers and developers.« less

Mining patterns in persistent surveillance systems with smart query and visual analytics

NASA Astrophysics Data System (ADS)

Habibi, Mohammad S.; Shirkhodaie, Amir

2013-05-01

In Persistent Surveillance Systems (PSS) the ability to detect and characterize events geospatially help take pre-emptive steps to counter adversary's actions. Interactive Visual Analytic (VA) model offers this platform for pattern investigation and reasoning to comprehend and/or predict such occurrences. The need for identifying and offsetting these threats requires collecting information from diverse sources, which brings with it increasingly abstract data. These abstract semantic data have a degree of inherent uncertainty and imprecision, and require a method for their filtration before being processed further. In this paper, we have introduced an approach based on Vector Space Modeling (VSM) technique for classification of spatiotemporal sequential patterns of group activities. The feature vectors consist of an array of attributes extracted from generated sensors semantic annotated messages. To facilitate proper similarity matching and detection of time-varying spatiotemporal patterns, a Temporal-Dynamic Time Warping (DTW) method with Gaussian Mixture Model (GMM) for Expectation Maximization (EM) is introduced. DTW is intended for detection of event patterns from neighborhood-proximity semantic frames derived from established ontology. GMM with EM, on the other hand, is employed as a Bayesian probabilistic model to estimated probability of events associated with a detected spatiotemporal pattern. In this paper, we present a new visual analytic tool for testing and evaluation group activities detected under this control scheme. Experimental results demonstrate the effectiveness of proposed approach for discovery and matching of subsequences within sequentially generated patterns space of our experiments.

Chemical imaging of drug delivery systems with structured surfaces-a combined analytical approach of confocal raman microscopy and optical profilometry.

PubMed

Kann, Birthe; Windbergs, Maike

2013-04-01

Confocal Raman microscopy is an analytical technique with a steadily increasing impact in the field of pharmaceutics as the instrumental setup allows for nondestructive visualization of component distribution within drug delivery systems. Here, the attention is mainly focused on classic solid carrier systems like tablets, pellets, or extrudates. Due to the opacity of these systems, Raman analysis is restricted either to exterior surfaces or cross sections. As Raman spectra are only recorded from one focal plane at a time, the sample is usually altered to create a smooth and even surface. However, this manipulation can lead to misinterpretation of the analytical results. Here, we present a trendsetting approach to overcome these analytical pitfalls with a combination of confocal Raman microscopy and optical profilometry. By acquiring a topography profile of the sample area of interest prior to Raman spectroscopy, the profile height information allowed to level the focal plane to the sample surface for each spectrum acquisition. We first demonstrated the basic principle of this complementary approach in a case study using a tilted silica wafer. In a second step, we successfully adapted the two techniques to investigate an extrudate and a lyophilisate as two exemplary solid drug carrier systems. Component distribution analysis with the novel analytical approach was neither hampered by the curvature of the cylindrical extrudate nor the highly structured surface of the lyophilisate. Therefore, the combined analytical approach bears a great potential to be implemented in diversified fields of pharmaceutical sciences.

ENVIRONMENTAL SYSTEMS MANAGEMENT AS APPLIED TO WATERSHEDS, UTILIZING REMOTE SENSING, DECISION SUPPORT AND VISUALIZATION

EPA Science Inventory

Environmental Systems Management as a conceptual framework and as a set of interdisciplinary analytical approaches will be described within the context of sustainable watershed management, within devergent complex ecosystems. A specific subset of integrated tools are deployed to...

Visual Analytics for Heterogeneous Geoscience Data

NASA Astrophysics Data System (ADS)

Pan, Y.; Yu, L.; Zhu, F.; Rilee, M. L.; Kuo, K. S.; Jiang, H.; Yu, H.

2017-12-01

Geoscience data obtained from diverse sources have been routinely leveraged by scientists to study various phenomena. The principal data sources include observations and model simulation outputs. These data are characterized by spatiotemporal heterogeneity originated from different instrument design specifications and/or computational model requirements used in data generation processes. Such inherent heterogeneity poses several challenges in exploring and analyzing geoscience data. First, scientists often wish to identify features or patterns co-located among multiple data sources to derive and validate certain hypotheses. Heterogeneous data make it a tedious task to search such features in dissimilar datasets. Second, features of geoscience data are typically multivariate. It is challenging to tackle the high dimensionality of geoscience data and explore the relations among multiple variables in a scalable fashion. Third, there is a lack of transparency in traditional automated approaches, such as feature detection or clustering, in that scientists cannot intuitively interact with their analysis processes and interpret results. To address these issues, we present a new scalable approach that can assist scientists in analyzing voluminous and diverse geoscience data. We expose a high-level query interface that allows users to easily express their customized queries to search features of interest across multiple heterogeneous datasets. For identified features, we develop a visualization interface that enables interactive exploration and analytics in a linked-view manner. Specific visualization techniques such as scatter plots to parallel coordinates are employed in each view to allow users to explore various aspects of features. Different views are linked and refreshed according to user interactions in any individual view. In such a manner, a user can interactively and iteratively gain understanding into the data through a variety of visual analytics operations. We demonstrate with use cases how scientists can combine the query and visualization interfaces to enable a customized workflow facilitating studies using heterogeneous geoscience datasets.

Beyond Control Panels: Direct Manipulation for Visual Analytics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Endert, Alexander; Bradel, Lauren; North, Chris

2013-07-19

Information Visualization strives to provide visual representations through which users can think about and gain insight into information. By leveraging the visual and cognitive systems of humans, complex relationships and phenomena occurring within datasets can be uncovered by exploring information visually. Interaction metaphors for such visualizations are designed to enable users direct control over the filters, queries, and other parameters controlling how the data is visually represented. Through the evolution of information visualization, more complex mathematical and data analytic models are being used to visualize relationships and patterns in data – creating the field of Visual Analytics. However, the expectationsmore » for how users interact with these visualizations has remained largely unchanged – focused primarily on the direct manipulation of parameters of the underlying mathematical models. In this article we present an opportunity to evolve the methodology for user interaction from the direct manipulation of parameters through visual control panels, to interactions designed specifically for visual analytic systems. Instead of focusing on traditional direct manipulation of mathematical parameters, the evolution of the field can be realized through direct manipulation within the visual representation – where users can not only gain insight, but also interact. This article describes future directions and research challenges that fundamentally change the meaning of direct manipulation with regards to visual analytics, advancing the Science of Interaction.« less

Falcon: Visual analysis of large, irregularly sampled, and multivariate time series data in additive manufacturing

DOE PAGES

Steed, Chad A.; Halsey, William; Dehoff, Ryan; ...

2017-02-16

Flexible visual analysis of long, high-resolution, and irregularly sampled time series data from multiple sensor streams is a challenge in several domains. In the field of additive manufacturing, this capability is critical for realizing the full potential of large-scale 3D printers. Here, we propose a visual analytics approach that helps additive manufacturing researchers acquire a deep understanding of patterns in log and imagery data collected by 3D printers. Our specific goals include discovering patterns related to defects and system performance issues, optimizing build configurations to avoid defects, and increasing production efficiency. We introduce Falcon, a new visual analytics system thatmore » allows users to interactively explore large, time-oriented data sets from multiple linked perspectives. Falcon provides overviews, detailed views, and unique segmented time series visualizations, all with adjustable scale options. To illustrate the effectiveness of Falcon at providing thorough and efficient knowledge discovery, we present a practical case study involving experts in additive manufacturing and data from a large-scale 3D printer. The techniques described are applicable to the analysis of any quantitative time series, though the focus of this paper is on additive manufacturing.« less

Falcon: Visual analysis of large, irregularly sampled, and multivariate time series data in additive manufacturing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steed, Chad A.; Halsey, William; Dehoff, Ryan

Flexible visual analysis of long, high-resolution, and irregularly sampled time series data from multiple sensor streams is a challenge in several domains. In the field of additive manufacturing, this capability is critical for realizing the full potential of large-scale 3D printers. Here, we propose a visual analytics approach that helps additive manufacturing researchers acquire a deep understanding of patterns in log and imagery data collected by 3D printers. Our specific goals include discovering patterns related to defects and system performance issues, optimizing build configurations to avoid defects, and increasing production efficiency. We introduce Falcon, a new visual analytics system thatmore » allows users to interactively explore large, time-oriented data sets from multiple linked perspectives. Falcon provides overviews, detailed views, and unique segmented time series visualizations, all with adjustable scale options. To illustrate the effectiveness of Falcon at providing thorough and efficient knowledge discovery, we present a practical case study involving experts in additive manufacturing and data from a large-scale 3D printer. The techniques described are applicable to the analysis of any quantitative time series, though the focus of this paper is on additive manufacturing.« less

MemAxes: Visualization and Analytics for Characterizing Complex Memory Performance Behaviors.

PubMed

Gimenez, Alfredo; Gamblin, Todd; Jusufi, Ilir; Bhatele, Abhinav; Schulz, Martin; Bremer, Peer-Timo; Hamann, Bernd

2018-07-01

Memory performance is often a major bottleneck for high-performance computing (HPC) applications. Deepening memory hierarchies, complex memory management, and non-uniform access times have made memory performance behavior difficult to characterize, and users require novel, sophisticated tools to analyze and optimize this aspect of their codes. Existing tools target only specific factors of memory performance, such as hardware layout, allocations, or access instructions. However, today's tools do not suffice to characterize the complex relationships between these factors. Further, they require advanced expertise to be used effectively. We present MemAxes, a tool based on a novel approach for analytic-driven visualization of memory performance data. MemAxes uniquely allows users to analyze the different aspects related to memory performance by providing multiple visual contexts for a centralized dataset. We define mappings of sampled memory access data to new and existing visual metaphors, each of which enabling a user to perform different analysis tasks. We present methods to guide user interaction by scoring subsets of the data based on known performance problems. This scoring is used to provide visual cues and automatically extract clusters of interest. We designed MemAxes in collaboration with experts in HPC and demonstrate its effectiveness in case studies.

On Establishing Big Data Wave Breakwaters with Analytics (Invited)

NASA Astrophysics Data System (ADS)

Riedel, M.

2013-12-01

The Research Data Alliance Big Data Analytics (RDA-BDA) Interest Group seeks to develop community based recommendations on feasible data analytics approaches to address scientific community needs of utilizing large quantities of data. RDA-BDA seeks to analyze different scientific domain applications and their potential use of various big data analytics techniques. A systematic classification of feasible combinations of analysis algorithms, analytical tools, data and resource characteristics and scientific queries will be covered in these recommendations. These combinations are complex since a wide variety of different data analysis algorithms exist (e.g. specific algorithms using GPUs of analyzing brain images) that need to work together with multiple analytical tools reaching from simple (iterative) map-reduce methods (e.g. with Apache Hadoop or Twister) to sophisticated higher level frameworks that leverage machine learning algorithms (e.g. Apache Mahout). These computational analysis techniques are often augmented with visual analytics techniques (e.g. computational steering on large-scale high performance computing platforms) to put the human judgement into the analysis loop or new approaches with databases that are designed to support new forms of unstructured or semi-structured data as opposed to the rather tradtional structural databases (e.g. relational databases). More recently, data analysis and underpinned analytics frameworks also have to consider energy footprints of underlying resources. To sum up, the aim of this talk is to provide pieces of information to understand big data analytics in the context of science and engineering using the aforementioned classification as the lighthouse and as the frame of reference for a systematic approach. This talk will provide insights about big data analytics methods in context of science within varios communities and offers different views of how approaches of correlation and causality offer complementary methods to advance in science and engineering today. The RDA Big Data Analytics Group seeks to understand what approaches are not only technically feasible, but also scientifically feasible. The lighthouse Goal of the RDA Big Data Analytics Group is a classification of clever combinations of various Technologies and scientific applications in order to provide clear recommendations to the scientific community what approaches are technicalla and scientifically feasible.

Bring It to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis.

PubMed

Stein, Manuel; Janetzko, Halldor; Lamprecht, Andreas; Breitkreutz, Thorsten; Zimmermann, Philipp; Goldlucke, Bastian; Schreck, Tobias; Andrienko, Gennady; Grossniklaus, Michael; Keim, Daniel A

2018-01-01

Analysts in professional team sport regularly perform analysis to gain strategic and tactical insights into player and team behavior. Goals of team sport analysis regularly include identification of weaknesses of opposing teams, or assessing performance and improvement potential of a coached team. Current analysis workflows are typically based on the analysis of team videos. Also, analysts can rely on techniques from Information Visualization, to depict e.g., player or ball trajectories. However, video analysis is typically a time-consuming process, where the analyst needs to memorize and annotate scenes. In contrast, visualization typically relies on an abstract data model, often using abstract visual mappings, and is not directly linked to the observed movement context anymore. We propose a visual analytics system that tightly integrates team sport video recordings with abstract visualization of underlying trajectory data. We apply appropriate computer vision techniques to extract trajectory data from video input. Furthermore, we apply advanced trajectory and movement analysis techniques to derive relevant team sport analytic measures for region, event and player analysis in the case of soccer analysis. Our system seamlessly integrates video and visualization modalities, enabling analysts to draw on the advantages of both analysis forms. Several expert studies conducted with team sport analysts indicate the effectiveness of our integrated approach.

ANALYTiC: An Active Learning System for Trajectory Classification.

PubMed

Soares Junior, Amilcar; Renso, Chiara; Matwin, Stan

2017-01-01

The increasing availability and use of positioning devices has resulted in large volumes of trajectory data. However, semantic annotations for such data are typically added by domain experts, which is a time-consuming task. Machine-learning algorithms can help infer semantic annotations from trajectory data by learning from sets of labeled data. Specifically, active learning approaches can minimize the set of trajectories to be annotated while preserving good performance measures. The ANALYTiC web-based interactive tool visually guides users through this annotation process.

Visual Analytics for Law Enforcement: Deploying a Service-Oriented Analytic Framework for Web-based Visualization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dowson, Scott T.; Bruce, Joseph R.; Best, Daniel M.

2009-04-14

This paper presents key components of the Law Enforcement Information Framework (LEIF) that provides communications, situational awareness, and visual analytics tools in a service-oriented architecture supporting web-based desktop and handheld device users. LEIF simplifies interfaces and visualizations of well-established visual analytical techniques to improve usability. Advanced analytics capability is maintained by enhancing the underlying processing to support the new interface. LEIF development is driven by real-world user feedback gathered through deployments at three operational law enforcement organizations in the US. LEIF incorporates a robust information ingest pipeline supporting a wide variety of information formats. LEIF also insulates interface and analyticalmore » components from information sources making it easier to adapt the framework for many different data repositories.« less

Examining the Use of a Visual Analytics System for Sensemaking Tasks: Case Studies with Domain Experts.

PubMed

Kang, Youn-Ah; Stasko, J

2012-12-01

While the formal evaluation of systems in visual analytics is still relatively uncommon, particularly rare are case studies of prolonged system use by domain analysts working with their own data. Conducting case studies can be challenging, but it can be a particularly effective way to examine whether visual analytics systems are truly helping expert users to accomplish their goals. We studied the use of a visual analytics system for sensemaking tasks on documents by six analysts from a variety of domains. We describe their application of the system along with the benefits, issues, and problems that we uncovered. Findings from the studies identify features that visual analytics systems should emphasize as well as missing capabilities that should be addressed. These findings inform design implications for future systems.

The forensic validity of visual analytics

NASA Astrophysics Data System (ADS)

Erbacher, Robert F.

2008-01-01

The wider use of visualization and visual analytics in wide ranging fields has led to the need for visual analytics capabilities to be legally admissible, especially when applied to digital forensics. This brings the need to consider legal implications when performing visual analytics, an issue not traditionally examined in visualization and visual analytics techniques and research. While digital data is generally admissible under the Federal Rules of Evidence [10][21], a comprehensive validation of the digital evidence is considered prudent. A comprehensive validation requires validation of the digital data under rules for authentication, hearsay, best evidence rule, and privilege. Additional issues with digital data arise when exploring digital data related to admissibility and the validity of what information was examined, to what extent, and whether the analysis process was sufficiently covered by a search warrant. For instance, a search warrant generally covers very narrow requirements as to what law enforcement is allowed to examine and acquire during an investigation. When searching a hard drive for child pornography, how admissible is evidence of an unrelated crime, i.e. drug dealing. This is further complicated by the concept of "in plain view". When performing an analysis of a hard drive what would be considered "in plain view" when analyzing a hard drive. The purpose of this paper is to discuss the issues of digital forensics and the related issues as they apply to visual analytics and identify how visual analytics techniques fit into the digital forensics analysis process, how visual analytics techniques can improve the legal admissibility of digital data, and identify what research is needed to further improve this process. The goal of this paper is to open up consideration of legal ramifications among the visualization community; the author is not a lawyer and the discussions are not meant to be inclusive of all differences in laws between states and countries.

Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework.

PubMed

El-Assady, Mennatallah; Sevastjanova, Rita; Sperrle, Fabian; Keim, Daniel; Collins, Christopher

2018-01-01

Topic modeling algorithms are widely used to analyze the thematic composition of text corpora but remain difficult to interpret and adjust. Addressing these limitations, we present a modular visual analytics framework, tackling the understandability and adaptability of topic models through a user-driven reinforcement learning process which does not require a deep understanding of the underlying topic modeling algorithms. Given a document corpus, our approach initializes two algorithm configurations based on a parameter space analysis that enhances document separability. We abstract the model complexity in an interactive visual workspace for exploring the automatic matching results of two models, investigating topic summaries, analyzing parameter distributions, and reviewing documents. The main contribution of our work is an iterative decision-making technique in which users provide a document-based relevance feedback that allows the framework to converge to a user-endorsed topic distribution. We also report feedback from a two-stage study which shows that our technique results in topic model quality improvements on two independent measures.

Semantic Enrichment of Movement Behavior with Foursquare--A Visual Analytics Approach.

PubMed

Krueger, Robert; Thom, Dennis; Ertl, Thomas

2015-08-01

In recent years, many approaches have been developed that efficiently and effectively visualize movement data, e.g., by providing suitable aggregation strategies to reduce visual clutter. Analysts can use them to identify distinct movement patterns, such as trajectories with similar direction, form, length, and speed. However, less effort has been spent on finding the semantics behind movements, i.e. why somebody or something is moving. This can be of great value for different applications, such as product usage and consumer analysis, to better understand urban dynamics, and to improve situational awareness. Unfortunately, semantic information often gets lost when data is recorded. Thus, we suggest to enrich trajectory data with POI information using social media services and show how semantic insights can be gained. Furthermore, we show how to handle semantic uncertainties in time and space, which result from noisy, unprecise, and missing data, by introducing a POI decision model in combination with highly interactive visualizations. Finally, we evaluate our approach with two case studies on a large electric scooter data set and test our model on data with known ground truth.

Eye-Tracking as a Tool to Evaluate Functional Ability in Everyday Tasks in Glaucoma.

PubMed

Kasneci, Enkelejda; Black, Alex A; Wood, Joanne M

2017-01-01

To date, few studies have investigated the eye movement patterns of individuals with glaucoma while they undertake everyday tasks in real-world settings. While some of these studies have reported possible compensatory gaze patterns in those with glaucoma who demonstrated good task performance despite their visual field loss, little is known about the complex interaction between field loss and visual scanning strategies and the impact on task performance and, consequently, on quality of life. We review existing approaches that have quantified the effect of glaucomatous visual field defects on the ability to undertake everyday activities through the use of eye movement analysis. Furthermore, we discuss current developments in eye-tracking technology and the potential for combining eye-tracking with virtual reality and advanced analytical approaches. Recent technological developments suggest that systems based on eye-tracking have the potential to assist individuals with glaucomatous loss to maintain or even improve their performance on everyday tasks and hence enhance their long-term quality of life. We discuss novel approaches for studying the visual search behavior of individuals with glaucoma that have the potential to assist individuals with glaucoma, through the use of personalized programs that take into consideration the individual characteristics of their remaining visual field and visual search behavior.

Eye-Tracking as a Tool to Evaluate Functional Ability in Everyday Tasks in Glaucoma

PubMed Central

Black, Alex A.

2017-01-01

To date, few studies have investigated the eye movement patterns of individuals with glaucoma while they undertake everyday tasks in real-world settings. While some of these studies have reported possible compensatory gaze patterns in those with glaucoma who demonstrated good task performance despite their visual field loss, little is known about the complex interaction between field loss and visual scanning strategies and the impact on task performance and, consequently, on quality of life. We review existing approaches that have quantified the effect of glaucomatous visual field defects on the ability to undertake everyday activities through the use of eye movement analysis. Furthermore, we discuss current developments in eye-tracking technology and the potential for combining eye-tracking with virtual reality and advanced analytical approaches. Recent technological developments suggest that systems based on eye-tracking have the potential to assist individuals with glaucomatous loss to maintain or even improve their performance on everyday tasks and hence enhance their long-term quality of life. We discuss novel approaches for studying the visual search behavior of individuals with glaucoma that have the potential to assist individuals with glaucoma, through the use of personalized programs that take into consideration the individual characteristics of their remaining visual field and visual search behavior. PMID:28293433

Web-based Visual Analytics for Extreme Scale Climate Science

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steed, Chad A; Evans, Katherine J; Harney, John F

In this paper, we introduce a Web-based visual analytics framework for democratizing advanced visualization and analysis capabilities pertinent to large-scale earth system simulations. We address significant limitations of present climate data analysis tools such as tightly coupled dependencies, ineffi- cient data movements, complex user interfaces, and static visualizations. Our Web-based visual analytics framework removes critical barriers to the widespread accessibility and adoption of advanced scientific techniques. Using distributed connections to back-end diagnostics, we minimize data movements and leverage HPC platforms. We also mitigate system dependency issues by employing a RESTful interface. Our framework embraces the visual analytics paradigm via newmore » visual navigation techniques for hierarchical parameter spaces, multi-scale representations, and interactive spatio-temporal data mining methods that retain details. Although generalizable to other science domains, the current work focuses on improving exploratory analysis of large-scale Community Land Model (CLM) and Community Atmosphere Model (CAM) simulations.« less

A reference web architecture and patterns for real-time visual analytics on large streaming data

NASA Astrophysics Data System (ADS)

Kandogan, Eser; Soroker, Danny; Rohall, Steven; Bak, Peter; van Ham, Frank; Lu, Jie; Ship, Harold-Jeffrey; Wang, Chun-Fu; Lai, Jennifer

2013-12-01

Monitoring and analysis of streaming data, such as social media, sensors, and news feeds, has become increasingly important for business and government. The volume and velocity of incoming data are key challenges. To effectively support monitoring and analysis, statistical and visual analytics techniques need to be seamlessly integrated; analytic techniques for a variety of data types (e.g., text, numerical) and scope (e.g., incremental, rolling-window, global) must be properly accommodated; interaction, collaboration, and coordination among several visualizations must be supported in an efficient manner; and the system should support the use of different analytics techniques in a pluggable manner. Especially in web-based environments, these requirements pose restrictions on the basic visual analytics architecture for streaming data. In this paper we report on our experience of building a reference web architecture for real-time visual analytics of streaming data, identify and discuss architectural patterns that address these challenges, and report on applying the reference architecture for real-time Twitter monitoring and analysis.

Visual Analytics in Public Safety: Example Capabilities for Example Government Agencies

DTIC Science & Technology

2011-10-01

is not limited to: the Police Records Information Management Environment for British Columbia (PRIME-BC), the Police Reporting and Occurrence System...and filtering for rapid identification of relevant documents - Graphical environment for visual evidence marshaling - Interactive linking and...analytical reasoning facilitated by interactive visual interfaces and integration with computational analytics. Indeed, a wide variety of technologies

TimeBench: a data model and software library for visual analytics of time-oriented data.

PubMed

Rind, Alexander; Lammarsch, Tim; Aigner, Wolfgang; Alsallakh, Bilal; Miksch, Silvia

2013-12-01

Time-oriented data play an essential role in many Visual Analytics scenarios such as extracting medical insights from collections of electronic health records or identifying emerging problems and vulnerabilities in network traffic. However, many software libraries for Visual Analytics treat time as a flat numerical data type and insufficiently tackle the complexity of the time domain such as calendar granularities and intervals. Therefore, developers of advanced Visual Analytics designs need to implement temporal foundations in their application code over and over again. We present TimeBench, a software library that provides foundational data structures and algorithms for time-oriented data in Visual Analytics. Its expressiveness and developer accessibility have been evaluated through application examples demonstrating a variety of challenges with time-oriented data and long-term developer studies conducted in the scope of research and student projects.

Classification and Visualization of Physical and Chemical Properties of Falsified Medicines with Handheld Raman Spectroscopy and X-Ray Computed Tomography.

PubMed

Kakio, Tomoko; Yoshida, Naoko; Macha, Susan; Moriguchi, Kazunobu; Hiroshima, Takashi; Ikeda, Yukihiro; Tsuboi, Hirohito; Kimura, Kazuko

2017-09-01

Analytical methods for the detection of substandard and falsified medical products (SFs) are important for public health and patient safety. Research to understand how the physical and chemical properties of SFs can be most effectively applied to distinguish the SFs from authentic products has not yet been investigated enough. Here, we investigated the usefulness of two analytical methods, handheld Raman spectroscopy (handheld Raman) and X-ray computed tomography (X-ray CT), for detecting SFs among oral solid antihypertensive pharmaceutical products containing candesartan cilexetil as an active pharmaceutical ingredient (API). X-ray CT visualized at least two different types of falsified tablets, one containing many cracks and voids and the other containing aggregates with high electron density, such as from the presence of the heavy elements. Generic products that purported to contain equivalent amounts of API to the authentic products were discriminated from the authentic products by the handheld Raman and the different physical structure on X-ray CT. Approach to investigate both the chemical and physical properties with handheld Raman and X-ray CT, respectively, promise the accurate discrimination of the SFs, even if their visual appearance is similar with authentic products. We present a decision tree for investigating the authenticity of samples purporting to be authentic commercial tablets. Our results indicate that the combination approach of visual observation, handheld Raman and X-ray CT is a powerful strategy for nondestructive discrimination of suspect samples.

Rethinking Visual Analytics for Streaming Data Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Crouser, R. Jordan; Franklin, Lyndsey; Cook, Kris

In the age of data science, the use of interactive information visualization techniques has become increasingly ubiquitous. From online scientific journals to the New York Times graphics desk, the utility of interactive visualization for both storytelling and analysis has become ever more apparent. As these techniques have become more readily accessible, the appeal of combining interactive visualization with computational analysis continues to grow. Arising out of a need for scalable, human-driven analysis, primary objective of visual analytics systems is to capitalize on the complementary strengths of human and machine analysis, using interactive visualization as a medium for communication between themore » two. These systems leverage developments from the fields of information visualization, computer graphics, machine learning, and human-computer interaction to support insight generation in areas where purely computational analyses fall short. Over the past decade, visual analytics systems have generated remarkable advances in many historically challenging analytical contexts. These include areas such as modeling political systems [Crouser et al. 2012], detecting financial fraud [Chang et al. 2008], and cybersecurity [Harrison et al. 2012]. In each of these contexts, domain expertise and human intuition is a necessary component of the analysis. This intuition is essential to building trust in the analytical products, as well as supporting the translation of evidence into actionable insight. In addition, each of these examples also highlights the need for scalable analysis. In each case, it is infeasible for a human analyst to manually assess the raw information unaided, and the communication overhead to divide the task between a large number of analysts makes simple parallelism intractable. Regardless of the domain, visual analytics tools strive to optimize the allocation of human analytical resources, and to streamline the sensemaking process on data that is massive, complex, incomplete, and uncertain in scenarios requiring human judgment.« less

Visual and Analytic Strategies in Geometry

ERIC Educational Resources Information Center

Kospentaris, George; Vosniadou, Stella; Kazic, Smaragda; Thanou, Emilian

2016-01-01

We argue that there is an increasing reliance on analytic strategies compared to visuospatial strategies, which is related to geometry expertise and not on individual differences in cognitive style. A Visual/Analytic Strategy Test (VAST) was developed to investigate the use of visuo-spatial and analytic strategies in geometry in 30 mathematics…

Demons registration for in vivo and deformable laser scanning confocal endomicroscopy.

PubMed

Chiew, Wei-Ming; Lin, Feng; Seah, Hock Soon

2017-09-01

A critical effect found in noninvasive in vivo endomicroscopic imaging modalities is image distortions due to sporadic movement exhibited by living organisms. In three-dimensional confocal imaging, this effect results in a dataset that is tilted across deeper slices. Apart from that, the sequential flow of the imaging-processing pipeline restricts real-time adjustments due to the unavailability of information obtainable only from subsequent stages. To solve these problems, we propose an approach to render Demons-registered datasets as they are being captured, focusing on the coupling between registration and visualization. To improve the acquisition process, we also propose a real-time visual analytics tool, which complements the imaging pipeline and the Demons registration pipeline with useful visual indicators to provide real-time feedback for immediate adjustments. We highlight the problem of deformation within the visualization pipeline for object-ordered and image-ordered rendering. Visualizations of critical information including registration forces and partial renderings of the captured data are also presented in the analytics system. We demonstrate the advantages of the algorithmic design through experimental results with both synthetically deformed datasets and actual in vivo, time-lapse tissue datasets expressing natural deformations. Remarkably, this algorithm design is for embedded implementation in intelligent biomedical imaging instrumentation with customizable circuitry. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Demons registration for in vivo and deformable laser scanning confocal endomicroscopy

NASA Astrophysics Data System (ADS)

Chiew, Wei Ming; Lin, Feng; Seah, Hock Soon

2017-09-01

A critical effect found in noninvasive in vivo endomicroscopic imaging modalities is image distortions due to sporadic movement exhibited by living organisms. In three-dimensional confocal imaging, this effect results in a dataset that is tilted across deeper slices. Apart from that, the sequential flow of the imaging-processing pipeline restricts real-time adjustments due to the unavailability of information obtainable only from subsequent stages. To solve these problems, we propose an approach to render Demons-registered datasets as they are being captured, focusing on the coupling between registration and visualization. To improve the acquisition process, we also propose a real-time visual analytics tool, which complements the imaging pipeline and the Demons registration pipeline with useful visual indicators to provide real-time feedback for immediate adjustments. We highlight the problem of deformation within the visualization pipeline for object-ordered and image-ordered rendering. Visualizations of critical information including registration forces and partial renderings of the captured data are also presented in the analytics system. We demonstrate the advantages of the algorithmic design through experimental results with both synthetically deformed datasets and actual in vivo, time-lapse tissue datasets expressing natural deformations. Remarkably, this algorithm design is for embedded implementation in intelligent biomedical imaging instrumentation with customizable circuitry.

Keeping Connected: A Review of the Research Relationship

ERIC Educational Resources Information Center

Moss, Julianne; Hay, Trevor

2014-01-01

In this paper, some key findings of the Keeping Connected project are discussed in light of the methodological challenges of developing an analytical approach in a large-scale study, particularly in starting with open-ended, participant-selected, digital still visual images as part of 31 longitudinal case studies. The paper works to clarify the…

Analyzing Effective Communication in Mathematics Group Work: The Role of Visual Mediators and Technical Terms

ERIC Educational Resources Information Center

Ryve, Andreas; Nilsson, Per; Pettersson, Kerstin

2013-01-01

Analyzing and designing productive group work and effective communication constitute ongoing research interests in mathematics education. In this article we contribute to this research by using and developing a newly introduced analytical approach for examining effective communication within group work in mathematics education. By using data from…

A Sensemaking Approach to Visual Analytics of Attribute-Rich Social Networks

ERIC Educational Resources Information Center

Gou, Liang

2012-01-01

Social networks have become more complex, in particular considering the fact that elements in social networks are not only abstract topological nodes and links, but contain rich social attributes and reflecting diverse social relationships. For example, in a co-authorship social network in a scientific community, nodes in the social network, which…

Intuitive Understanding of Solutions of Partially Differential Equations

ERIC Educational Resources Information Center

Kobayashi, Y.

2008-01-01

This article uses diagrams that help the observer see how solutions of the wave equation and heat conduction equation are obtained. The analytical approach cannot necessarily show the mechanisms of the key to the solution without transforming the differential equation into a more convenient form by separation of variables. The visual clues based…

Big data analytics in immunology: a knowledge-based approach.

PubMed

Zhang, Guang Lan; Sun, Jing; Chitkushev, Lou; Brusic, Vladimir

2014-01-01

With the vast amount of immunological data available, immunology research is entering the big data era. These data vary in granularity, quality, and complexity and are stored in various formats, including publications, technical reports, and databases. The challenge is to make the transition from data to actionable knowledge and wisdom and bridge the knowledge gap and application gap. We report a knowledge-based approach based on a framework called KB-builder that facilitates data mining by enabling fast development and deployment of web-accessible immunological data knowledge warehouses. Immunological knowledge discovery relies heavily on both the availability of accurate, up-to-date, and well-organized data and the proper analytics tools. We propose the use of knowledge-based approaches by developing knowledgebases combining well-annotated data with specialized analytical tools and integrating them into analytical workflow. A set of well-defined workflow types with rich summarization and visualization capacity facilitates the transformation from data to critical information and knowledge. By using KB-builder, we enabled streamlining of normally time-consuming processes of database development. The knowledgebases built using KB-builder will speed up rational vaccine design by providing accurate and well-annotated data coupled with tailored computational analysis tools and workflow.

StreamMap: Smooth Dynamic Visualization of High-Density Streaming Points.

PubMed

Li, Chenhui; Baciu, George; Han, Yu

2018-03-01

Interactive visualization of streaming points for real-time scatterplots and linear blending of correlation patterns is increasingly becoming the dominant mode of visual analytics for both big data and streaming data from active sensors and broadcasting media. To better visualize and interact with inter-stream patterns, it is generally necessary to smooth out gaps or distortions in the streaming data. Previous approaches either animate the points directly or present a sampled static heat-map. We propose a new approach, called StreamMap, to smoothly blend high-density streaming points and create a visual flow that emphasizes the density pattern distributions. In essence, we present three new contributions for the visualization of high-density streaming points. The first contribution is a density-based method called super kernel density estimation that aggregates streaming points using an adaptive kernel to solve the overlapping problem. The second contribution is a robust density morphing algorithm that generates several smooth intermediate frames for a given pair of frames. The third contribution is a trend representation design that can help convey the flow directions of the streaming points. The experimental results on three datasets demonstrate the effectiveness of StreamMap when dynamic visualization and visual analysis of trend patterns on streaming points are required.

A visual analytics approach for pattern-recognition in patient-generated data.

PubMed

Feller, Daniel J; Burgermaster, Marissa; Levine, Matthew E; Smaldone, Arlene; Davidson, Patricia G; Albers, David J; Mamykina, Lena

2018-06-13

To develop and test a visual analytics tool to help clinicians identify systematic and clinically meaningful patterns in patient-generated data (PGD) while decreasing perceived information overload. Participatory design was used to develop Glucolyzer, an interactive tool featuring hierarchical clustering and a heatmap visualization to help registered dietitians (RDs) identify associative patterns between blood glucose levels and per-meal macronutrient composition for individuals with type 2 diabetes (T2DM). Ten RDs participated in a within-subjects experiment to compare Glucolyzer to a static logbook format. For each representation, participants had 25 minutes to examine 1 month of diabetes self-monitoring data captured by an individual with T2DM and identify clinically meaningful patterns. We compared the quality and accuracy of the observations generated using each representation. Participants generated 50% more observations when using Glucolyzer (98) than when using the logbook format (64) without any loss in accuracy (69% accuracy vs 62%, respectively, p = .17). Participants identified more observations that included ingredients other than carbohydrates using Glucolyzer (36% vs 16%, p = .027). Fewer RDs reported feelings of information overload using Glucolyzer compared to the logbook format. Study participants displayed variable acceptance of hierarchical clustering. Visual analytics have the potential to mitigate provider concerns about the volume of self-monitoring data. Glucolyzer helped dietitians identify meaningful patterns in self-monitoring data without incurring perceived information overload. Future studies should assess whether similar tools can support clinicians in personalizing behavioral interventions that improve patient outcomes.

Lack of habituation of evoked visual potentials in analytic information processing style: evidence in healthy subjects.

PubMed

Buonfiglio, Marzia; Toscano, M; Puledda, F; Avanzini, G; Di Clemente, L; Di Sabato, F; Di Piero, V

2015-03-01

Habituation is considered one of the most basic mechanisms of learning. Habituation deficit to several sensory stimulations has been defined as a trait of migraine brain and also observed in other disorders. On the other hand, analytic information processing style is characterized by the habit of continually evaluating stimuli and it has been associated with migraine. We investigated a possible correlation between lack of habituation of evoked visual potentials and analytic cognitive style in healthy subjects. According to Sternberg-Wagner self-assessment inventory, 15 healthy volunteers (HV) with high analytic score and 15 HV with high global score were recruited. Both groups underwent visual evoked potentials recordings after psychological evaluation. We observed significant lack of habituation in analytical individuals compared to global group. In conclusion, a reduced habituation of visual evoked potentials has been observed in analytic subjects. Our results suggest that further research should be undertaken regarding the relationship between analytic cognitive style and lack of habituation in both physiological and pathophysiological conditions.

A Data-Driven Approach to Interactive Visualization of Power Grids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhu, Jun

Driven by emerging industry standards, electric utilities and grid coordination organizations are eager to seek advanced tools to assist grid operators to perform mission-critical tasks and enable them to make quick and accurate decisions. The emerging field of visual analytics holds tremendous promise for improving the business practices in today’s electric power industry. The conducted investigation, however, has revealed that the existing commercial power grid visualization tools heavily rely on human designers, hindering user’s ability to discover. Additionally, for a large grid, it is very labor-intensive and costly to build and maintain the pre-designed visual displays. This project proposes amore » data-driven approach to overcome the common challenges. The proposed approach relies on developing powerful data manipulation algorithms to create visualizations based on the characteristics of empirically or mathematically derived data. The resulting visual presentations emphasize what the data is rather than how the data should be presented, thus fostering comprehension and discovery. Furthermore, the data-driven approach formulates visualizations on-the-fly. It does not require a visualization design stage, completely eliminating or significantly reducing the cost for building and maintaining visual displays. The research and development (R&D) conducted in this project is mainly divided into two phases. The first phase (Phase I & II) focuses on developing data driven techniques for visualization of power grid and its operation. Various data-driven visualization techniques were investigated, including pattern recognition for auto-generation of one-line diagrams, fuzzy model based rich data visualization for situational awareness, etc. The R&D conducted during the second phase (Phase IIB) focuses on enhancing the prototyped data driven visualization tool based on the gathered requirements and use cases. The goal is to evolve the prototyped tool developed during the first phase into a commercial grade product. We will use one of the identified application areas as an example to demonstrate how research results achieved in this project are successfully utilized to address an emerging industry need. In summary, the data-driven visualization approach developed in this project has proven to be promising for building the next-generation power grid visualization tools. Application of this approach has resulted in a state-of-the-art commercial tool currently being leveraged by more than 60 utility organizations in North America and Europe .« less

A graph algebra for scalable visual analytics.

PubMed

Shaverdian, Anna A; Zhou, Hao; Michailidis, George; Jagadish, Hosagrahar V

2012-01-01

Visual analytics (VA), which combines analytical techniques with advanced visualization features, is fast becoming a standard tool for extracting information from graph data. Researchers have developed many tools for this purpose, suggesting a need for formal methods to guide these tools' creation. Increased data demands on computing requires redesigning VA tools to consider performance and reliability in the context of analysis of exascale datasets. Furthermore, visual analysts need a way to document their analyses for reuse and results justification. A VA graph framework encapsulated in a graph algebra helps address these needs. Its atomic operators include selection and aggregation. The framework employs a visual operator and supports dynamic attributes of data to enable scalable visual exploration of data.

Data visualisation in surveillance for injury prevention and control: conceptual bases and case studies

PubMed Central

Martinez, Ramon; Ordunez, Pedro; Soliz, Patricia N; Ballesteros, Michael F

2016-01-01

Background The complexity of current injury-related health issues demands the usage of diverse and massive data sets for comprehensive analyses, and application of novel methods to communicate data effectively to the public health community, decision-makers and the public. Recent advances in information visualisation, availability of new visual analytic methods and tools, and progress on information technology provide an opportunity for shaping the next generation of injury surveillance. Objective To introduce data visualisation conceptual bases, and propose a visual analytic and visualisation platform in public health surveillance for injury prevention and control. Methods The paper introduces data visualisation conceptual bases, describes a visual analytic and visualisation platform, and presents two real-world case studies illustrating their application in public health surveillance for injury prevention and control. Results Application of visual analytic and visualisation platform is presented as solution for improved access to heterogeneous data sources, enhance data exploration and analysis, communicate data effectively, and support decision-making. Conclusions Applications of data visualisation concepts and visual analytic platform could play a key role to shape the next generation of injury surveillance. Visual analytic and visualisation platform could improve data use, the analytic capacity, and ability to effectively communicate findings and key messages. The public health surveillance community is encouraged to identify opportunities to develop and expand its use in injury prevention and control. PMID:26728006

Integrated remote sensing and visualization (IRSV) system for transportation infrastructure operations and management, phase two, volume 4 : web-based bridge information database--visualization analytics and distributed sensing.

DOT National Transportation Integrated Search

2012-03-01

This report introduces the design and implementation of a Web-based bridge information visual analytics system. This : project integrates Internet, multiple databases, remote sensing, and other visualization technologies. The result : combines a GIS ...

Visualisation and Analytic Strategies for Anticipating the Folding of Nets

ERIC Educational Resources Information Center

Wright, Vince

2016-01-01

Visual and analytic strategies are features of students' schemes for spatial tasks. The strategies used by six students to anticipate the folding of nets were investigated. Evidence suggested that visual and analytic strategies were strongly connected in competent performance.

Generalized plasma dispersion function: One-solve-all treatment, visualizations, and application to Landau damping

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xie, Hua-Sheng

2013-09-15

A unified, fast, and effective approach is developed for numerical calculation of the well-known plasma dispersion function with extensions from Maxwellian distribution to almost arbitrary distribution functions, such as the δ, flat top, triangular, κ or Lorentzian, slowing down, and incomplete Maxwellian distributions. The singularity and analytic continuation problems are also solved generally. Given that the usual conclusion γ∝∂f{sub 0}/∂v is only a rough approximation when discussing the distribution function effects on Landau damping, this approach provides a useful tool for rigorous calculations of the linear wave and instability properties of plasma for general distribution functions. The results are alsomore » verified via a linear initial value simulation approach. Intuitive visualizations of the generalized plasma dispersion function are also provided.« less

Explorative visual analytics on interval-based genomic data and their metadata.

PubMed

Jalili, Vahid; Matteucci, Matteo; Masseroli, Marco; Ceri, Stefano

2017-12-04

With the wide-spreading of public repositories of NGS processed data, the availability of user-friendly and effective tools for data exploration, analysis and visualization is becoming very relevant. These tools enable interactive analytics, an exploratory approach for the seamless "sense-making" of data through on-the-fly integration of analysis and visualization phases, suggested not only for evaluating processing results, but also for designing and adapting NGS data analysis pipelines. This paper presents abstractions for supporting the early analysis of NGS processed data and their implementation in an associated tool, named GenoMetric Space Explorer (GeMSE). This tool serves the needs of the GenoMetric Query Language, an innovative cloud-based system for computing complex queries over heterogeneous processed data. It can also be used starting from any text files in standard BED, BroadPeak, NarrowPeak, GTF, or general tab-delimited format, containing numerical features of genomic regions; metadata can be provided as text files in tab-delimited attribute-value format. GeMSE allows interactive analytics, consisting of on-the-fly cycling among steps of data exploration, analysis and visualization that help biologists and bioinformaticians in making sense of heterogeneous genomic datasets. By means of an explorative interaction support, users can trace past activities and quickly recover their results, seamlessly going backward and forward in the analysis steps and comparative visualizations of heatmaps. GeMSE effective application and practical usefulness is demonstrated through significant use cases of biological interest. GeMSE is available at http://www.bioinformatics.deib.polimi.it/GeMSE/ , and its source code is available at https://github.com/Genometric/GeMSE under GPLv3 open-source license.

Modeling and Visualizing Flow of Chemical Agents Across Complex Terrain

NASA Technical Reports Server (NTRS)

Kao, David; Kramer, Marc; Chaderjian, Neal

2005-01-01

Release of chemical agents across complex terrain presents a real threat to homeland security. Modeling and visualization tools are being developed that capture flow fluid terrain interaction as well as point dispersal downstream flow paths. These analytic tools when coupled with UAV atmospheric observations provide predictive capabilities to allow for rapid emergency response as well as developing a comprehensive preemptive counter-threat evacuation plan. The visualization tools involve high-end computing and massive parallel processing combined with texture mapping. We demonstrate our approach across a mountainous portion of North California under two contrasting meteorological conditions. Animations depicting flow over this geographical location provide immediate assistance in decision support and crisis management.

Dissociable meta-analytic brain networks contribute to coordinated emotional processing.

PubMed

Riedel, Michael C; Yanes, Julio A; Ray, Kimberly L; Eickhoff, Simon B; Fox, Peter T; Sutherland, Matthew T; Laird, Angela R

2018-06-01

Meta-analytic techniques for mining the neuroimaging literature continue to exert an impact on our conceptualization of functional brain networks contributing to human emotion and cognition. Traditional theories regarding the neurobiological substrates contributing to affective processing are shifting from regional- towards more network-based heuristic frameworks. To elucidate differential brain network involvement linked to distinct aspects of emotion processing, we applied an emergent meta-analytic clustering approach to the extensive body of affective neuroimaging results archived in the BrainMap database. Specifically, we performed hierarchical clustering on the modeled activation maps from 1,747 experiments in the affective processing domain, resulting in five meta-analytic groupings of experiments demonstrating whole-brain recruitment. Behavioral inference analyses conducted for each of these groupings suggested dissociable networks supporting: (1) visual perception within primary and associative visual cortices, (2) auditory perception within primary auditory cortices, (3) attention to emotionally salient information within insular, anterior cingulate, and subcortical regions, (4) appraisal and prediction of emotional events within medial prefrontal and posterior cingulate cortices, and (5) induction of emotional responses within amygdala and fusiform gyri. These meta-analytic outcomes are consistent with a contemporary psychological model of affective processing in which emotionally salient information from perceived stimuli are integrated with previous experiences to engender a subjective affective response. This study highlights the utility of using emergent meta-analytic methods to inform and extend psychological theories and suggests that emotions are manifest as the eventual consequence of interactions between large-scale brain networks. © 2018 Wiley Periodicals, Inc.

Code Pulse: Software Assurance (SWA) Visual Analytics for Dynamic Analysis of Code

DTIC Science & Technology

2014-09-01

31 4.5.1 Market Analysis...competitive market analysis to assess the tool potential. The final transition targets were selected and expressed along with our research on the topic...public release milestones. Details of our testing methodology is in our Software Test Plan deliv- erable, CP- STP -0001. A summary of this approach is

Charles Morris's Semiotic Model and Analytical Studies of Visual and Verbal Representations in Technical Communication

ERIC Educational Resources Information Center

Fan, Jiang-Ping

2006-01-01

In this article, the author demonstrates that the semiotic model proposed by Charles Morris enables us to optimize our understanding of technical communication practices and provides a good point of inquiry. To illustrate this point, the author exemplifies the semiotic approaches by scholars in technical communication and elaborates Morris's model…

The World Spatiotemporal Analytics and Mapping Project (WSTAMP): Discovering, Exploring, and Mapping Spatiotemporal Patterns Across Heterogenous Space-Time Data

NASA Astrophysics Data System (ADS)

Morton, A.; Stewart, R.; Held, E.; Piburn, J.; Allen, M. R.; McManamay, R.; Sanyal, J.; Sorokine, A.; Bhaduri, B. L.

2017-12-01

Spatiotemporal (ST) analytics applied to major spatio-temporal data sources from major vendors such as USGS, NOAA, World Bank and World Health Organization have tremendous value in shedding light on the evolution of physical, cultural, and geopolitical landscapes on a local and global level. Especially powerful is the integration of these physical and cultural datasets across multiple and disparate formats, facilitating new interdisciplinary analytics and insights. Realizing this potential first requires an ST data model that addresses challenges in properly merging data from multiple authors, with evolving ontological perspectives, semantical differences, changing attributes, and content that is textual, numeric, categorical, and hierarchical. Equally challenging is the development of analytical and visualization approaches that provide a serious exploration of this integrated data while remaining accessible to practitioners with varied backgrounds. The WSTAMP project at the Oak Ridge National Laboratory has yielded two major results in addressing these challenges: 1) development of the WSTAMP database, a significant advance in ST data modeling that integrates 16000+ attributes covering 200+ countries for over 50 years from over 30 major sources and 2) a novel online ST exploratory and analysis tool providing an array of modern statistical and visualization techniques for analyzing these data temporally, spatially, and spatiotemporally under a standard analytic workflow. We report on these advances, provide an illustrative case study, and inform how others may freely access the tool.

Implementing Operational Analytics using Big Data Technologies to Detect and Predict Sensor Anomalies

NASA Astrophysics Data System (ADS)

Coughlin, J.; Mital, R.; Nittur, S.; SanNicolas, B.; Wolf, C.; Jusufi, R.

2016-09-01

Operational analytics when combined with Big Data technologies and predictive techniques have been shown to be valuable in detecting mission critical sensor anomalies that might be missed by conventional analytical techniques. Our approach helps analysts and leaders make informed and rapid decisions by analyzing large volumes of complex data in near real-time and presenting it in a manner that facilitates decision making. It provides cost savings by being able to alert and predict when sensor degradations pass a critical threshold and impact mission operations. Operational analytics, which uses Big Data tools and technologies, can process very large data sets containing a variety of data types to uncover hidden patterns, unknown correlations, and other relevant information. When combined with predictive techniques, it provides a mechanism to monitor and visualize these data sets and provide insight into degradations encountered in large sensor systems such as the space surveillance network. In this study, data from a notional sensor is simulated and we use big data technologies, predictive algorithms and operational analytics to process the data and predict sensor degradations. This study uses data products that would commonly be analyzed at a site. This study builds on a big data architecture that has previously been proven valuable in detecting anomalies. This paper outlines our methodology of implementing an operational analytic solution through data discovery, learning and training of data modeling and predictive techniques, and deployment. Through this methodology, we implement a functional architecture focused on exploring available big data sets and determine practical analytic, visualization, and predictive technologies.

Data visualisation in surveillance for injury prevention and control: conceptual bases and case studies.

PubMed

Martinez, Ramon; Ordunez, Pedro; Soliz, Patricia N; Ballesteros, Michael F

2016-04-01

The complexity of current injury-related health issues demands the usage of diverse and massive data sets for comprehensive analyses, and application of novel methods to communicate data effectively to the public health community, decision-makers and the public. Recent advances in information visualisation, availability of new visual analytic methods and tools, and progress on information technology provide an opportunity for shaping the next generation of injury surveillance. To introduce data visualisation conceptual bases, and propose a visual analytic and visualisation platform in public health surveillance for injury prevention and control. The paper introduces data visualisation conceptual bases, describes a visual analytic and visualisation platform, and presents two real-world case studies illustrating their application in public health surveillance for injury prevention and control. Application of visual analytic and visualisation platform is presented as solution for improved access to heterogeneous data sources, enhance data exploration and analysis, communicate data effectively, and support decision-making. Applications of data visualisation concepts and visual analytic platform could play a key role to shape the next generation of injury surveillance. Visual analytic and visualisation platform could improve data use, the analytic capacity, and ability to effectively communicate findings and key messages. The public health surveillance community is encouraged to identify opportunities to develop and expand its use in injury prevention and control. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

PAVA: Physiological and Anatomical Visual Analytics for Mapping of Tissue-Specific Concentration and Time-Course Data

EPA Science Inventory

We describe the development and implementation of a Physiological and Anatomical Visual Analytics tool (PAVA), a web browser-based application, used to visualize experimental/simulated chemical time-course data (dosimetry), epidemiological data and Physiologically-Annotated Data ...

A Visual Analytics Paradigm Enabling Trillion-Edge Graph Exploration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wong, Pak C.; Haglin, David J.; Gillen, David S.

We present a visual analytics paradigm and a system prototype for exploring web-scale graphs. A web-scale graph is described as a graph with ~one trillion edges and ~50 billion vertices. While there is an aggressive R&D effort in processing and exploring web-scale graphs among internet vendors such as Facebook and Google, visualizing a graph of that scale still remains an underexplored R&D area. The paper describes a nontraditional peek-and-filter strategy that facilitates the exploration of a graph database of unprecedented size for visualization and analytics. We demonstrate that our system prototype can 1) preprocess a graph with ~25 billion edgesmore » in less than two hours and 2) support database query and visualization on the processed graph database afterward. Based on our computational performance results, we argue that we most likely will achieve the one trillion edge mark (a computational performance improvement of 40 times) for graph visual analytics in the near future.« less

World Spatiotemporal Analytics and Mapping Project (wstamp): Discovering, Exploring, and Mapping Spatiotemporal Patterns across the World's Largest Open Soruce Data Sets

NASA Astrophysics Data System (ADS)

Stewart, R.; Piburn, J.; Sorokine, A.; Myers, A.; Moehl, J.; White, D.

2015-07-01

The application of spatiotemporal (ST) analytics to integrated data from major sources such as the World Bank, United Nations, and dozens of others holds tremendous potential for shedding new light on the evolution of cultural, health, economic, and geopolitical landscapes on a global level. Realizing this potential first requires an ST data model that addresses challenges in properly merging data from multiple authors, with evolving ontological perspectives, semantical differences, and changing attributes, as well as content that is textual, numeric, categorical, and hierarchical. Equally challenging is the development of analytical and visualization approaches that provide a serious exploration of this integrated data while remaining accessible to practitioners with varied backgrounds. The WSTAMP project at Oak Ridge National Laboratory has yielded two major results in addressing these challenges: 1) development of the WSTAMP database, a significant advance in ST data modeling that integrates 10,000+ attributes covering over 200 nation states spanning over 50 years from over 30 major sources and 2) a novel online ST exploratory and analysis tool providing an array of modern statistical and visualization techniques for analyzing these data temporally, spatially, and spatiotemporally under a standard analytic workflow. We discuss the status of this work and report on major findings.

The World Spatiotemporal Analytics and Mapping Project (WSTAMP): Further Progress in Discovering, Exploring, and Mapping Spatiotemporal Patterns Across the World's Largest Open Source Data Sets

NASA Astrophysics Data System (ADS)

Piburn, J.; Stewart, R.; Myers, A.; Sorokine, A.; Axley, E.; Anderson, D.; Burdette, J.; Biddle, C.; Hohl, A.; Eberle, R.; Kaufman, J.; Morton, A.

2017-10-01

Spatiotemporal (ST) analytics applied to major data sources such as the World Bank and World Health Organization has shown tremendous value in shedding light on the evolution of cultural, health, economic, and geopolitical landscapes on a global level. WSTAMP engages this opportunity by situating analysts, data, and analytics together within a visually rich and computationally rigorous online analysis environment. Since introducing WSTAMP at the First International Workshop on Spatiotemporal Computing, several transformative advances have occurred. Collaboration with human computer interaction experts led to a complete interface redesign that deeply immerses the analyst within a ST context, significantly increases visual and textual content, provides navigational crosswalks for attribute discovery, substantially reduce mouse and keyboard actions, and supports user data uploads. Secondly, the database has been expanded to include over 16,000 attributes, 50 years of time, and 200+ nation states and redesigned to support non-annual, non-national, city, and interaction data. Finally, two new analytics are implemented for analyzing large portfolios of multi-attribute data and measuring the behavioral stability of regions along different dimensions. These advances required substantial new approaches in design, algorithmic innovations, and increased computational efficiency. We report on these advances and inform how others may freely access the tool.

Translucent Radiosity: Efficiently Combining Diffuse Inter-Reflection and Subsurface Scattering.

PubMed

Sheng, Yu; Shi, Yulong; Wang, Lili; Narasimhan, Srinivasa G

2014-07-01

It is hard to efficiently model the light transport in scenes with translucent objects for interactive applications. The inter-reflection between objects and their environments and the subsurface scattering through the materials intertwine to produce visual effects like color bleeding, light glows, and soft shading. Monte-Carlo based approaches have demonstrated impressive results but are computationally expensive, and faster approaches model either only inter-reflection or only subsurface scattering. In this paper, we present a simple analytic model that combines diffuse inter-reflection and isotropic subsurface scattering. Our approach extends the classical work in radiosity by including a subsurface scattering matrix that operates in conjunction with the traditional form factor matrix. This subsurface scattering matrix can be constructed using analytic, measurement-based or simulation-based models and can capture both homogeneous and heterogeneous translucencies. Using a fast iterative solution to radiosity, we demonstrate scene relighting and dynamically varying object translucencies at near interactive rates.

Toward Usable Interactive Analytics: Coupling Cognition and Computation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Endert, Alexander; North, Chris; Chang, Remco

Interactive analytics provide users a myriad of computational means to aid in extracting meaningful information from large and complex datasets. Much prior work focuses either on advancing the capabilities of machine-centric approaches by the data mining and machine learning communities, or human-driven methods by the visualization and CHI communities. However, these methods do not yet support a true human-machine symbiotic relationship where users and machines work together collaboratively and adapt to each other to advance an interactive analytic process. In this paper we discuss some of the inherent issues, outlining what we believe are the steps toward usable interactive analyticsmore » that will ultimately increase the effectiveness for both humans and computers to produce insights.« less

Lateral flow devices

DOEpatents

Mazumdar, Debapriya; Liu, Juewen; Lu, Yi

2010-09-21

An analytical test for an analyte comprises (a) a base, having a reaction area and a visualization area, (b) a capture species, on the base in the visualization area, comprising nucleic acid, and (c) analysis chemistry reagents, on the base in the reaction area. The analysis chemistry reagents comprise (i) a substrate comprising nucleic acid and a first label, and (ii) a reactor comprising nucleic acid. The analysis chemistry reagents can react with a sample comprising the analyte and water, to produce a visualization species comprising nucleic acid and the first label, and the capture species can bind the visualization species.

Models of dyadic social interaction.

PubMed Central

Griffin, Dale; Gonzalez, Richard

2003-01-01

We discuss the logic of research designs for dyadic interaction and present statistical models with parameters that are tied to psychologically relevant constructs. Building on Karl Pearson's classic nineteenth-century statistical analysis of within-organism similarity, we describe several approaches to indexing dyadic interdependence and provide graphical methods for visualizing dyadic data. We also describe several statistical and conceptual solutions to the 'levels of analytic' problem in analysing dyadic data. These analytic strategies allow the researcher to examine and measure psychological questions of interdependence and social influence. We provide illustrative data from casually interacting and romantic dyads. PMID:12689382

Clustervision: Visual Supervision of Unsupervised Clustering.

PubMed

Kwon, Bum Chul; Eysenbach, Ben; Verma, Janu; Ng, Kenney; De Filippi, Christopher; Stewart, Walter F; Perer, Adam

2018-01-01

Clustering, the process of grouping together similar items into distinct partitions, is a common type of unsupervised machine learning that can be useful for summarizing and aggregating complex multi-dimensional data. However, data can be clustered in many ways, and there exist a large body of algorithms designed to reveal different patterns. While having access to a wide variety of algorithms is helpful, in practice, it is quite difficult for data scientists to choose and parameterize algorithms to get the clustering results relevant for their dataset and analytical tasks. To alleviate this problem, we built Clustervision, a visual analytics tool that helps ensure data scientists find the right clustering among the large amount of techniques and parameters available. Our system clusters data using a variety of clustering techniques and parameters and then ranks clustering results utilizing five quality metrics. In addition, users can guide the system to produce more relevant results by providing task-relevant constraints on the data. Our visual user interface allows users to find high quality clustering results, explore the clusters using several coordinated visualization techniques, and select the cluster result that best suits their task. We demonstrate this novel approach using a case study with a team of researchers in the medical domain and showcase that our system empowers users to choose an effective representation of their complex data.

ID-Viewer: a visual analytics architecture for infectious diseases surveillance and response management in Pakistan.

PubMed

Ali, M A; Ahsan, Z; Amin, M; Latif, S; Ayyaz, A; Ayyaz, M N

2016-05-01

Globally, disease surveillance systems are playing a significant role in outbreak detection and response management of Infectious Diseases (IDs). However, in developing countries like Pakistan, epidemic outbreaks are difficult to detect due to scarcity of public health data and absence of automated surveillance systems. Our research is intended to formulate an integrated service-oriented visual analytics architecture for ID surveillance, identify key constituents and set up a baseline for easy reproducibility of such systems in the future. This research focuses on development of ID-Viewer, which is a visual analytics decision support system for ID surveillance. It is a blend of intelligent approaches to make use of real-time streaming data from Emergency Departments (EDs) for early outbreak detection, health care resource allocation and epidemic response management. We have developed a robust service-oriented visual analytics architecture for ID surveillance, which provides automated mechanisms for ID data acquisition, outbreak detection and epidemic response management. Classification of chief-complaints is accomplished using dynamic classification module, which employs neural networks and fuzzy-logic to categorize syndromes. Standard routines by Center for Disease Control (CDC), i.e. c1-c3 (c1-mild, c2-medium and c3-ultra), and spatial scan statistics are employed for detection of temporal and spatio-temporal disease outbreaks respectively. Prediction of imminent disease threats is accomplished using support vector regression for early warnings and response planning. Geographical visual analytics displays are developed that allow interactive visualization of syndromic clusters, monitoring disease spread patterns, and identification of spatio-temporal risk zones. We analysed performance of surveillance framework using ID data for year 2011-2015. Dynamic syndromic classifier is able to classify chief-complaints to appropriate syndromes with high classification accuracy. Outbreak detection methods are able to detect the ID outbreaks in start of epidemic time zones. Prediction model is able to forecast dengue trend for 20 weeks ahead with nominal normalized root mean square error of 0.29. Interactive geo-spatiotemporal displays, i.e. heat-maps, and choropleth are shown in respective sections. The proposed framework will set a standard and provide necessary details for future implementation of such a system for resource-constrained regions. It will improve early outbreak detection attributable to natural and man-made biological threats, monitor spatio-temporal epidemic trends and provide assurance that an outbreak has, or has not occurred. Advanced analytics features will be beneficial in timely organization/formulation of health management policies, disease control activities and efficient health care resource allocation. Copyright © 2016 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

A framework for inference about carnivore density from unstructured spatial sampling of scat using detector dogs

Treesearch

Craig M. Thompson; J. Andrew Royle; James D. Garner

2012-01-01

Wildlife management often hinges upon an accurate assessment of population density. Although undeniably useful, many of the traditional approaches to density estimation such as visual counts, livetrapping, or mark–recapture suffer from a suite of methodological and analytical weaknesses. Rare, secretive, or highly mobile species exacerbate these problems through the...

Medical and Healthcare Curriculum Exploratory Analysis.

PubMed

Komenda, Martin; Karolyi, Matěj; Pokorná, Andrea; Vaitsis, Christos

2017-01-01

In the recent years, medical and healthcare higher education institutions compile their curricula in different ways in order to cover all necessary topics and sections that the students will need to go through to success in their future clinical practice. A medical and healthcare curriculum consists of many descriptive parameters, which define statements of what, when, and how students will learn in the course of their studies. For the purpose of understanding a complicated medical and healthcare curriculum structure, we have developed a web-oriented platform for curriculum management covering in detail formal metadata specifications in accordance with the approved pedagogical background, namely outcome-based approach. Our platform provides a rich database that can be used for innovative detailed educational data analysis. In this contribution we would like to present how we used a proven process model as a way of increasing accuracy in solving individual analytical tasks with the available data. Moreover, we introduce an innovative approach on how to explore a dataset in accordance with the selected methodology. The achieved results from the selected analytical issues are presented here in clear visual interpretations in an attempt to visually describe the entire medical and healthcare curriculum.

Reducing the Analytical Bottleneck for Domain Scientists: Lessons from a Climate Data Visualization Case Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dasgupta, Aritra; Poco, Jorge; Bertini, Enrico

2016-01-01

The gap between large-scale data production rate and the rate of generation of data-driven scientific insights has led to an analytical bottleneck in scientific domains like climate, biology, etc. This is primarily due to the lack of innovative analytical tools that can help scientists efficiently analyze and explore alternative hypotheses about the data, and communicate their findings effectively to a broad audience. In this paper, by reflecting on a set of successful collaborative research efforts between with a group of climate scientists and visualization researchers, we introspect how interactive visualization can help reduce the analytical bottleneck for domain scientists.

Visual exploration of parameter influence on phylogenetic trees.

PubMed

Hess, Martin; Bremm, Sebastian; Weissgraeber, Stephanie; Hamacher, Kay; Goesele, Michael; Wiemeyer, Josef; von Landesberger, Tatiana

2014-01-01

Evolutionary relationships between organisms are frequently derived as phylogenetic trees inferred from multiple sequence alignments (MSAs). The MSA parameter space is exponentially large, so tens of thousands of potential trees can emerge for each dataset. A proposed visual-analytics approach can reveal the parameters' impact on the trees. Given input trees created with different parameter settings, it hierarchically clusters the trees according to their structural similarity. The most important clusters of similar trees are shown together with their parameters. This view offers interactive parameter exploration and automatic identification of relevant parameters. Biologists applied this approach to real data of 16S ribosomal RNA and protein sequences of ion channels. It revealed which parameters affected the tree structures. This led to a more reliable selection of the best trees.

Integrated remote sensing and visualization (IRSV) system for transportation infrastructure operations and management, phase one, volume 4 : use of knowledge integrated visual analytics system in supporting bridge management.

DOT National Transportation Integrated Search

2009-12-01

The goals of integration should be: Supporting domain oriented data analysis through the use of : knowledge augmented visual analytics system. In this project, we focus on: : Providing interactive data exploration for bridge managements. : ...

Human Factors in Streaming Data Analysis: Challenges and Opportunities for Information Visualization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dasgupta, Aritra; Arendt, Dustin L.; Franklin, Lyndsey

State-of-the-art visual analytics models and frameworks mostly assume a static snapshot of the data, while in many cases it is a stream with constant updates and changes. Exploration of streaming data poses unique challenges as machine-level computations and abstractions need to be synchronized with the visual representation of the data and the temporally evolving human insights. In the visual analytics literature, we lack a thorough characterization of streaming data and analysis of the challenges associated with task abstraction, visualization design, and adaptation of the role of human-in-the-loop for exploration of data streams. We aim to fill this gap by conductingmore » a survey of the state-of-the-art in visual analytics of streaming data for systematically describing the contributions and shortcomings of current techniques and analyzing the research gaps that need to be addressed in the future. Our contributions are: i) problem characterization for identifying challenges that are unique to streaming data analysis tasks, ii) a survey and analysis of the state-of-the-art in streaming data visualization research with a focus on the visualization design space for dynamic data and the role of the human-in-the-loop, and iii) reflections on the design-trade-offs for streaming visual analytics techniques and their practical applicability in real-world application scenarios.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kaurov, Alexander A., E-mail: kaurov@uchicago.edu

The methods for studying the epoch of cosmic reionization vary from full radiative transfer simulations to purely analytical models. While numerical approaches are computationally expensive and are not suitable for generating many mock catalogs, analytical methods are based on assumptions and approximations. We explore the interconnection between both methods. First, we ask how the analytical framework of excursion set formalism can be used for statistical analysis of numerical simulations and visual representation of the morphology of ionization fronts. Second, we explore the methods of training the analytical model on a given numerical simulation. We present a new code which emergedmore » from this study. Its main application is to match the analytical model with a numerical simulation. Then, it allows one to generate mock reionization catalogs with volumes exceeding the original simulation quickly and computationally inexpensively, meanwhile reproducing large-scale statistical properties. These mock catalogs are particularly useful for cosmic microwave background polarization and 21 cm experiments, where large volumes are required to simulate the observed signal.« less

Evaluation methodology for comparing memory and communication of analytic processes in visual analytics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ragan, Eric D; Goodall, John R

2014-01-01

Provenance tools can help capture and represent the history of analytic processes. In addition to supporting analytic performance, provenance tools can be used to support memory of the process and communication of the steps to others. Objective evaluation methods are needed to evaluate how well provenance tools support analyst s memory and communication of analytic processes. In this paper, we present several methods for the evaluation of process memory, and we discuss the advantages and limitations of each. We discuss methods for determining a baseline process for comparison, and we describe various methods that can be used to elicit processmore » recall, step ordering, and time estimations. Additionally, we discuss methods for conducting quantitative and qualitative analyses of process memory. By organizing possible memory evaluation methods and providing a meta-analysis of the potential benefits and drawbacks of different approaches, this paper can inform study design and encourage objective evaluation of process memory and communication.« less

Curating and Integrating Data from Multiple Sources to Support Healthcare Analytics.

PubMed

Ng, Kenney; Kakkanatt, Chris; Benigno, Michael; Thompson, Clay; Jackson, Margaret; Cahan, Amos; Zhu, Xinxin; Zhang, Ping; Huang, Paul

2015-01-01

As the volume and variety of healthcare related data continues to grow, the analysis and use of this data will increasingly depend on the ability to appropriately collect, curate and integrate disparate data from many different sources. We describe our approach to and highlight our experiences with the development of a robust data collection, curation and integration infrastructure that supports healthcare analytics. This system has been successfully applied to the processing of a variety of data types including clinical data from electronic health records and observational studies, genomic data, microbiomic data, self-reported data from surveys and self-tracked data from wearable devices from over 600 subjects. The curated data is currently being used to support healthcare analytic applications such as data visualization, patient stratification and predictive modeling.

The Role of Teamwork in the Analysis of Big Data: A Study of Visual Analytics and Box Office Prediction.

PubMed

Buchanan, Verica; Lu, Yafeng; McNeese, Nathan; Steptoe, Michael; Maciejewski, Ross; Cooke, Nancy

2017-03-01

Historically, domains such as business intelligence would require a single analyst to engage with data, develop a model, answer operational questions, and predict future behaviors. However, as the problems and domains become more complex, organizations are employing teams of analysts to explore and model data to generate knowledge. Furthermore, given the rapid increase in data collection, organizations are struggling to develop practices for intelligence analysis in the era of big data. Currently, a variety of machine learning and data mining techniques are available to model data and to generate insights and predictions, and developments in the field of visual analytics have focused on how to effectively link data mining algorithms with interactive visuals to enable analysts to explore, understand, and interact with data and data models. Although studies have explored the role of single analysts in the visual analytics pipeline, little work has explored the role of teamwork and visual analytics in the analysis of big data. In this article, we present an experiment integrating statistical models, visual analytics techniques, and user experiments to study the role of teamwork in predictive analytics. We frame our experiment around the analysis of social media data for box office prediction problems and compare the prediction performance of teams, groups, and individuals. Our results indicate that a team's performance is mediated by the team's characteristics such as openness of individual members to others' positions and the type of planning that goes into the team's analysis. These findings have important implications for how organizations should create teams in order to make effective use of information from their analytic models.

Using Interactive Data Visualizations for Exploratory Analysis in Undergraduate Genomics Coursework: Field Study Findings and Guidelines

ERIC Educational Resources Information Center

Mirel, Barbara; Kumar, Anuj; Nong, Paige; Su, Gang; Meng, Fan

2016-01-01

Life scientists increasingly use visual analytics to explore large data sets and generate hypotheses. Undergraduate biology majors should be learning these same methods. Yet visual analytics is one of the most underdeveloped areas of undergraduate biology education. This study sought to determine the feasibility of undergraduate biology majors…

Literature and Product Review of Visual Analytics for Maritime Awareness

DTIC Science & Technology

2009-10-28

the user’s knowledge and experience. • Riveiro et al [107] provide a useful discussion of the cognitive process of anomaly detection based on...changes over time can be seen visually. • Wilkinson et al [140] suggests that we need visual analytics for three principal purposes: checking raw data...Predictions within the Current Plot • Yue et al [146] describe an AI blackboard-based agent that leverages interactive visualization and mixed

Collaboration and Synergy among Government, Industry and Academia in M&S Domain: Turkey’s Approach

DTIC Science & Technology

2009-10-01

Analysis, Decision Support System Design and Implementation, Simulation Output Analysis, Statistical Data Analysis, Virtual Reality , Artificial... virtual and constructive visual simulation systems as well as integrated advanced analytical models. Collaboration and Synergy among Government...simulation systems that are ready to use, credible, integrated with C4ISR systems.  Creating synthetic environments and/or virtual prototypes of concepts

What We Can Learn from the Data: A Multiple-Case Study Examining Behavior Patterns by Students with Different Characteristics in Using a Serious Game

ERIC Educational Resources Information Center

Liu, Min; Lee, Jaejin; Kang, Jina; Liu, Sa

2016-01-01

Using a multi-case approach, we examined students' behavior patterns in interacting with a serious game environment using the emerging technologies of learning analytics and data visualization in order to understand how the patterns may vary according to students' learning characteristics. The results confirmed some preliminary findings from our…

Electrochromic Molecular Imprinting Sensor for Visual and Smartphone-Based Detections.

PubMed

Capoferri, Denise; Álvarez-Diduk, Ruslan; Del Carlo, Michele; Compagnone, Dario; Merkoçi, Arben

2018-05-01

Electrochromic effect and molecularly imprinted technology have been used to develop a sensitive and selective electrochromic sensor. The polymeric matrices obtained using the imprinting technology are robust molecular recognition elements and have the potential to mimic natural recognition entities with very high selectivity. The electrochromic behavior of iridium oxide nanoparticles (IrOx NPs) as physicochemical transducer together with a molecularly imprinted polymer (MIP) as recognition layer resulted in a fast and efficient translation of the detection event. The sensor was fabricated using screen-printing technology with indium tin oxide as a transparent working electrode; IrOx NPs where electrodeposited onto the electrode followed by thermal polymerization of polypyrrole in the presence of the analyte (chlorpyrifos). Two different approaches were used to detect and quantify the pesticide: direct visual detection and smartphone imaging. Application of different oxidation potentials for 10 s resulted in color changes directly related to the concentration of the analyte. For smartphone imaging, at fixed potential, the concentration of the analyte was dependent on the color intensity of the electrode. The electrochromic sensor detects a highly toxic compound (chlorpyrifos) with a 100 fM and 1 mM dynamic range. So far, to the best of our knowledge, this is the first work where an electrochromic MIP sensor uses the electrochromic properties of IrOx to detect a certain analyte with high selectivity and sensitivity.

Interactive exploration of surveillance video through action shot summarization and trajectory visualization.

PubMed

Meghdadi, Amir H; Irani, Pourang

2013-12-01

We propose a novel video visual analytics system for interactive exploration of surveillance video data. Our approach consists of providing analysts with various views of information related to moving objects in a video. To do this we first extract each object's movement path. We visualize each movement by (a) creating a single action shot image (a still image that coalesces multiple frames), (b) plotting its trajectory in a space-time cube and (c) displaying an overall timeline view of all the movements. The action shots provide a still view of the moving object while the path view presents movement properties such as speed and location. We also provide tools for spatial and temporal filtering based on regions of interest. This allows analysts to filter out large amounts of movement activities while the action shot representation summarizes the content of each movement. We incorporated this multi-part visual representation of moving objects in sViSIT, a tool to facilitate browsing through the video content by interactive querying and retrieval of data. Based on our interaction with security personnel who routinely interact with surveillance video data, we identified some of the most common tasks performed. This resulted in designing a user study to measure time-to-completion of the various tasks. These generally required searching for specific events of interest (targets) in videos. Fourteen different tasks were designed and a total of 120 min of surveillance video were recorded (indoor and outdoor locations recording movements of people and vehicles). The time-to-completion of these tasks were compared against a manual fast forward video browsing guided with movement detection. We demonstrate how our system can facilitate lengthy video exploration and significantly reduce browsing time to find events of interest. Reports from expert users identify positive aspects of our approach which we summarize in our recommendations for future video visual analytics systems.

Graphing trillions of triangles.

PubMed

Burkhardt, Paul

2017-07-01

The increasing size of Big Data is often heralded but how data are transformed and represented is also profoundly important to knowledge discovery, and this is exemplified in Big Graph analytics. Much attention has been placed on the scale of the input graph but the product of a graph algorithm can be many times larger than the input. This is true for many graph problems, such as listing all triangles in a graph. Enabling scalable graph exploration for Big Graphs requires new approaches to algorithms, architectures, and visual analytics. A brief tutorial is given to aid the argument for thoughtful representation of data in the context of graph analysis. Then a new algebraic method to reduce the arithmetic operations in counting and listing triangles in graphs is introduced. Additionally, a scalable triangle listing algorithm in the MapReduce model will be presented followed by a description of the experiments with that algorithm that led to the current largest and fastest triangle listing benchmarks to date. Finally, a method for identifying triangles in new visual graph exploration technologies is proposed.

GANViz: A Visual Analytics Approach to Understand the Adversarial Game.

PubMed

Wang, Junpeng; Gou, Liang; Yang, Hao; Shen, Han-Wei

2018-06-01

Generative models bear promising implications to learn data representations in an unsupervised fashion with deep learning. Generative Adversarial Nets (GAN) is one of the most popular frameworks in this arena. Despite the promising results from different types of GANs, in-depth understanding on the adversarial training process of the models remains a challenge to domain experts. The complexity and the potential long-time training process of the models make it hard to evaluate, interpret, and optimize them. In this work, guided by practical needs from domain experts, we design and develop a visual analytics system, GANViz, aiming to help experts understand the adversarial process of GANs in-depth. Specifically, GANViz evaluates the model performance of two subnetworks of GANs, provides evidence and interpretations of the models' performance, and empowers comparative analysis with the evidence. Through our case studies with two real-world datasets, we demonstrate that GANViz can provide useful insight into helping domain experts understand, interpret, evaluate, and potentially improve GAN models.

Analysis, simulation and visualization of 1D tapping via reduced dynamical models

NASA Astrophysics Data System (ADS)

Blackmore, Denis; Rosato, Anthony; Tricoche, Xavier; Urban, Kevin; Zou, Luo

2014-04-01

A low-dimensional center-of-mass dynamical model is devised as a simplified means of approximately predicting some important aspects of the motion of a vertical column comprised of a large number of particles subjected to gravity and periodic vertical tapping. This model is investigated first as a continuous dynamical system using analytical, simulation and visualization techniques. Then, by employing an approach analogous to that used to approximate the dynamics of a bouncing ball on an oscillating flat plate, it is modeled as a discrete dynamical system and analyzed to determine bifurcations and transitions to chaotic motion along with other properties. The predictions of the analysis are then compared-primarily qualitatively-with visualization and simulation results of the reduced continuous model, and ultimately with simulations of the complete system dynamics.

Patterns and Sequences: Interactive Exploration of Clickstreams to Understand Common Visitor Paths.

PubMed

Liu, Zhicheng; Wang, Yang; Dontcheva, Mira; Hoffman, Matthew; Walker, Seth; Wilson, Alan

2017-01-01

Modern web clickstream data consists of long, high-dimensional sequences of multivariate events, making it difficult to analyze. Following the overarching principle that the visual interface should provide information about the dataset at multiple levels of granularity and allow users to easily navigate across these levels, we identify four levels of granularity in clickstream analysis: patterns, segments, sequences and events. We present an analytic pipeline consisting of three stages: pattern mining, pattern pruning and coordinated exploration between patterns and sequences. Based on this approach, we discuss properties of maximal sequential patterns, propose methods to reduce the number of patterns and describe design considerations for visualizing the extracted sequential patterns and the corresponding raw sequences. We demonstrate the viability of our approach through an analysis scenario and discuss the strengths and limitations of the methods based on user feedback.

How can knowledge discovery methods uncover spatio-temporal patterns in environmental data?

NASA Astrophysics Data System (ADS)

Wachowicz, Monica

2000-04-01

This paper proposes the integration of KDD, GVis and STDB as a long-term strategy, which will allow users to apply knowledge discovery methods for uncovering spatio-temporal patterns in environmental data. The main goal is to combine innovative techniques and associated tools for exploring very large environmental data sets in order to arrive at valid, novel, potentially useful, and ultimately understandable spatio-temporal patterns. The GeoInsight approach is described using the principles and key developments in the research domains of KDD, GVis, and STDB. The GeoInsight approach aims at the integration of these research domains in order to provide tools for performing information retrieval, exploration, analysis, and visualization. The result is a knowledge-based design, which involves visual thinking (perceptual-cognitive process) and automated information processing (computer-analytical process).

Physiological and Anatomical Visual Analytics (PAVA) Background

EPA Pesticide Factsheets

The need to efficiently analyze human chemical disposition data from in vivo studies or in silico PBPK modeling efforts, and to see complex disposition data in a logical manner, has created a unique opportunity for visual analytics applid to PAD.

VAiRoma: A Visual Analytics System for Making Sense of Places, Times, and Events in Roman History.

PubMed

Cho, Isaac; Dou, Wewnen; Wang, Derek Xiaoyu; Sauda, Eric; Ribarsky, William

2016-01-01

Learning and gaining knowledge of Roman history is an area of interest for students and citizens at large. This is an example of a subject with great sweep (with many interrelated sub-topics over, in this case, a 3,000 year history) that is hard to grasp by any individual and, in its full detail, is not available as a coherent story. In this paper, we propose a visual analytics approach to construct a data driven view of Roman history based on a large collection of Wikipedia articles. Extracting and enabling the discovery of useful knowledge on events, places, times, and their connections from large amounts of textual data has always been a challenging task. To this aim, we introduce VAiRoma, a visual analytics system that couples state-of-the-art text analysis methods with an intuitive visual interface to help users make sense of events, places, times, and more importantly, the relationships between them. VAiRoma goes beyond textual content exploration, as it permits users to compare, make connections, and externalize the findings all within the visual interface. As a result, VAiRoma allows users to learn and create new knowledge regarding Roman history in an informed way. We evaluated VAiRoma with 16 participants through a user study, with the task being to learn about roman piazzas through finding relevant articles and new relationships. Our study results showed that the VAiRoma system enables the participants to find more relevant articles and connections compared to Web searches and literature search conducted in a roman library. Subjective feedback on VAiRoma was also very positive. In addition, we ran two case studies that demonstrate how VAiRoma can be used for deeper analysis, permitting the rapid discovery and analysis of a small number of key documents even when the original collection contains hundreds of thousands of documents.

Visualization and recommendation of large image collections toward effective sensemaking

NASA Astrophysics Data System (ADS)

Gu, Yi; Wang, Chaoli; Nemiroff, Robert; Kao, David; Parra, Denis

2016-03-01

In our daily lives, images are among the most commonly found data which we need to handle. We present iGraph, a graph-based approach for visual analytics of large image collections and their associated text information. Given such a collection, we compute the similarity between images, the distance between texts, and the connection between image and text to construct iGraph, a compound graph representation which encodes the underlying relationships among these images and texts. To enable effective visual navigation and comprehension of iGraph with tens of thousands of nodes and hundreds of millions of edges, we present a progressive solution that offers collection overview, node comparison, and visual recommendation. Our solution not only allows users to explore the entire collection with representative images and keywords but also supports detailed comparison for understanding and intuitive guidance for navigation. The visual exploration of iGraph is further enhanced with the implementation of bubble sets to highlight group memberships of nodes, suggestion of abnormal keywords or time periods based on text outlier detection, and comparison of four different recommendation solutions. For performance speedup, multiple graphics processing units and central processing units are utilized for processing and visualization in parallel. We experiment with two image collections and leverage a cluster driving a display wall of nearly 50 million pixels. We show the effectiveness of our approach by demonstrating experimental results and conducting a user study.

Just-in-time Time Data Analytics and Visualization of Climate Simulations using the Bellerophon Framework

NASA Astrophysics Data System (ADS)

Anantharaj, V. G.; Venzke, J.; Lingerfelt, E.; Messer, B.

2015-12-01

Climate model simulations are used to understand the evolution and variability of earth's climate. Unfortunately, high-resolution multi-decadal climate simulations can take days to weeks to complete. Typically, the simulation results are not analyzed until the model runs have ended. During the course of the simulation, the output may be processed periodically to ensure that the model is preforming as expected. However, most of the data analytics and visualization are not performed until the simulation is finished. The lengthy time period needed for the completion of the simulation constrains the productivity of climate scientists. Our implementation of near real-time data visualization analytics capabilities allows scientists to monitor the progress of their simulations while the model is running. Our analytics software executes concurrently in a co-scheduling mode, monitoring data production. When new data are generated by the simulation, a co-scheduled data analytics job is submitted to render visualization artifacts of the latest results. These visualization output are automatically transferred to Bellerophon's data server located at ORNL's Compute and Data Environment for Science (CADES) where they are processed and archived into Bellerophon's database. During the course of the experiment, climate scientists can then use Bellerophon's graphical user interface to view animated plots and their associated metadata. The quick turnaround from the start of the simulation until the data are analyzed permits research decisions and projections to be made days or sometimes even weeks sooner than otherwise possible! The supercomputer resources used to run the simulation are unaffected by co-scheduling the data visualization jobs, so the model runs continuously while the data are visualized. Our just-in-time data visualization software looks to increase climate scientists' productivity as climate modeling moves into exascale era of computing.

SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics

PubMed Central

Vartak, Manasi; Rahman, Sajjadur; Madden, Samuel; Parameswaran, Aditya; Polyzotis, Neoklis

2015-01-01

Data analysts often build visualizations as the first step in their analytical workflow. However, when working with high-dimensional datasets, identifying visualizations that show relevant or desired trends in data can be laborious. We propose SeeDB, a visualization recommendation engine to facilitate fast visual analysis: given a subset of data to be studied, SeeDB intelligently explores the space of visualizations, evaluates promising visualizations for trends, and recommends those it deems most “useful” or “interesting”. The two major obstacles in recommending interesting visualizations are (a) scale: evaluating a large number of candidate visualizations while responding within interactive time scales, and (b) utility: identifying an appropriate metric for assessing interestingness of visualizations. For the former, SeeDB introduces pruning optimizations to quickly identify high-utility visualizations and sharing optimizations to maximize sharing of computation across visualizations. For the latter, as a first step, we adopt a deviation-based metric for visualization utility, while indicating how we may be able to generalize it to other factors influencing utility. We implement SeeDB as a middleware layer that can run on top of any DBMS. Our experiments show that our framework can identify interesting visualizations with high accuracy. Our optimizations lead to multiple orders of magnitude speedup on relational row and column stores and provide recommendations at interactive time scales. Finally, we demonstrate via a user study the effectiveness of our deviation-based utility metric and the value of recommendations in supporting visual analytics. PMID:26779379

SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics.

PubMed

Vartak, Manasi; Rahman, Sajjadur; Madden, Samuel; Parameswaran, Aditya; Polyzotis, Neoklis

2015-09-01

Data analysts often build visualizations as the first step in their analytical workflow. However, when working with high-dimensional datasets, identifying visualizations that show relevant or desired trends in data can be laborious. We propose SeeDB, a visualization recommendation engine to facilitate fast visual analysis: given a subset of data to be studied, SeeDB intelligently explores the space of visualizations, evaluates promising visualizations for trends, and recommends those it deems most "useful" or "interesting". The two major obstacles in recommending interesting visualizations are (a) scale : evaluating a large number of candidate visualizations while responding within interactive time scales, and (b) utility : identifying an appropriate metric for assessing interestingness of visualizations. For the former, SeeDB introduces pruning optimizations to quickly identify high-utility visualizations and sharing optimizations to maximize sharing of computation across visualizations. For the latter, as a first step, we adopt a deviation-based metric for visualization utility, while indicating how we may be able to generalize it to other factors influencing utility. We implement SeeDB as a middleware layer that can run on top of any DBMS. Our experiments show that our framework can identify interesting visualizations with high accuracy. Our optimizations lead to multiple orders of magnitude speedup on relational row and column stores and provide recommendations at interactive time scales. Finally, we demonstrate via a user study the effectiveness of our deviation-based utility metric and the value of recommendations in supporting visual analytics.

Effects of Using Dynamic Mathematics Software on Preservice Mathematics Teachers' Spatial Visualization Skills: The Case of Spatial Analytic Geometry

ERIC Educational Resources Information Center

Kösa, Temel

2016-01-01

The purpose of this study was to investigate the effects of using dynamic geometry software on preservice mathematics teachers' spatial visualization skills and to determine whether spatial visualization skills can be a predictor of success in learning analytic geometry of space. The study used a quasi-experimental design with a control group.…

Visual Information for the Desktop, version 1.0

DOE Office of Scientific and Technical Information (OSTI.GOV)

2006-03-29

VZIN integrates visual analytics capabilities into popular desktop tools to aid a user in searching and understanding an information space. VZIN allows users to Drag-Drop-Visualize-Explore-Organize information within tools such as Microsoft Office, Windows Explorer, Excel, and Outlook. VZIN is tailorable to specific client or industry requirements. VZIN follows the desktop metaphors so that advanced analytical capabilities are available with minimal user training.

Spectral Unmixing Applied to Desert Soils for the Detection of Sub-Pixel Disturbances

DTIC Science & Technology

2012-09-01

and Glazner, 1997). Rocks underlying Panum Crater consist of the granitic and metamorphic batholith associated with the Sierra Nevada. On top of this...of Management and Budget, Paperwork Reduction Project (0704-0188) Washington DC 20503. 1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE September...technology can be used to detect and characterize surface disturbance both literally (visually) and non-literally (analytically). Non-literal approaches

Map LineUps: Effects of spatial structure on graphical inference.

PubMed

Beecham, Roger; Dykes, Jason; Meulemans, Wouter; Slingsby, Aidan; Turkay, Cagatay; Wood, Jo

2017-01-01

Fundamental to the effective use of visualization as an analytic and descriptive tool is the assurance that presenting data visually provides the capability of making inferences from what we see. This paper explores two related approaches to quantifying the confidence we may have in making visual inferences from mapped geospatial data. We adapt Wickham et al.'s 'Visual Line-up' method as a direct analogy with Null Hypothesis Significance Testing (NHST) and propose a new approach for generating more credible spatial null hypotheses. Rather than using as a spatial null hypothesis the unrealistic assumption of complete spatial randomness, we propose spatially autocorrelated simulations as alternative nulls. We conduct a set of crowdsourced experiments (n=361) to determine the just noticeable difference (JND) between pairs of choropleth maps of geographic units controlling for spatial autocorrelation (Moran's I statistic) and geometric configuration (variance in spatial unit area). Results indicate that people's abilities to perceive differences in spatial autocorrelation vary with baseline autocorrelation structure and the geometric configuration of geographic units. These results allow us, for the first time, to construct a visual equivalent of statistical power for geospatial data. Our JND results add to those provided in recent years by Klippel et al. (2011), Harrison et al. (2014) and Kay & Heer (2015) for correlation visualization. Importantly, they provide an empirical basis for an improved construction of visual line-ups for maps and the development of theory to inform geospatial tests of graphical inference.

General Analytical Schemes for the Characterization of Pectin-Based Edible Gelled Systems

PubMed Central

Haghighi, Maryam; Rezaei, Karamatollah

2012-01-01

Pectin-based gelled systems have gained increasing attention for the design of newly developed food products. For this reason, the characterization of such formulas is a necessity in order to present scientific data and to introduce an appropriate finished product to the industry. Various analytical techniques are available for the evaluation of the systems formulated on the basis of pectin and the designed gel. In this paper, general analytical approaches for the characterization of pectin-based gelled systems were categorized into several subsections including physicochemical analysis, visual observation, textural/rheological measurement, microstructural image characterization, and psychorheological evaluation. Three-dimensional trials to assess correlations among microstructure, texture, and taste were also discussed. Practical examples of advanced objective techniques including experimental setups for small and large deformation rheological measurements and microstructural image analysis were presented in more details. PMID:22645484

Empirical Analysis of the Subjective Impressions and Objective Measures of Domain Scientists’ Visual Analytic Judgments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dasgupta, Aritra; Burrows, Susannah M.; Han, Kyungsik

2017-05-08

Scientists often use specific data analysis and presentation methods familiar within their domain. But does high familiarity drive better analytical judgment? This question is especially relevant when familiar methods themselves can have shortcomings: many visualizations used conventionally for scientific data analysis and presentation do not follow established best practices. This necessitates new methods that might be unfamiliar yet prove to be more effective. But there is little empirical understanding of the relationships between scientists’ subjective impressions about familiar and unfamiliar visualizations and objective measures of their visual analytic judgments. To address this gap and to study these factors, we focusmore » on visualizations used for comparison of climate model performance. We report on a comprehensive survey-based user study with 47 climate scientists and present an analysis of : i) relationships among scientists’ familiarity, their perceived lev- els of comfort, confidence, accuracy, and objective measures of accuracy, and ii) relationships among domain experience, visualization familiarity, and post-study preference.« less

WetDATA Hub: Democratizing Access to Water Data to Accelerate Innovation through Data Visualization, Predictive Analytics and Artificial Intelligence Applications

NASA Astrophysics Data System (ADS)

Sarni, W.

2017-12-01

Water scarcity and poor quality impacts economic development, business growth, and social well-being. Water has become, in our generation, the foremost critical local, regional, and global issue of our time. Despite these needs, there is no water hub or water technology accelerator solely dedicated to water data and tools. There is a need by the public and private sectors for vastly improved data management and visualization tools. This is the WetDATA opportunity - to develop a water data tech hub dedicated to water data acquisition, analytics, and visualization tools for informed policy and business decisions. WetDATA's tools will help incubate disruptive water data technologies and accelerate adoption of current water data solutions. WetDATA is a Colorado-based (501c3), global hub for water data analytics and technology innovation. WetDATA's vision is to be a global leader in water information, data technology innovation and collaborate with other US and global water technology hubs. ROADMAP * Portal (www.wetdata.org) to provide stakeholders with tools/resources to understand related water risks. * The initial activities will provide education, awareness and tools to stakeholders to support the implementation of the Colorado State Water Plan. * Leverage the Western States Water Council Water Data Exchange database. * Development of visualization, predictive analytics and AI tools to engage with stakeholders and provide actionable data and information. TOOLS Education: Provide information on water issues and risks at the local, state, national and global scale. Visualizations: Development of data analytics and visualization tools based upon the 2030 Water Resources Group methodology to support the implementation of the Colorado State Water Plan. Predictive Analytics: Accessing publically available water databases and using machine learning to develop water availability forecasting tools, and time lapse images to support city / urban planning.

Visual analysis of online social media to open up the investigation of stance phenomena

PubMed Central

Kucher, Kostiantyn; Schamp-Bjerede, Teri; Kerren, Andreas; Paradis, Carita; Sahlgren, Magnus

2015-01-01

Online social media are a perfect text source for stance analysis. Stance in human communication is concerned with speaker attitudes, beliefs, feelings and opinions. Expressions of stance are associated with the speakers' view of what they are talking about and what is up for discussion and negotiation in the intersubjective exchange. Taking stance is thus crucial for the social construction of meaning. Increased knowledge of stance can be useful for many application fields such as business intelligence, security analytics, or social media monitoring. In order to process large amounts of text data for stance analyses, linguists need interactive tools to explore the textual sources as well as the processed data based on computational linguistics techniques. Both original texts and derived data are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data that can be used to open up the investigation of stance phenomena. Our approach complements traditional linguistic analysis techniques and is based on the analysis of utterances associated with two stance categories: sentiment and certainty. Our contributions include (1) the description of a novel web-based solution for analyzing the use and patterns of stance meanings and expressions in human communication over time; and (2) specialized techniques used for visualizing analysis provenance and corpus overview/navigation. We demonstrate our approach by means of text media on a highly controversial scandal with regard to expressions of anger and provide an expert review from linguists who have been using our tool. PMID:29249903

Visual analysis of online social media to open up the investigation of stance phenomena.

PubMed

Kucher, Kostiantyn; Schamp-Bjerede, Teri; Kerren, Andreas; Paradis, Carita; Sahlgren, Magnus

2016-04-01

Online social media are a perfect text source for stance analysis. Stance in human communication is concerned with speaker attitudes, beliefs, feelings and opinions. Expressions of stance are associated with the speakers' view of what they are talking about and what is up for discussion and negotiation in the intersubjective exchange. Taking stance is thus crucial for the social construction of meaning. Increased knowledge of stance can be useful for many application fields such as business intelligence, security analytics, or social media monitoring. In order to process large amounts of text data for stance analyses, linguists need interactive tools to explore the textual sources as well as the processed data based on computational linguistics techniques. Both original texts and derived data are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data that can be used to open up the investigation of stance phenomena. Our approach complements traditional linguistic analysis techniques and is based on the analysis of utterances associated with two stance categories: sentiment and certainty. Our contributions include (1) the description of a novel web-based solution for analyzing the use and patterns of stance meanings and expressions in human communication over time; and (2) specialized techniques used for visualizing analysis provenance and corpus overview/navigation. We demonstrate our approach by means of text media on a highly controversial scandal with regard to expressions of anger and provide an expert review from linguists who have been using our tool.

Visualization and Analytics Software Tools for Peregrine System |

Science.gov Websites

R is a language and environment for statistical computing and graphics. Go to the R web site for System Visualization and Analytics Software Tools for Peregrine System Learn about the available visualization for OpenGL-based applications. For more information, please go to the FastX page. ParaView An open

Simple and Sensitive Paper-Based Device Coupling Electrochemical Sample Pretreatment and Colorimetric Detection.

PubMed

Silva, Thalita G; de Araujo, William R; Muñoz, Rodrigo A A; Richter, Eduardo M; Santana, Mário H P; Coltro, Wendell K T; Paixão, Thiago R L C

2016-05-17

We report the development of a simple, portable, low-cost, high-throughput visual colorimetric paper-based analytical device for the detection of procaine in seized cocaine samples. The interference of most common cutting agents found in cocaine samples was verified, and a novel electrochemical approach was used for sample pretreatment in order to increase the selectivity. Under the optimized experimental conditions, a linear analytical curve was obtained for procaine concentrations ranging from 5 to 60 μmol L(-1), with a detection limit of 0.9 μmol L(-1). The accuracy of the proposed method was evaluated using seized cocaine samples and an addition and recovery protocol.

Visual Analytics for MOOC Data.

PubMed

Qu, Huamin; Chen, Qing

2015-01-01

With the rise of massive open online courses (MOOCs), tens of millions of learners can now enroll in more than 1,000 courses via MOOC platforms such as Coursera and edX. As a result, a huge amount of data has been collected. Compared with traditional education records, the data from MOOCs has much finer granularity and also contains new pieces of information. It is the first time in history that such comprehensive data related to learning behavior has become available for analysis. What roles can visual analytics play in this MOOC movement? The authors survey the current practice and argue that MOOCs provide an opportunity for visualization researchers and that visual analytics systems for MOOCs can benefit a range of end users such as course instructors, education researchers, students, university administrators, and MOOC providers.

A geovisual analytic approach to understanding geo-social relationships in the international trade network.

PubMed

Luo, Wei; Yin, Peifeng; Di, Qian; Hardisty, Frank; MacEachren, Alan M

2014-01-01

The world has become a complex set of geo-social systems interconnected by networks, including transportation networks, telecommunications, and the internet. Understanding the interactions between spatial and social relationships within such geo-social systems is a challenge. This research aims to address this challenge through the framework of geovisual analytics. We present the GeoSocialApp which implements traditional network analysis methods in the context of explicitly spatial and social representations. We then apply it to an exploration of international trade networks in terms of the complex interactions between spatial and social relationships. This exploration using the GeoSocialApp helps us develop a two-part hypothesis: international trade network clusters with structural equivalence are strongly 'balkanized' (fragmented) according to the geography of trading partners, and the geographical distance weighted by population within each network cluster has a positive relationship with the development level of countries. In addition to demonstrating the potential of visual analytics to provide insight concerning complex geo-social relationships at a global scale, the research also addresses the challenge of validating insights derived through interactive geovisual analytics. We develop two indicators to quantify the observed patterns, and then use a Monte-Carlo approach to support the hypothesis developed above.

A Geovisual Analytic Approach to Understanding Geo-Social Relationships in the International Trade Network

PubMed Central

Luo, Wei; Yin, Peifeng; Di, Qian; Hardisty, Frank; MacEachren, Alan M.

2014-01-01

The world has become a complex set of geo-social systems interconnected by networks, including transportation networks, telecommunications, and the internet. Understanding the interactions between spatial and social relationships within such geo-social systems is a challenge. This research aims to address this challenge through the framework of geovisual analytics. We present the GeoSocialApp which implements traditional network analysis methods in the context of explicitly spatial and social representations. We then apply it to an exploration of international trade networks in terms of the complex interactions between spatial and social relationships. This exploration using the GeoSocialApp helps us develop a two-part hypothesis: international trade network clusters with structural equivalence are strongly ‘balkanized’ (fragmented) according to the geography of trading partners, and the geographical distance weighted by population within each network cluster has a positive relationship with the development level of countries. In addition to demonstrating the potential of visual analytics to provide insight concerning complex geo-social relationships at a global scale, the research also addresses the challenge of validating insights derived through interactive geovisual analytics. We develop two indicators to quantify the observed patterns, and then use a Monte-Carlo approach to support the hypothesis developed above. PMID:24558409

Bibliometric mapping: eight decades of analytical chemistry, with special focus on the use of mass spectrometry.

PubMed

Waaijer, Cathelijn J F; Palmblad, Magnus

2015-01-01

In this Feature we use automatic bibliometric mapping tools to visualize the history of analytical chemistry from the 1920s until the present. In particular, we have focused on the application of mass spectrometry in different fields. The analysis shows major shifts in research focus and use of mass spectrometry. We conclude by discussing the application of bibliometric mapping and visualization tools in analytical chemists' research.

Collaborative visual analytics of radio surveys in the Big Data era

NASA Astrophysics Data System (ADS)

Vohl, Dany; Fluke, Christopher J.; Hassan, Amr H.; Barnes, David G.; Kilborn, Virginia A.

2017-06-01

Radio survey datasets comprise an increasing number of individual observations stored as sets of multidimensional data. In large survey projects, astronomers commonly face limitations regarding: 1) interactive visual analytics of sufficiently large subsets of data; 2) synchronous and asynchronous collaboration; and 3) documentation of the discovery workflow. To support collaborative data inquiry, we present encube, a large-scale comparative visual analytics framework. encube can utilise advanced visualization environments such as the CAVE2 (a hybrid 2D and 3D virtual reality environment powered with a 100 Tflop/s GPU-based supercomputer and 84 million pixels) for collaborative analysis of large subsets of data from radio surveys. It can also run on standard desktops, providing a capable visual analytics experience across the display ecology. encube is composed of four primary units enabling compute-intensive processing, advanced visualisation, dynamic interaction, parallel data query, along with data management. Its modularity will make it simple to incorporate astronomical analysis packages and Virtual Observatory capabilities developed within our community. We discuss how encube builds a bridge between high-end display systems (such as CAVE2) and the classical desktop, preserving all traces of the work completed on either platform - allowing the research process to continue wherever you are.

Many-objective reservoir policy identification and refinement to reduce policy inertia and myopia in water management

NASA Astrophysics Data System (ADS)

Giuliani, M.; Herman, J. D.; Castelletti, A.; Reed, P.

2014-04-01

This study contributes a decision analytic framework to overcome policy inertia and myopia in complex river basin management contexts. The framework combines reservoir policy identification, many-objective optimization under uncertainty, and visual analytics to characterize current operations and discover key trade-offs between alternative policies for balancing competing demands and system uncertainties. The approach is demonstrated on the Conowingo Dam, located within the Lower Susquehanna River, USA. The Lower Susquehanna River is an interstate water body that has been subject to intensive water management efforts due to competing demands from urban water supply, atomic power plant cooling, hydropower production, and federally regulated environmental flows. We have identified a baseline operating policy for the Conowingo Dam that closely reproduces the dynamics of current releases and flows for the Lower Susquehanna and thus can be used to represent the preferences structure guiding current operations. Starting from this baseline policy, our proposed decision analytic framework then combines evolutionary many-objective optimization with visual analytics to discover new operating policies that better balance the trade-offs within the Lower Susquehanna. Our results confirm that the baseline operating policy, which only considers deterministic historical inflows, significantly overestimates the system's reliability in meeting the reservoir's competing demands. Our proposed framework removes this bias by successfully identifying alternative reservoir policies that are more robust to hydroclimatic uncertainties while also better addressing the trade-offs across the Conowingo Dam's multisector services.

Geo-Sandbox: An Interactive Geoscience Training Tool with Analytics to Better Understand Student Problem Solving Approaches

NASA Astrophysics Data System (ADS)

Butt, N.; Pidlisecky, A.; Ganshorn, H.; Cockett, R.

2015-12-01

The software company 3 Point Science has developed three interactive learning programs designed to teach, test and practice visualization skills and geoscience concepts. A study was conducted with 21 geoscience students at the University of Calgary who participated in 2 hour sessions of software interaction and written pre and post-tests. Computer and SMART touch table interfaces were used to analyze user interaction, problem solving methods and visualization skills. By understanding and pinpointing user problem solving methods it is possible to reconstruct viewpoints and thought processes. This could allow us to give personalized feedback in real time, informing the user of problem solving tips and possible misconceptions.

Forecasting hotspots using predictive visual analytics approach

DOEpatents

Maciejewski, Ross; Hafen, Ryan; Rudolph, Stephen; Cleveland, William; Ebert, David

2014-12-30

A method for forecasting hotspots is provided. The method may include the steps of receiving input data at an input of the computational device, generating a temporal prediction based on the input data, generating a geospatial prediction based on the input data, and generating output data based on the time series and geospatial predictions. The output data may be configured to display at least one user interface at an output of the computational device.

Streaming Visual Analytics Workshop Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cook, Kristin A.; Burtner, Edwin R.; Kritzstein, Brian P.

How can we best enable users to understand complex emerging events and make appropriate assessments from streaming data? This was the central question addressed at a three-day workshop on streaming visual analytics. This workshop was organized by Pacific Northwest National Laboratory for a government sponsor. It brought together forty researchers and subject matter experts from government, industry, and academia. This report summarizes the outcomes from that workshop. It describes elements of the vision for a streaming visual analytic environment and set of important research directions needed to achieve this vision. Streaming data analysis is in many ways the analysis andmore » understanding of change. However, current visual analytics systems usually focus on static data collections, meaning that dynamically changing conditions are not appropriately addressed. The envisioned mixed-initiative streaming visual analytics environment creates a collaboration between the analyst and the system to support the analysis process. It raises the level of discourse from low-level data records to higher-level concepts. The system supports the analyst’s rapid orientation and reorientation as situations change. It provides an environment to support the analyst’s critical thinking. It infers tasks and interests based on the analyst’s interactions. The system works as both an assistant and a devil’s advocate, finding relevant data and alerts as well as considering alternative hypotheses. Finally, the system supports sharing of findings with others. Making such an environment a reality requires research in several areas. The workshop discussions focused on four broad areas: support for critical thinking, visual representation of change, mixed-initiative analysis, and the use of narratives for analysis and communication.« less

A workflow learning model to improve geovisual analytics utility

PubMed Central

Roth, Robert E; MacEachren, Alan M; McCabe, Craig A

2011-01-01

Introduction This paper describes the design and implementation of the G-EX Portal Learn Module, a web-based, geocollaborative application for organizing and distributing digital learning artifacts. G-EX falls into the broader context of geovisual analytics, a new research area with the goal of supporting visually-mediated reasoning about large, multivariate, spatiotemporal information. Because this information is unprecedented in amount and complexity, GIScientists are tasked with the development of new tools and techniques to make sense of it. Our research addresses the challenge of implementing these geovisual analytics tools and techniques in a useful manner. Objectives The objective of this paper is to develop and implement a method for improving the utility of geovisual analytics software. The success of software is measured by its usability (i.e., how easy the software is to use?) and utility (i.e., how useful the software is). The usability and utility of software can be improved by refining the software, increasing user knowledge about the software, or both. It is difficult to achieve transparent usability (i.e., software that is immediately usable without training) of geovisual analytics software because of the inherent complexity of the included tools and techniques. In these situations, improving user knowledge about the software through the provision of learning artifacts is as important, if not more so, than iterative refinement of the software itself. Therefore, our approach to improving utility is focused on educating the user. Methodology The research reported here was completed in two steps. First, we developed a model for learning about geovisual analytics software. Many existing digital learning models assist only with use of the software to complete a specific task and provide limited assistance with its actual application. To move beyond task-oriented learning about software use, we propose a process-oriented approach to learning based on the concept of scientific workflows. Second, we implemented an interface in the G-EX Portal Learn Module to demonstrate the workflow learning model. The workflow interface allows users to drag learning artifacts uploaded to the G-EX Portal onto a central whiteboard and then annotate the workflow using text and drawing tools. Once completed, users can visit the assembled workflow to get an idea of the kind, number, and scale of analysis steps, view individual learning artifacts associated with each node in the workflow, and ask questions about the overall workflow or individual learning artifacts through the associated forums. An example learning workflow in the domain of epidemiology is provided to demonstrate the effectiveness of the approach. Results/Conclusions In the context of geovisual analytics, GIScientists are not only responsible for developing software to facilitate visually-mediated reasoning about large and complex spatiotemporal information, but also for ensuring that this software works. The workflow learning model discussed in this paper and demonstrated in the G-EX Portal Learn Module is one approach to improving the utility of geovisual analytics software. While development of the G-EX Portal Learn Module is ongoing, we expect to release the G-EX Portal Learn Module by Summer 2009. PMID:21983545

A workflow learning model to improve geovisual analytics utility.

PubMed

Roth, Robert E; Maceachren, Alan M; McCabe, Craig A

2009-01-01

INTRODUCTION: This paper describes the design and implementation of the G-EX Portal Learn Module, a web-based, geocollaborative application for organizing and distributing digital learning artifacts. G-EX falls into the broader context of geovisual analytics, a new research area with the goal of supporting visually-mediated reasoning about large, multivariate, spatiotemporal information. Because this information is unprecedented in amount and complexity, GIScientists are tasked with the development of new tools and techniques to make sense of it. Our research addresses the challenge of implementing these geovisual analytics tools and techniques in a useful manner. OBJECTIVES: The objective of this paper is to develop and implement a method for improving the utility of geovisual analytics software. The success of software is measured by its usability (i.e., how easy the software is to use?) and utility (i.e., how useful the software is). The usability and utility of software can be improved by refining the software, increasing user knowledge about the software, or both. It is difficult to achieve transparent usability (i.e., software that is immediately usable without training) of geovisual analytics software because of the inherent complexity of the included tools and techniques. In these situations, improving user knowledge about the software through the provision of learning artifacts is as important, if not more so, than iterative refinement of the software itself. Therefore, our approach to improving utility is focused on educating the user. METHODOLOGY: The research reported here was completed in two steps. First, we developed a model for learning about geovisual analytics software. Many existing digital learning models assist only with use of the software to complete a specific task and provide limited assistance with its actual application. To move beyond task-oriented learning about software use, we propose a process-oriented approach to learning based on the concept of scientific workflows. Second, we implemented an interface in the G-EX Portal Learn Module to demonstrate the workflow learning model. The workflow interface allows users to drag learning artifacts uploaded to the G-EX Portal onto a central whiteboard and then annotate the workflow using text and drawing tools. Once completed, users can visit the assembled workflow to get an idea of the kind, number, and scale of analysis steps, view individual learning artifacts associated with each node in the workflow, and ask questions about the overall workflow or individual learning artifacts through the associated forums. An example learning workflow in the domain of epidemiology is provided to demonstrate the effectiveness of the approach. RESULTS/CONCLUSIONS: In the context of geovisual analytics, GIScientists are not only responsible for developing software to facilitate visually-mediated reasoning about large and complex spatiotemporal information, but also for ensuring that this software works. The workflow learning model discussed in this paper and demonstrated in the G-EX Portal Learn Module is one approach to improving the utility of geovisual analytics software. While development of the G-EX Portal Learn Module is ongoing, we expect to release the G-EX Portal Learn Module by Summer 2009.

Composite glycerol/graphite/aromatic acid matrices for thin-layer chromatography/matrix-assisted laser desorption/ionization mass spectrometry of heterocyclic compounds.

PubMed

Esparza, Cesar; Borisov, R S; Varlamov, A V; Zaikin, V G

2016-10-28

New composite matrices have been suggested for the analysis of mixtures of different synthetic organic compounds (N-containing heterocycles and erectile dysfunction drugs) by thin layer chromatography/matrix-assisted laser desorption ionization time-of-flight mass spectrometry (TLC/MALDI-TOF). Different mixtures of classical MALDI matrices and graphite particles dispersed in glycerol were used for the registration of MALDI mass spectra directly from TLC plates after analytes separation. In most of cases, the mass spectra possessed [M+H] + ions; however, for some analytes only [M+Na] + and [M+K] + ions were observed. These ions have been used to generate visualized TLC chromatograms. The described approach increases the desorption/ionization efficiencies of analytes separated by TLC, prevent spot blurring, simplifies and decrease time for sample preparation. Copyright © 2016 Elsevier B.V. All rights reserved.

Study of a vibrating plate: comparison between experimental (ESPI) and analytical results

NASA Astrophysics Data System (ADS)

Romero, G.; Alvarez, L.; Alanís, E.; Nallim, L.; Grossi, R.

2003-07-01

Real-time electronic speckle pattern interferometry (ESPI) was used for tuning and visualization of natural frequencies of a trapezoidal plate. The plate was excited to resonant vibration by a sinusoidal acoustical source, which provided a continuous range of audio frequencies. Fringe patterns produced during the time-average recording of the vibrating plate—corresponding to several resonant frequencies—were registered. From these interferograms, calculations of vibrational amplitudes by means of zero-order Bessel functions were performed in some particular cases. The system was also studied analytically. The analytical approach developed is based on the Rayleigh-Ritz method and on the use of non-orthogonal right triangular co-ordinates. The deflection of the plate is approximated by a set of beam characteristic orthogonal polynomials generated by using the Gram-Schmidt procedure. A high degree of correlation between computational analysis and experimental results was observed.

Big data and visual analytics in anaesthesia and health care.

PubMed

Simpao, A F; Ahumada, L M; Rehman, M A

2015-09-01

Advances in computer technology, patient monitoring systems, and electronic health record systems have enabled rapid accumulation of patient data in electronic form (i.e. big data). Organizations such as the Anesthesia Quality Institute and Multicenter Perioperative Outcomes Group have spearheaded large-scale efforts to collect anaesthesia big data for outcomes research and quality improvement. Analytics--the systematic use of data combined with quantitative and qualitative analysis to make decisions--can be applied to big data for quality and performance improvements, such as predictive risk assessment, clinical decision support, and resource management. Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces, and it can facilitate performance of cognitive activities involving big data. Ongoing integration of big data and analytics within anaesthesia and health care will increase demand for anaesthesia professionals who are well versed in both the medical and the information sciences. © The Author 2015. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Rhodobase, a meta-analytical tool for reconstructing gene regulatory networks in a model photosynthetic bacterium.

PubMed

Moskvin, Oleg V; Bolotin, Dmitry; Wang, Andrew; Ivanov, Pavel S; Gomelsky, Mark

2011-02-01

We present Rhodobase, a web-based meta-analytical tool for analysis of transcriptional regulation in a model anoxygenic photosynthetic bacterium, Rhodobacter sphaeroides. The gene association meta-analysis is based on the pooled data from 100 of R. sphaeroides whole-genome DNA microarrays. Gene-centric regulatory networks were visualized using the StarNet approach (Jupiter, D.C., VanBuren, V., 2008. A visual data mining tool that facilitates reconstruction of transcription regulatory networks. PLoS ONE 3, e1717) with several modifications. We developed a means to identify and visualize operons and superoperons. We designed a framework for the cross-genome search for transcription factor binding sites that takes into account high GC-content and oligonucleotide usage profile characteristic of the R. sphaeroides genome. To facilitate reconstruction of directional relationships between co-regulated genes, we screened upstream sequences (-400 to +20bp from start codons) of all genes for putative binding sites of bacterial transcription factors using a self-optimizing search method developed here. To test performance of the meta-analysis tools and transcription factor site predictions, we reconstructed selected nodes of the R. sphaeroides transcription factor-centric regulatory matrix. The test revealed regulatory relationships that correlate well with the experimentally derived data. The database of transcriptional profile correlations, the network visualization engine and the optimized search engine for transcription factor binding sites analysis are available at http://rhodobase.org. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

SOCRAT Platform Design: A Web Architecture for Interactive Visual Analytics Applications

PubMed Central

Kalinin, Alexandr A.; Palanimalai, Selvam; Dinov, Ivo D.

2018-01-01

The modern web is a successful platform for large scale interactive web applications, including visualizations. However, there are no established design principles for building complex visual analytics (VA) web applications that could efficiently integrate visualizations with data management, computational transformation, hypothesis testing, and knowledge discovery. This imposes a time-consuming design and development process on many researchers and developers. To address these challenges, we consider the design requirements for the development of a module-based VA system architecture, adopting existing practices of large scale web application development. We present the preliminary design and implementation of an open-source platform for Statistics Online Computational Resource Analytical Toolbox (SOCRAT). This platform defines: (1) a specification for an architecture for building VA applications with multi-level modularity, and (2) methods for optimizing module interaction, re-usage, and extension. To demonstrate how this platform can be used to integrate a number of data management, interactive visualization, and analysis tools, we implement an example application for simple VA tasks including raw data input and representation, interactive visualization and analysis. PMID:29630069

SOCRAT Platform Design: A Web Architecture for Interactive Visual Analytics Applications.

PubMed

Kalinin, Alexandr A; Palanimalai, Selvam; Dinov, Ivo D

2017-04-01

The modern web is a successful platform for large scale interactive web applications, including visualizations. However, there are no established design principles for building complex visual analytics (VA) web applications that could efficiently integrate visualizations with data management, computational transformation, hypothesis testing, and knowledge discovery. This imposes a time-consuming design and development process on many researchers and developers. To address these challenges, we consider the design requirements for the development of a module-based VA system architecture, adopting existing practices of large scale web application development. We present the preliminary design and implementation of an open-source platform for Statistics Online Computational Resource Analytical Toolbox (SOCRAT). This platform defines: (1) a specification for an architecture for building VA applications with multi-level modularity, and (2) methods for optimizing module interaction, re-usage, and extension. To demonstrate how this platform can be used to integrate a number of data management, interactive visualization, and analysis tools, we implement an example application for simple VA tasks including raw data input and representation, interactive visualization and analysis.

Using geovisual analytics in Google Earth to understand disease distribution: a case study of campylobacteriosis in the Czech Republic (2008-2012).

PubMed

Marek, Lukáš; Tuček, Pavel; Pászto, Vít

2015-01-28

Visual analytics aims to connect the processing power of information technologies and the user's ability of logical thinking and reasoning through the complex visual interaction. Moreover, the most of the data contain the spatial component. Therefore, the need for geovisual tools and methods arises. Either one can develop own system but the dissemination of findings and its usability might be problematic or the widespread and well-known platform can be utilized. The aim of this paper is to prove the applicability of Google Earth™ software as a tool for geovisual analytics that helps to understand the spatio-temporal patterns of the disease distribution. We combined the complex joint spatio-temporal analysis with comprehensive visualisation. We analysed the spatio-temporal distribution of the campylobacteriosis in the Czech Republic between 2008 and 2012. We applied three main approaches in the study: (1) the geovisual analytics of the surveillance data that were visualised in the form of bubble chart; (2) the geovisual analytics of the disease's weekly incidence surfaces computed by spatio-temporal kriging and (3) the spatio-temporal scan statistics that was employed in order to identify high or low rates clusters of affected municipalities. The final data are stored in Keyhole Markup Language files and visualised in Google Earth™ in order to apply geovisual analytics. Using geovisual analytics we were able to display and retrieve information from complex dataset efficiently. Instead of searching for patterns in a series of static maps or using numerical statistics, we created the set of interactive visualisations in order to explore and communicate results of analyses to the wider audience. The results of the geovisual analytics identified periodical patterns in the behaviour of the disease as well as fourteen spatio-temporal clusters of increased relative risk. We prove that Google Earth™ software is a usable tool for the geovisual analysis of the disease distribution. Google Earth™ has many indisputable advantages (widespread, freely available, intuitive interface, space-time visualisation capabilities and animations, communication of results), nevertheless it is still needed to combine it with pre-processing tools that prepare the data into a form suitable for the geovisual analytics itself.

Immersive Visual Analytics for Transformative Neutron Scattering Science

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steed, Chad A; Daniel, Jamison R; Drouhard, Margaret

The ORNL Spallation Neutron Source (SNS) provides the most intense pulsed neutron beams in the world for scientific research and development across a broad range of disciplines. SNS experiments produce large volumes of complex data that are analyzed by scientists with varying degrees of experience using 3D visualization and analysis systems. However, it is notoriously difficult to achieve proficiency with 3D visualizations. Because 3D representations are key to understanding the neutron scattering data, scientists are unable to analyze their data in a timely fashion resulting in inefficient use of the limited and expensive SNS beam time. We believe a moremore » intuitive interface for exploring neutron scattering data can be created by combining immersive virtual reality technology with high performance data analytics and human interaction. In this paper, we present our initial investigations of immersive visualization concepts as well as our vision for an immersive visual analytics framework that could lower the barriers to 3D exploratory data analysis of neutron scattering data at the SNS.« less

Curriculum Mapping with Academic Analytics in Medical and Healthcare Education.

PubMed

Komenda, Martin; Víta, Martin; Vaitsis, Christos; Schwarz, Daniel; Pokorná, Andrea; Zary, Nabil; Dušek, Ladislav

2015-01-01

No universal solution, based on an approved pedagogical approach, exists to parametrically describe, effectively manage, and clearly visualize a higher education institution's curriculum, including tools for unveiling relationships inside curricular datasets. We aim to solve the issue of medical curriculum mapping to improve understanding of the complex structure and content of medical education programs. Our effort is based on the long-term development and implementation of an original web-based platform, which supports an outcomes-based approach to medical and healthcare education and is suitable for repeated updates and adoption to curriculum innovations. We adopted data exploration and visualization approaches in the context of medical curriculum innovations in higher education institutions domain. We have developed a robust platform, covering detailed formal metadata specifications down to the level of learning units, interconnections, and learning outcomes, in accordance with Bloom's taxonomy and direct links to a particular biomedical nomenclature. Furthermore, we used selected modeling techniques and data mining methods to generate academic analytics reports from medical curriculum mapping datasets. We present a solution that allows users to effectively optimize a curriculum structure that is described with appropriate metadata, such as course attributes, learning units and outcomes, a standardized vocabulary nomenclature, and a tree structure of essential terms. We present a case study implementation that includes effective support for curriculum reengineering efforts of academics through a comprehensive overview of the General Medicine study program. Moreover, we introduce deep content analysis of a dataset that was captured with the use of the curriculum mapping platform; this may assist in detecting any potentially problematic areas, and hence it may help to construct a comprehensive overview for the subsequent global in-depth medical curriculum inspection. We have proposed, developed, and implemented an original framework for medical and healthcare curriculum innovations and harmonization, including: planning model, mapping model, and selected academic analytics extracted with the use of data mining.

Curriculum Mapping with Academic Analytics in Medical and Healthcare Education

PubMed Central

Komenda, Martin; Víta, Martin; Vaitsis, Christos; Schwarz, Daniel; Pokorná, Andrea; Zary, Nabil; Dušek, Ladislav

2015-01-01

Background No universal solution, based on an approved pedagogical approach, exists to parametrically describe, effectively manage, and clearly visualize a higher education institution’s curriculum, including tools for unveiling relationships inside curricular datasets. Objective We aim to solve the issue of medical curriculum mapping to improve understanding of the complex structure and content of medical education programs. Our effort is based on the long-term development and implementation of an original web-based platform, which supports an outcomes-based approach to medical and healthcare education and is suitable for repeated updates and adoption to curriculum innovations. Methods We adopted data exploration and visualization approaches in the context of medical curriculum innovations in higher education institutions domain. We have developed a robust platform, covering detailed formal metadata specifications down to the level of learning units, interconnections, and learning outcomes, in accordance with Bloom’s taxonomy and direct links to a particular biomedical nomenclature. Furthermore, we used selected modeling techniques and data mining methods to generate academic analytics reports from medical curriculum mapping datasets. Results We present a solution that allows users to effectively optimize a curriculum structure that is described with appropriate metadata, such as course attributes, learning units and outcomes, a standardized vocabulary nomenclature, and a tree structure of essential terms. We present a case study implementation that includes effective support for curriculum reengineering efforts of academics through a comprehensive overview of the General Medicine study program. Moreover, we introduce deep content analysis of a dataset that was captured with the use of the curriculum mapping platform; this may assist in detecting any potentially problematic areas, and hence it may help to construct a comprehensive overview for the subsequent global in-depth medical curriculum inspection. Conclusions We have proposed, developed, and implemented an original framework for medical and healthcare curriculum innovations and harmonization, including: planning model, mapping model, and selected academic analytics extracted with the use of data mining. PMID:26624281

A Software Developer’s Guide to Informal Evaluation of Visual Analytics Environments Using VAST Challenge Information

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cook, Kristin A.; Scholtz, Jean; Whiting, Mark A.

The VAST Challenge has been a popular venue for academic and industry participants for over ten years. Many participants comment that the majority of their time in preparing VAST Challenge entries is discovering elements in their software environments that need to be redesigned in order to solve the given task. Fortunately, there is no need to wait until the VAST Challenge is announced to test out software systems. The Visual Analytics Benchmark Repository contains all past VAST Challenge tasks, data, solutions and submissions. This paper details the various types of evaluations that may be conducted using the Repository information. Inmore » this paper we describe how developers can do informal evaluations of various aspects of their visual analytics environments using VAST Challenge information. Aspects that can be evaluated include the appropriateness of the software for various tasks, the various data types and formats that can be accommodated, the effectiveness and efficiency of the process supported by the software, and the intuitiveness of the visualizations and interactions. Researchers can compare their visualizations and interactions to those submitted to determine novelty. In addition, the paper provides pointers to various guidelines that software teams can use to evaluate the usability of their software. While these evaluations are not a replacement for formal evaluation methods, this information can be extremely useful during the development of visual analytics environments.« less

Graphing trillions of triangles

PubMed Central

Burkhardt, Paul

2016-01-01

The increasing size of Big Data is often heralded but how data are transformed and represented is also profoundly important to knowledge discovery, and this is exemplified in Big Graph analytics. Much attention has been placed on the scale of the input graph but the product of a graph algorithm can be many times larger than the input. This is true for many graph problems, such as listing all triangles in a graph. Enabling scalable graph exploration for Big Graphs requires new approaches to algorithms, architectures, and visual analytics. A brief tutorial is given to aid the argument for thoughtful representation of data in the context of graph analysis. Then a new algebraic method to reduce the arithmetic operations in counting and listing triangles in graphs is introduced. Additionally, a scalable triangle listing algorithm in the MapReduce model will be presented followed by a description of the experiments with that algorithm that led to the current largest and fastest triangle listing benchmarks to date. Finally, a method for identifying triangles in new visual graph exploration technologies is proposed. PMID:28690426

Graphical Descriptives: A Way to Improve Data Transparency and Methodological Rigor in Psychology.

PubMed

Tay, Louis; Parrigon, Scott; Huang, Qiming; LeBreton, James M

2016-09-01

Several calls have recently been issued to the social sciences for enhanced transparency of research processes and enhanced rigor in the methodological treatment of data and data analytics. We propose the use of graphical descriptives (GDs) as one mechanism for responding to both of these calls. GDs provide a way to visually examine data. They serve as quick and efficient tools for checking data distributions, variable relations, and the potential appropriateness of different statistical analyses (e.g., do data meet the minimum assumptions for a particular analytic method). Consequently, we believe that GDs can promote increased transparency in the journal review process, encourage best practices for data analysis, and promote a more inductive approach to understanding psychological data. We illustrate the value of potentially including GDs as a step in the peer-review process and provide a user-friendly online resource (www.graphicaldescriptives.org) for researchers interested in including data visualizations in their research. We conclude with suggestions on how GDs can be expanded and developed to enhance transparency. © The Author(s) 2016.

An Empirical Study of Chronic Diseases in the United States: A Visual Analytics Approach to Public Health

PubMed Central

Raghupathi, Wullianallur; Raghupathi, Viju

2018-01-01

In this research we explore the current state of chronic diseases in the United States, using data from the Centers for Disease Control and Prevention and applying visualization and descriptive analytics techniques. Five main categories of variables are studied, namely chronic disease conditions, behavioral health, mental health, demographics, and overarching conditions. These are analyzed in the context of regions and states within the U.S. to discover possible correlations between variables in several categories. There are widespread variations in the prevalence of diverse chronic diseases, the number of hospitalizations for specific diseases, and the diagnosis and mortality rates for different states. Identifying such correlations is fundamental to developing insights that will help in the creation of targeted management, mitigation, and preventive policies, ultimately minimizing the risks and costs of chronic diseases. As the population ages and individuals suffer from multiple conditions, or comorbidity, it is imperative that the various stakeholders, including the government, non-governmental organizations (NGOs), policy makers, health providers, and society as a whole, address these adverse effects in a timely and efficient manner. PMID:29494555

The Role of Visual Learning in Improving Students' High-Order Thinking Skills

ERIC Educational Resources Information Center

Raiyn, Jamal

2016-01-01

Various concepts have been introduced to improve students' analytical thinking skills based on problem based learning (PBL). This paper introduces a new concept to increase student's analytical thinking skills based on a visual learning strategy. Such a strategy has three fundamental components: a teacher, a student, and a learning process. The…

Multi-hazard national-level risk assessment in Africa using global approaches

NASA Astrophysics Data System (ADS)

Fraser, Stuart; Jongman, Brenden; Simpson, Alanna; Murnane, Richard

2016-04-01

In recent years Sub-Saharan Africa has been characterized by unprecedented opportunity for transformation and sustained growth. However, natural disasters such as droughts, floods, cyclones, earthquakes, landslides, volcanic eruptions and extreme temperatures cause significant economic and human losses, and major development challenges. Quantitative disaster risk assessments are an important basis for governments to understand disaster risk in their country, and to develop effective risk management and risk financing solutions. However, the data-scarce nature of many Sub-Saharan African countries as well as a lack of financing for risk assessments has long prevented detailed analytics. Recent advances in globally applicable disaster risk modelling practices and data availability offer new opportunities. In December 2013 the European Union approved a € 60 million contribution to support the development of an analytical basis for risk financing and to accelerate the effective implementation of a comprehensive disaster risk reduction. The World Bank's Global Facility for Disaster Reduction and Recovery (GFDRR) was selected as the implementing partner of the Program for Result Area 5: the "Africa Disaster Risk Assessment and Financing Program." As part of this effort, the GFDRR is overseeing the production of national-level multi-hazard risk profiles for a range of countries in Sub-Saharan Africa, using a combination of national and global datasets and state-of-the-art hazard and risk assessment methodologies. In this presentation, we will highlight the analytical approach behind these assessments, and show results for the first five countries for which the assessment has been completed (Kenya, Uganda, Senegal, Niger and Ethiopia). The presentation will also demonstrate the visualization of the risk assessments into understandable and visually attractive risk profile documents.

A Graphics Design Framework to Visualize Multi-Dimensional Economic Datasets

ERIC Educational Resources Information Center

Chandramouli, Magesh; Narayanan, Badri; Bertoline, Gary R.

2013-01-01

This study implements a prototype graphics visualization framework to visualize multidimensional data. This graphics design framework serves as a "visual analytical database" for visualization and simulation of economic models. One of the primary goals of any kind of visualization is to extract useful information from colossal volumes of…

Application of Data Provenance in Healthcare Analytics Software: Information Visualisation of User Activities

PubMed Central

Xu, Shen; Rogers, Toby; Fairweather, Elliot; Glenn, Anthony; Curran, James; Curcin, Vasa

2018-01-01

Data provenance is a technique that describes the history of digital objects. In health data settings, it can be used to deliver auditability and transparency, and to achieve trust in a software system. However, implementing data provenance in analytics software at an enterprise level presents a different set of challenges from the research environments where data provenance was originally devised. In this paper, the challenges of reporting provenance information to the user is presented. Provenance captured from analytics software can be large and complex and visualizing a series of tasks over a long period can be overwhelming even for a domain expert, requiring visual aggregation mechanisms that fit with complex human cognitive activities involved in the process. This research studied how provenance-based reporting can be integrated into a health data analytics software, using the example of Atmolytics visual reporting tool. PMID:29888084

The science of visual analysis at extreme scale

NASA Astrophysics Data System (ADS)

Nowell, Lucy T.

2011-01-01

Driven by market forces and spanning the full spectrum of computational devices, computer architectures are changing in ways that present tremendous opportunities and challenges for data analysis and visual analytic technologies. Leadership-class high performance computing system will have as many as a million cores by 2020 and support 10 billion-way concurrency, while laptop computers are expected to have as many as 1,000 cores by 2015. At the same time, data of all types are increasing exponentially and automated analytic methods are essential for all disciplines. Many existing analytic technologies do not scale to make full use of current platforms and fewer still are likely to scale to the systems that will be operational by the end of this decade. Furthermore, on the new architectures and for data at extreme scales, validating the accuracy and effectiveness of analytic methods, including visual analysis, will be increasingly important.

Developing Visual Thinking in the Electronic Health Record.

PubMed

Boyd, Andrew D; Young, Christine D; Amatayakul, Margret; Dieter, Michael G; Pawola, Lawrence M

2017-01-01

The purpose of this vision paper is to identify how data visualization could transform healthcare. Electronic Health Records (EHRs) are maturing with new technology and tools being applied. Researchers are reaping the benefits of data visualization to better access compilations of EHR data for enhanced clinical research. Data visualization, while still primarily the domain of clinical researchers, is beginning to show promise for other stakeholders. A non-exhaustive review of the literature indicates that respective to the growth and development of the EHR, the maturity of data visualization in healthcare is in its infancy. Visual analytics has been only cursorily applied to healthcare. A fundamental issue contributing to fragmentation and poor coordination of healthcare delivery is that each member of the healthcare team, including patients, has a different view. Summarizing all of this care comprehensively for any member of the healthcare team is a "wickedly hard" visual analytics and data visualization problem to solve.

Distance-based microfluidic quantitative detection methods for point-of-care testing.

PubMed

Tian, Tian; Li, Jiuxing; Song, Yanling; Zhou, Leiji; Zhu, Zhi; Yang, Chaoyong James

2016-04-07

Equipment-free devices with quantitative readout are of great significance to point-of-care testing (POCT), which provides real-time readout to users and is especially important in low-resource settings. Among various equipment-free approaches, distance-based visual quantitative detection methods rely on reading the visual signal length for corresponding target concentrations, thus eliminating the need for sophisticated instruments. The distance-based methods are low-cost, user-friendly and can be integrated into portable analytical devices. Moreover, such methods enable quantitative detection of various targets by the naked eye. In this review, we first introduce the concept and history of distance-based visual quantitative detection methods. Then, we summarize the main methods for translation of molecular signals to distance-based readout and discuss different microfluidic platforms (glass, PDMS, paper and thread) in terms of applications in biomedical diagnostics, food safety monitoring, and environmental analysis. Finally, the potential and future perspectives are discussed.

Multiplexed Paper Analytical Device for Quantification of Metals using Distance-Based Detection

PubMed Central

Cate, David M.; Noblitt, Scott D.; Volckens, John; Henry, Charles S.

2015-01-01

Exposure to metal-containing aerosols has been linked with adverse health outcomes for almost every organ in the human body. Commercially available techniques for quantifying particulate metals are time-intensive, laborious, and expensive; often sample analysis exceeds $100. We report a simple technique, based upon a distance-based detection motif, for quantifying metal concentrations of Ni, Cu, and Fe in airborne particulate matter using microfluidic paper-based analytical devices. Paper substrates are used to create sensors that are self-contained, self-timing, and require only a drop of sample for operation. Unlike other colorimetric approaches in paper microfluidics that rely on optical instrumentation for analysis, with distance-based detection, analyte is quantified visually based on the distance of a colorimetric reaction, similar to reading temperature on a thermometer. To demonstrate the effectiveness of this approach, Ni, Cu, and Fe were measured individually in single-channel devices; detection limits as low as 0.1, 0.1, and 0.05 µg were reported for Ni, Cu, and Fe. Multiplexed analysis of all three metals was achieved with detection limits of 1, 5, and 1 µg for Ni, Cu, and Fe. We also extended the dynamic range for multi-analyte detection by printing concentration gradients of colorimetric reagents using an off the shelf inkjet printer. Analyte selectivity was demonstrated for common interferences. To demonstrate utility of the method, Ni, Cu, and Fe were measured from samples of certified welding fume; levels measured with paper sensors matched known values determined gravimetrically. PMID:26009988

The effect of introspection on judgment and decision making is dependent on the quality of conscious thinking.

PubMed

Leisti, Tuomas; Häkkinen, Jukka

2016-05-01

That introspection may impair certain judgments and result in fabrication has been attributed to a distracting shift from more adaptive intuitive processing to more analytic and conscious processing. This phenomenon was studied in an experiment where participants made multidimensional visual choices. It was found that the effect of this shift on decision-making performance was dependent on the quality of the explanations during introspection, while the performance in silent conditions was not. Therefore, it appears that the effect of introspection on judgments is not only influenced by the thinking mode per se, but also by the individual's ability to approach the decision problem analytically. Copyright © 2016 Elsevier Inc. All rights reserved.

SmartR: an open-source platform for interactive visual analytics for translational research data

PubMed Central

Herzinger, Sascha; Gu, Wei; Satagopam, Venkata; Eifes, Serge; Rege, Kavita; Barbosa-Silva, Adriano; Schneider, Reinhard

2017-01-01

Abstract Summary: In translational research, efficient knowledge exchange between the different fields of expertise is crucial. An open platform that is capable of storing a multitude of data types such as clinical, pre-clinical or OMICS data combined with strong visual analytical capabilities will significantly accelerate the scientific progress by making data more accessible and hypothesis generation easier. The open data warehouse tranSMART is capable of storing a variety of data types and has a growing user community including both academic institutions and pharmaceutical companies. tranSMART, however, currently lacks interactive and dynamic visual analytics and does not permit any post-processing interaction or exploration. For this reason, we developed SmartR, a plugin for tranSMART, that equips the platform not only with several dynamic visual analytical workflows, but also provides its own framework for the addition of new custom workflows. Modern web technologies such as D3.js or AngularJS were used to build a set of standard visualizations that were heavily improved with dynamic elements. Availability and Implementation: The source code is licensed under the Apache 2.0 License and is freely available on GitHub: https://github.com/transmart/SmartR. Contact: reinhard.schneider@uni.lu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28334291

SmartR: an open-source platform for interactive visual analytics for translational research data.

PubMed

Herzinger, Sascha; Gu, Wei; Satagopam, Venkata; Eifes, Serge; Rege, Kavita; Barbosa-Silva, Adriano; Schneider, Reinhard

2017-07-15

In translational research, efficient knowledge exchange between the different fields of expertise is crucial. An open platform that is capable of storing a multitude of data types such as clinical, pre-clinical or OMICS data combined with strong visual analytical capabilities will significantly accelerate the scientific progress by making data more accessible and hypothesis generation easier. The open data warehouse tranSMART is capable of storing a variety of data types and has a growing user community including both academic institutions and pharmaceutical companies. tranSMART, however, currently lacks interactive and dynamic visual analytics and does not permit any post-processing interaction or exploration. For this reason, we developed SmartR , a plugin for tranSMART, that equips the platform not only with several dynamic visual analytical workflows, but also provides its own framework for the addition of new custom workflows. Modern web technologies such as D3.js or AngularJS were used to build a set of standard visualizations that were heavily improved with dynamic elements. The source code is licensed under the Apache 2.0 License and is freely available on GitHub: https://github.com/transmart/SmartR . reinhard.schneider@uni.lu. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

Feature Geo Analytics and Big Data Processing: Hybrid Approaches for Earth Science and Real-Time Decision Support

NASA Astrophysics Data System (ADS)

Wright, D. J.; Raad, M.; Hoel, E.; Park, M.; Mollenkopf, A.; Trujillo, R.

2016-12-01

Introduced is a new approach for processing spatiotemporal big data by leveraging distributed analytics and storage. A suite of temporally-aware analysis tools summarizes data nearby or within variable windows, aggregates points (e.g., for various sensor observations or vessel positions), reconstructs time-enabled points into tracks (e.g., for mapping and visualizing storm tracks), joins features (e.g., to find associations between features based on attributes, spatial relationships, temporal relationships or all three simultaneously), calculates point densities, finds hot spots (e.g., in species distributions), and creates space-time slices and cubes (e.g., in microweather applications with temperature, humidity, and pressure, or within human mobility studies). These "feature geo analytics" tools run in both batch and streaming spatial analysis mode as distributed computations across a cluster of servers on typical "big" data sets, where static data exist in traditional geospatial formats (e.g., shapefile) locally on a disk or file share, attached as static spatiotemporal big data stores, or streamed in near-real-time. In other words, the approach registers large datasets or data stores with ArcGIS Server, then distributes analysis across a cluster of machines for parallel processing. Several brief use cases will be highlighted based on a 16-node server cluster at 14 Gb RAM per node, allowing, for example, the buffering of over 8 million points or thousands of polygons in 1 minute. The approach is "hybrid" in that ArcGIS Server integrates open-source big data frameworks such as Apache Hadoop and Apache Spark on the cluster in order to run the analytics. In addition, the user may devise and connect custom open-source interfaces and tools developed in Python or Python Notebooks; the common denominator being the familiar REST API.

Podium: Ranking Data Using Mixed-Initiative Visual Analytics.

PubMed

Wall, Emily; Das, Subhajit; Chawla, Ravish; Kalidindi, Bharath; Brown, Eli T; Endert, Alex

2018-01-01

People often rank and order data points as a vital part of making decisions. Multi-attribute ranking systems are a common tool used to make these data-driven decisions. Such systems often take the form of a table-based visualization in which users assign weights to the attributes representing the quantifiable importance of each attribute to a decision, which the system then uses to compute a ranking of the data. However, these systems assume that users are able to quantify their conceptual understanding of how important particular attributes are to a decision. This is not always easy or even possible for users to do. Rather, people often have a more holistic understanding of the data. They form opinions that data point A is better than data point B but do not necessarily know which attributes are important. To address these challenges, we present a visual analytic application to help people rank multi-variate data points. We developed a prototype system, Podium, that allows users to drag rows in the table to rank order data points based on their perception of the relative value of the data. Podium then infers a weighting model using Ranking SVM that satisfies the user's data preferences as closely as possible. Whereas past systems help users understand the relationships between data points based on changes to attribute weights, our approach helps users to understand the attributes that might inform their understanding of the data. We present two usage scenarios to describe some of the potential uses of our proposed technique: (1) understanding which attributes contribute to a user's subjective preferences for data, and (2) deconstructing attributes of importance for existing rankings. Our proposed approach makes powerful machine learning techniques more usable to those who may not have expertise in these areas.

Evaluation of Analytical Modeling Functions for the Phonation Onset Process.

PubMed

Petermann, Simon; Kniesburges, Stefan; Ziethe, Anke; Schützenberger, Anne; Döllinger, Michael

2016-01-01

The human voice originates from oscillations of the vocal folds in the larynx. The duration of the voice onset (VO), called the voice onset time (VOT), is currently under investigation as a clinical indicator for correct laryngeal functionality. Different analytical approaches for computing the VOT based on endoscopic imaging were compared to determine the most reliable method to quantify automatically the transient vocal fold oscillations during VO. Transnasal endoscopic imaging in combination with a high-speed camera (8000 fps) was applied to visualize the phonation onset process. Two different definitions of VO interval were investigated. Six analytical functions were tested that approximate the envelope of the filtered or unfiltered glottal area waveform (GAW) during phonation onset. A total of 126 recordings from nine healthy males and 210 recordings from 15 healthy females were evaluated. Three criteria were analyzed to determine the most appropriate computation approach: (1) reliability of the fit function for a correct approximation of VO; (2) consistency represented by the standard deviation of VOT; and (3) accuracy of the approximation of VO. The results suggest the computation of VOT by a fourth-order polynomial approximation in the interval between 32.2 and 67.8% of the saturation amplitude of the filtered GAW.

Use of the self-organising map network (SOMNet) as a decision support system for regional mental health planning.

PubMed

Chung, Younjin; Salvador-Carulla, Luis; Salinas-Pérez, José A; Uriarte-Uriarte, Jose J; Iruin-Sanz, Alvaro; García-Alonso, Carlos R

2018-04-25

Decision-making in mental health systems should be supported by the evidence-informed knowledge transfer of data. Since mental health systems are inherently complex, involving interactions between its structures, processes and outcomes, decision support systems (DSS) need to be developed using advanced computational methods and visual tools to allow full system analysis, whilst incorporating domain experts in the analysis process. In this study, we use a DSS model developed for interactive data mining and domain expert collaboration in the analysis of complex mental health systems to improve system knowledge and evidence-informed policy planning. We combine an interactive visual data mining approach, the self-organising map network (SOMNet), with an operational expert knowledge approach, expert-based collaborative analysis (EbCA), to develop a DSS model. The SOMNet was applied to the analysis of healthcare patterns and indicators of three different regional mental health systems in Spain, comprising 106 small catchment areas and providing healthcare for over 9 million inhabitants. Based on the EbCA, the domain experts in the development team guided and evaluated the analytical processes and results. Another group of 13 domain experts in mental health systems planning and research evaluated the model based on the analytical information of the SOMNet approach for processing information and discovering knowledge in a real-world context. Through the evaluation, the domain experts assessed the feasibility and technology readiness level (TRL) of the DSS model. The SOMNet, combined with the EbCA, effectively processed evidence-based information when analysing system outliers, explaining global and local patterns, and refining key performance indicators with their analytical interpretations. The evaluation results showed that the DSS model was feasible by the domain experts and reached level 7 of the TRL (system prototype demonstration in operational environment). This study supports the benefits of combining health systems engineering (SOMNet) and expert knowledge (EbCA) to analyse the complexity of health systems research. The use of the SOMNet approach contributes to the demonstration of DSS for mental health planning in practice.

Microemulsification: an approach for analytical determinations.

PubMed

Lima, Renato S; Shiroma, Leandro Y; Teixeira, Alvaro V N C; de Toledo, José R; do Couto, Bruno C; de Carvalho, Rogério M; Carrilho, Emanuel; Kubota, Lauro T; Gobbi, Angelo L

2014-09-16

We address a novel method for analytical determinations that combines simplicity, rapidity, low consumption of chemicals, and portability with high analytical performance taking into account parameters such as precision, linearity, robustness, and accuracy. This approach relies on the effect of the analyte content over the Gibbs free energy of dispersions, affecting the thermodynamic stabilization of emulsions or Winsor systems to form microemulsions (MEs). Such phenomenon was expressed by the minimum volume fraction of amphiphile required to form microemulsion (Φ(ME)), which was the analytical signal of the method. Thus, the measurements can be taken by visually monitoring the transition of the dispersions from cloudy to transparent during the microemulsification, like a titration. It bypasses the employment of electric energy. The performed studies were: phase behavior, droplet dimension by dynamic light scattering, analytical curve, and robustness tests. The reliability of the method was evaluated by determining water in ethanol fuels and monoethylene glycol in complex samples of liquefied natural gas. The dispersions were composed of water-chlorobenzene (water analysis) and water-oleic acid (monoethylene glycol analysis) with ethanol as the hydrotrope phase. The mean hydrodynamic diameter values for the nanostructures in the droplet-based water-chlorobenzene MEs were in the range of 1 to 11 nm. The procedures of microemulsification were conducted by adding ethanol to water-oleic acid (W-O) mixtures with the aid of micropipette and shaking. The Φ(ME) measurements were performed in a thermostatic water bath at 23 °C by direct observation that is based on the visual analyses of the media. The experiments to determine water demonstrated that the analytical performance depends on the composition of ME. It shows flexibility in the developed method. The linear range was fairly broad with limits of linearity up to 70.00% water in ethanol. For monoethylene glycol in water, in turn, the linear range was observed throughout the volume fraction of analyte. The best limits of detection were 0.32% v/v water to ethanol and 0.30% v/v monoethylene glycol to water. Furthermore, the accuracy was highly satisfactory. The natural gas samples provided by the Petrobras exhibited color, particulate material, high ionic strength, and diverse compounds as metals, carboxylic acids, and anions. These samples had a conductivity of up to 2630 μS cm(-1); the conductivity of pure monoethylene glycol was only 0.30 μS cm(-1). Despite such downsides, the method allowed accurate measures bypassing steps such as extraction, preconcentration, and dilution of the sample. In addition, the levels of robustness were promising. This parameter was evaluated by investigating the effect of (i) deviations in volumetric preparation of the dispersions and (ii) changes in temperature over the analyte contents recorded by the method.

Geometric quantification of features in large flow fields.

PubMed

Kendall, Wesley; Huang, Jian; Peterka, Tom

2012-01-01

Interactive exploration of flow features in large-scale 3D unsteady-flow data is one of the most challenging visualization problems today. To comprehensively explore the complex feature spaces in these datasets, a proposed system employs a scalable framework for investigating a multitude of characteristics from traced field lines. This capability supports the examination of various neighborhood-based geometric attributes in concert with other scalar quantities. Such an analysis wasn't previously possible because of the large computational overhead and I/O requirements. The system integrates visual analytics methods by letting users procedurally and interactively describe and extract high-level flow features. An exploration of various phenomena in a large global ocean-modeling simulation demonstrates the approach's generality and expressiveness as well as its efficacy.

Application of surface analytical methods for hazardous situation in the Adriatic Sea: monitoring of organic matter dynamics and oil pollution

NASA Astrophysics Data System (ADS)

Pletikapić, Galja; Ivošević DeNardis, Nadica

2017-01-01

Surface analytical methods are applied to examine the environmental status of seawaters. The present overview emphasizes advantages of combining surface analytical methods, applied to a hazardous situation in the Adriatic Sea, such as monitoring of the first aggregation phases of dissolved organic matter in order to potentially predict the massive mucilage formation and testing of oil spill cleanup. Such an approach, based on fast and direct characterization of organic matter and its high-resolution visualization, sets a continuous-scale description of organic matter from micro- to nanometre scales. Electrochemical method of chronoamperometry at the dropping mercury electrode meets the requirements for monitoring purposes due to the simple and fast analysis of a large number of natural seawater samples enabling simultaneous differentiation of organic constituents. In contrast, atomic force microscopy allows direct visualization of biotic and abiotic particles and provides an insight into structural organization of marine organic matter at micro- and nanometre scales. In the future, merging data at different spatial scales, taking into account experimental input on micrometre scale, observations on metre scale and modelling on kilometre scale, will be important for developing sophisticated technological platforms for knowledge transfer, reports and maps applicable for the marine environmental protection and management of the coastal area, especially for tourism, fishery and cruiser trafficking.

SnapShot: Visualization to Propel Ice Hockey Analytics.

PubMed

Pileggi, H; Stolper, C D; Boyle, J M; Stasko, J T

2012-12-01

Sports analysts live in a world of dynamic games flattened into tables of numbers, divorced from the rinks, pitches, and courts where they were generated. Currently, these professional analysts use R, Stata, SAS, and other statistical software packages for uncovering insights from game data. Quantitative sports consultants seek a competitive advantage both for their clients and for themselves as analytics becomes increasingly valued by teams, clubs, and squads. In order for the information visualization community to support the members of this blossoming industry, it must recognize where and how visualization can enhance the existing analytical workflow. In this paper, we identify three primary stages of today's sports analyst's routine where visualization can be beneficially integrated: 1) exploring a dataspace; 2) sharing hypotheses with internal colleagues; and 3) communicating findings to stakeholders.Working closely with professional ice hockey analysts, we designed and built SnapShot, a system to integrate visualization into the hockey intelligence gathering process. SnapShot employs a variety of information visualization techniques to display shot data, yet given the importance of a specific hockey statistic, shot length, we introduce a technique, the radial heat map. Through a user study, we received encouraging feedback from several professional analysts, both independent consultants and professional team personnel.

Visualizing Molecular Diffusion through Passive Permeability Barriers in Cells: Conventional and Novel Approaches

PubMed Central

Lin, Yu-Chun; Phua, Siew Cheng; Lin, Benjamin; Inoue, Takanari

2013-01-01

Diffusion barriers are universal solutions for cells to achieve distinct organizations, compositions, and activities within a limited space. The influence of diffusion barriers on the spatiotemporal dynamics of signaling molecules often determines cellular physiology and functions. Over the years, the passive permeability barriers in various subcellular locales have been characterized using elaborate analytical techniques. In this review, we will summarize the current state of knowledge on the various passive permeability barriers present in mammalian cells. We will conclude with a description of several conventional techniques and one new approach based on chemically-inducible diffusion trap (C-IDT) for probing permeable barriers. PMID:23731778

Design Patterns to Achieve 300x Speedup for Oceanographic Analytics in the Cloud

NASA Astrophysics Data System (ADS)

Jacob, J. C.; Greguska, F. R., III; Huang, T.; Quach, N.; Wilson, B. D.

2017-12-01

We describe how we achieve super-linear speedup over standard approaches for oceanographic analytics on a cluster computer and the Amazon Web Services (AWS) cloud. NEXUS is an open source platform for big data analytics in the cloud that enables this performance through a combination of horizontally scalable data parallelism with Apache Spark and rapid data search, subset, and retrieval with tiled array storage in cloud-aware NoSQL databases like Solr and Cassandra. NEXUS is the engine behind several public portals at NASA and OceanWorks is a newly funded project for the ocean community that will mature and extend this capability for improved data discovery, subset, quality screening, analysis, matchup of satellite and in situ measurements, and visualization. We review the Python language API for Spark and how to use it to quickly convert existing programs to use Spark to run with cloud-scale parallelism, and discuss strategies to improve performance. We explain how partitioning the data over space, time, or both leads to algorithmic design patterns for Spark analytics that can be applied to many different algorithms. We use NEXUS analytics as examples, including area-averaged time series, time averaged map, and correlation map.

Modeling human comprehension of data visualizations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matzen, Laura E.; Haass, Michael Joseph; Divis, Kristin Marie

This project was inspired by two needs. The first is a need for tools to help scientists and engineers to design effective data visualizations for communicating information, whether to the user of a system, an analyst who must make decisions based on complex data, or in the context of a technical report or publication. Most scientists and engineers are not trained in visualization design, and they could benefit from simple metrics to assess how well their visualization's design conveys the intended message. In other words, will the most important information draw the viewer's attention? The second is the need formore » cognition-based metrics for evaluating new types of visualizations created by researchers in the information visualization and visual analytics communities. Evaluating visualizations is difficult even for experts. However, all visualization methods and techniques are intended to exploit the properties of the human visual system to convey information efficiently to a viewer. Thus, developing evaluation methods that are rooted in the scientific knowledge of the human visual system could be a useful approach. In this project, we conducted fundamental research on how humans make sense of abstract data visualizations, and how this process is influenced by their goals and prior experience. We then used that research to develop a new model, the Data Visualization Saliency Model, that can make accurate predictions about which features in an abstract visualization will draw a viewer's attention. The model is an evaluation tool that can address both of the needs described above, supporting both visualization research and Sandia mission needs.« less

Creating value in health care through big data: opportunities and policy implications.

PubMed

Roski, Joachim; Bo-Linn, George W; Andrews, Timothy A

2014-07-01

Big data has the potential to create significant value in health care by improving outcomes while lowering costs. Big data's defining features include the ability to handle massive data volume and variety at high velocity. New, flexible, and easily expandable information technology (IT) infrastructure, including so-called data lakes and cloud data storage and management solutions, make big-data analytics possible. However, most health IT systems still rely on data warehouse structures. Without the right IT infrastructure, analytic tools, visualization approaches, work flows, and interfaces, the insights provided by big data are likely to be limited. Big data's success in creating value in the health care sector may require changes in current polices to balance the potential societal benefits of big-data approaches and the protection of patients' confidentiality. Other policy implications of using big data are that many current practices and policies related to data use, access, sharing, privacy, and stewardship need to be revised. Project HOPE—The People-to-People Health Foundation, Inc.

QFD-ANP Approach for the Conceptual Design of Research Vessels: A Case Study

NASA Astrophysics Data System (ADS)

Venkata Subbaiah, Kambagowni; Yeshwanth Sai, Koneru; Suresh, Challa

2016-10-01

Conceptual design is a subset of concept art wherein a new idea of product is created instead of a visual representation which would directly be used in a final product. The purpose is to understand the needs of conceptual design which are being used in engineering designs and to clarify the current conceptual design practice. Quality function deployment (QFD) is a customer oriented design approach for developing new or improved products and services to enhance customer satisfaction. House of quality (HOQ) has been traditionally used as planning tool of QFD which translates customer requirements (CRs) into design requirements (DRs). Factor analysis is carried out in order to reduce the CR portions of HOQ. The analytical hierarchical process is employed to obtain the priority ratings of CR's which are used in constructing HOQ. This paper mainly discusses about the conceptual design of an oceanographic research vessel using analytical network process (ANP) technique. Finally the QFD-ANP integrated methodology helps to establish the importance ratings of DRs.

Towards a Web-Enabled Geovisualization and Analytics Platform for the Energy and Water Nexus

NASA Astrophysics Data System (ADS)

Sanyal, J.; Chandola, V.; Sorokine, A.; Allen, M.; Berres, A.; Pang, H.; Karthik, R.; Nugent, P.; McManamay, R.; Stewart, R.; Bhaduri, B. L.

2017-12-01

Interactive data analytics are playing an increasingly vital role in the generation of new, critical insights regarding the complex dynamics of the energy/water nexus (EWN) and its interactions with climate variability and change. Integration of impacts, adaptation, and vulnerability (IAV) science with emerging, and increasingly critical, data science capabilities offers a promising potential to meet the needs of the EWN community. To enable the exploration of pertinent research questions, a web-based geospatial visualization platform is being built that integrates a data analysis toolbox with advanced data fusion and data visualization capabilities to create a knowledge discovery framework for the EWN. The system, when fully built out, will offer several geospatial visualization capabilities including statistical visual analytics, clustering, principal-component analysis, dynamic time warping, support uncertainty visualization and the exploration of data provenance, as well as support machine learning discoveries to render diverse types of geospatial data and facilitate interactive analysis. Key components in the system architecture includes NASA's WebWorldWind, the Globus toolkit, postgresql, as well as other custom built software modules.

Visualization techniques for computer network defense

NASA Astrophysics Data System (ADS)

Beaver, Justin M.; Steed, Chad A.; Patton, Robert M.; Cui, Xiaohui; Schultz, Matthew

2011-06-01

Effective visual analysis of computer network defense (CND) information is challenging due to the volume and complexity of both the raw and analyzed network data. A typical CND is comprised of multiple niche intrusion detection tools, each of which performs network data analysis and produces a unique alerting output. The state-of-the-practice in the situational awareness of CND data is the prevalent use of custom-developed scripts by Information Technology (IT) professionals to retrieve, organize, and understand potential threat events. We propose a new visual analytics framework, called the Oak Ridge Cyber Analytics (ORCA) system, for CND data that allows an operator to interact with all detection tool outputs simultaneously. Aggregated alert events are presented in multiple coordinated views with timeline, cluster, and swarm model analysis displays. These displays are complemented with both supervised and semi-supervised machine learning classifiers. The intent of the visual analytics framework is to improve CND situational awareness, to enable an analyst to quickly navigate and analyze thousands of detected events, and to combine sophisticated data analysis techniques with interactive visualization such that patterns of anomalous activities may be more easily identified and investigated.

Big Data in industry

NASA Astrophysics Data System (ADS)

Latinović, T. S.; Preradović, D. M.; Barz, C. R.; Latinović, M. T.; Petrica, P. P.; Pop-Vadean, A.

2016-08-01

The amount of data at the global level has grown exponentially. Along with this phenomena, we have a need for a new unit of measure like exabyte, zettabyte, and yottabyte as the last unit measures the amount of data. The growth of data gives a situation where the classic systems for the collection, storage, processing, and visualization of data losing the battle with a large amount, speed, and variety of data that is generated continuously. Many of data that is created by the Internet of Things, IoT (cameras, satellites, cars, GPS navigation, etc.). It is our challenge to come up with new technologies and tools for the management and exploitation of these large amounts of data. Big Data is a hot topic in recent years in IT circles. However, Big Data is recognized in the business world, and increasingly in the public administration. This paper proposes an ontology of big data analytics and examines how to enhance business intelligence through big data analytics as a service by presenting a big data analytics services-oriented architecture. This paper also discusses the interrelationship between business intelligence and big data analytics. The proposed approach in this paper might facilitate the research and development of business analytics, big data analytics, and business intelligence as well as intelligent agents.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sanfilippo, Antonio P.; Chikkagoudar, Satish

We describe an approach to analyzing trade data which uses clustering to detect similarities across shipping manifest records, classification to evaluate clustering results and categorize new unseen shipping data records, and visual analytics to provide to support situation awareness in dynamic decision making to monitor and warn against the movement of radiological threat materials through search, analysis and forecasting capabilities. The evaluation of clustering results through classification and systematic inspection of the clusters show the clusters have strong semantic cohesion and offer novel ways to detect transactions related to nuclear smuggling.

Experiences of Individuals With Visual Impairments in Integrated Physical Education: A Retrospective Study.

PubMed

Haegele, Justin A; Zhu, Xihe

2017-12-01

The purpose of this retrospective study was to examine the experiences of adults with visual impairments during school-based integrated physical education (PE). An interpretative phenomenological analysis (IPA) research approach was used and 16 adults (ages 21-48 years; 10 women, 6 men) with visual impairments acted as participants for this study. The primary sources of data were semistructured audiotaped telephone interviews and reflective field notes, which were recorded during and immediately following each interview. Thematic development was undertaken utilizing a 3-step analytical process guided by IPA. Based on the data analysis, 3 interrelated themes emerged from the participant transcripts: (a) feelings about "being put to the side," frustration and inadequacy; (b) "She is blind, she can't do it," debilitating feelings from physical educators' attitudes; and (c) "not self-esteem raising," feelings about peer interactions. The 1st theme described the participants' experiences and ascribed meaning to exclusionary practices. The 2nd theme described the participants' frustration over being treated differently by their PE teachers because of their visual impairments. Lastly, "not self-esteem raising," feelings about peer interactions demonstrated how participants felt about issues regarding challenging social situations with peers in PE. Utilizing an IPA approach, the researchers uncovered 3 interrelated themes that depicted central feelings, experiences, and reflections, which informed the meaning of the participants' PE experiences. The emerged themes provide unique insight into the embodied experiences of those with visual impairments in PE and fill a previous gap in the extant literature.

FuryExplorer: visual-interactive exploration of horse motion capture data

NASA Astrophysics Data System (ADS)

Wilhelm, Nils; Vögele, Anna; Zsoldos, Rebeka; Licka, Theresia; Krüger, Björn; Bernard, Jürgen

2015-01-01

The analysis of equine motion has a long tradition in the past of mankind. Equine biomechanics aims at detecting characteristics of horses indicative of good performance. Especially, veterinary medicine gait analysis plays an important role in diagnostics and in the emerging research of long-term effects of athletic exercises. More recently, the incorporation of motion capture technology contributed to an easier and faster analysis, with a trend from mere observation of horses towards the analysis of multivariate time-oriented data. However, due to the novelty of this topic being raised within an interdisciplinary context, there is yet a lack of visual-interactive interfaces to facilitate time series data analysis and information discourse for the veterinary and biomechanics communities. In this design study, we bring visual analytics technology into the respective domains, which, to our best knowledge, was never approached before. Based on requirements developed in the domain characterization phase, we present a visual-interactive system for the exploration of horse motion data. The system provides multiple views which enable domain experts to explore frequent poses and motions, but also to drill down to interesting subsets, possibly containing unexpected patterns. We show the applicability of the system in two exploratory use cases, one on the comparison of different gait motions, and one on the analysis of lameness recovery. Finally, we present the results of a summative user study conducted in the environment of the domain experts. The overall outcome was a significant improvement in effectiveness and efficiency in the analytical workflow of the domain experts.

How Can Visual Analytics Assist Investigative Analysis? Design Implications from an Evaluation.

PubMed

Youn-Ah Kang; Görg, Carsten; Stasko, John

2011-05-01

Despite the growing number of systems providing visual analytic support for investigative analysis, few empirical studies of the potential benefits of such systems have been conducted, particularly controlled, comparative evaluations. Determining how such systems foster insight and sensemaking is important for their continued growth and study, however. Furthermore, studies that identify how people use such systems and why they benefit (or not) can help inform the design of new systems in this area. We conducted an evaluation of the visual analytics system Jigsaw employed in a small investigative sensemaking exercise, and compared its use to three other more traditional methods of analysis. Sixteen participants performed a simulated intelligence analysis task under one of the four conditions. Experimental results suggest that Jigsaw assisted participants to analyze the data and identify an embedded threat. We describe different analysis strategies used by study participants and how computational support (or the lack thereof) influenced the strategies. We then illustrate several characteristics of the sensemaking process identified in the study and provide design implications for investigative analysis tools based thereon. We conclude with recommendations on metrics and techniques for evaluating visual analytics systems for investigative analysis.

A Paper-Based Electrochromic Array for Visualized Electrochemical Sensing.

PubMed

Zhang, Fengling; Cai, Tianyi; Ma, Liang; Zhan, Liyuan; Liu, Hong

2017-01-31

We report a battery-powered, paper-based electrochromic array for visualized electrochemical sensing. The paper-based sensing system consists of six parallel electrochemical cells, which are powered by an aluminum-air battery. Each single electrochemical cell uses a Prussian Blue spot electrodeposited on an indium-doped tin oxide thin film as the electrochromic indicator. Each electrochemical cell is preloaded with increasing amounts of analyte. The sample activates the battery for the sensing. Both the preloaded analyte and the analyte in the sample initiate the color change of Prussian Blue to Prussian White. With a reaction time of 60 s, the number of electrochemical cells with complete color changes is correlated to the concentration of analyte in the sample. As a proof-of-concept analyte, lactic acid was detected semi-quantitatively using the naked eye.

Insight solutions are correct more often than analytic solutions

PubMed Central

Salvi, Carola; Bricolo, Emanuela; Kounios, John; Bowden, Edward; Beeman, Mark

2016-01-01

How accurate are insights compared to analytical solutions? In four experiments, we investigated how participants’ solving strategies influenced their solution accuracies across different types of problems, including one that was linguistic, one that was visual and two that were mixed visual-linguistic. In each experiment, participants’ self-judged insight solutions were, on average, more accurate than their analytic ones. We hypothesised that insight solutions have superior accuracy because they emerge into consciousness in an all-or-nothing fashion when the unconscious solving process is complete, whereas analytic solutions can be guesses based on conscious, prematurely terminated, processing. This hypothesis is supported by the finding that participants’ analytic solutions included relatively more incorrect responses (i.e., errors of commission) than timeouts (i.e., errors of omission) compared to their insight responses. PMID:27667960

Chromatographic Separation and Visual Detection on Wicking Microfluidic Devices: Quantitation of Cu2+ in Surface, Ground, and Drinking Water.

PubMed

Bandara, Gayan C; Heist, Christopher A; Remcho, Vincent T

2018-02-20

Copper is widely applied in industrial and technological applications and is an essential micronutrient for humans and animals. However, exposure to high environmental levels of copper, especially through drinking water, can lead to copper toxicity, resulting in severe acute and chronic health effects. Therefore, regular monitoring of aqueous copper ions has become necessary as recent anthropogenic activities have led to elevated environmental concentrations of copper. On-site monitoring processes require an inexpensive, simple, and portable analytical approach capable of generating reliable qualitative and quantitative data efficiently. Membrane-based lateral flow microfluidic devices are ideal candidates as they facilitate rapid, inexpensive, and portable measurements. Here we present a simple, chromatographic separation approach in combination with a visual detection method for Cu 2+ quantitation, performed in a lateral flow microfluidic channel. This method appreciably minimizes interferences by incorporating a nonspecific polymer inclusion membrane (PIM) based assay with a "dot-counting" approach to quantification. In this study, hydrophobic polycaprolactone (PCL)-filled glass microfiber (GMF) membranes were used as the base substrate onto which the PIM was evenly dispensed as an array of dots. The devices thus prepared were then selectively exposed to oxygen radicals through a mask to generate a hydrophilic surface path along which the sample was wicked. Using this approach, copper concentrations from 1 to 20 ppm were quantified from 5 μL samples using only visual observation of the assay device.

Iterative Integration of Visual Insights during Scalable Patent Search and Analysis.

PubMed

Koch, S; Bosch, H; Giereth, M; Ertl, T

2011-05-01

Patents are of growing importance in current economic markets. Analyzing patent information has, therefore, become a common task for many interest groups. As a prerequisite for patent analysis, extensive search for relevant patent information is essential. Unfortunately, the complexity of patent material inhibits a straightforward retrieval of all relevant patent documents and leads to iterative, time-consuming approaches in practice. Already the amount of patent data to be analyzed poses challenges with respect to scalability. Further scalability issues arise concerning the diversity of users and the large variety of analysis tasks. With "PatViz", a system for interactive analysis of patent information has been developed addressing scalability at various levels. PatViz provides a visual environment allowing for interactive reintegration of insights into subsequent search iterations, thereby bridging the gap between search and analytic processes. Because of its extensibility, we expect that the approach we have taken can be employed in different problem domains that require high quality of search results regarding their completeness.

The intracranial facial nerve as seen through different surgical windows: an extensive anatomosurgical study.

PubMed

Bernardo, Antonio; Evins, Alexander I; Visca, Anna; Stieg, Phillip E

2013-06-01

The facial nerve has a short intracranial course but crosses critical and frequently accessed surgical structures during cranial base surgery. When performing approaches to complex intracranial regions, it is essential to understand the nerve's conventional and topographic anatomy from different surgical perspectives as well as its relationship with surrounding structures. To describe the entire intracranial course of the facial nerve as observed via different neurosurgical approaches and to provide an analytical evaluation of the degree of nerve exposure achieved with each approach. Anterior petrosectomies (middle fossa, extended middle fossa), posterior petrosectomies (translabyrinthine, retrolabyrinthine, transcochlear), a retrosigmoid, a far lateral, and anterior transfacial (extended maxillectomy, mandibular swing) approaches were performed on 10 adult cadaveric heads (20 sides). The degree of facial nerve exposure achieved per segment for each approach was assessed and graded independently by 3 surgeons. The anterior petrosal approaches offered good visualization of the nerve in the cerebellopontine angle and intracanalicular portion superiorly, whereas the posterior petrosectomies provided more direct visualization without the need for cerebellar retraction. The far lateral approach exposed part of the posterior and the entire inferior quadrants, whereas the retrosigmoid approach exposed parts of the superior and inferior quadrants and the entire posterior quadrant. Anterior and anteroinferior exposure of the facial nerve was achieved via the transfacial approaches. The surgical route used must rely on the size, nature, and general location of the lesion, as well as on the capability of the particular approach to better expose the appropriate segment of the facial nerve.

Verification of Compartmental Epidemiological Models using Metamorphic Testing, Model Checking and Visual Analytics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ramanathan, Arvind; Steed, Chad A; Pullum, Laura L

Compartmental models in epidemiology are widely used as a means to model disease spread mechanisms and understand how one can best control the disease in case an outbreak of a widespread epidemic occurs. However, a significant challenge within the community is in the development of approaches that can be used to rigorously verify and validate these models. In this paper, we present an approach to rigorously examine and verify the behavioral properties of compartmen- tal epidemiological models under several common modeling scenarios including birth/death rates and multi-host/pathogen species. Using metamorphic testing, a novel visualization tool and model checking, we buildmore » a workflow that provides insights into the functionality of compartmental epidemiological models. Our initial results indicate that metamorphic testing can be used to verify the implementation of these models and provide insights into special conditions where these mathematical models may fail. The visualization front-end allows the end-user to scan through a variety of parameters commonly used in these models to elucidate the conditions under which an epidemic can occur. Further, specifying these models using a process algebra allows one to automatically construct behavioral properties that can be rigorously verified using model checking. Taken together, our approach allows for detecting implementation errors as well as handling conditions under which compartmental epidemiological models may fail to provide insights into disease spread dynamics.« less

Visual Analysis of Cloud Computing Performance Using Behavioral Lines.

PubMed

Muelder, Chris; Zhu, Biao; Chen, Wei; Zhang, Hongxin; Ma, Kwan-Liu

2016-02-29

Cloud computing is an essential technology to Big Data analytics and services. A cloud computing system is often comprised of a large number of parallel computing and storage devices. Monitoring the usage and performance of such a system is important for efficient operations, maintenance, and security. Tracing every application on a large cloud system is untenable due to scale and privacy issues. But profile data can be collected relatively efficiently by regularly sampling the state of the system, including properties such as CPU load, memory usage, network usage, and others, creating a set of multivariate time series for each system. Adequate tools for studying such large-scale, multidimensional data are lacking. In this paper, we present a visual based analysis approach to understanding and analyzing the performance and behavior of cloud computing systems. Our design is based on similarity measures and a layout method to portray the behavior of each compute node over time. When visualizing a large number of behavioral lines together, distinct patterns often appear suggesting particular types of performance bottleneck. The resulting system provides multiple linked views, which allow the user to interactively explore the data by examining the data or a selected subset at different levels of detail. Our case studies, which use datasets collected from two different cloud systems, show that this visual based approach is effective in identifying trends and anomalies of the systems.

Modernized Approach for Generating Reproducible Heterogeneity Using Transmitted-Light for Flow Visualization Experiments

NASA Astrophysics Data System (ADS)

Jones, A. A.; Holt, R. M.

2017-12-01

Image capturing in flow experiments has been used for fluid mechanics research since the early 1970s. Interactions of fluid flow between the vadose zone and permanent water table are of great interest because this zone is responsible for all recharge waters, pollutant transport and irrigation efficiency for agriculture. Griffith, et al. (2011) developed an approach where constructed reproducible "geologically realistic" sand configurations are deposited in sandfilled experimental chambers for light-transmitted flow visualization experiments. This method creates reproducible, reverse graded, layered (stratified) thin-slab sand chambers for point source experiments visualizing multiphase flow through porous media. Reverse-graded stratification of sand chambers mimic many naturally occurring sedimentary deposits. Sandfilled chambers use light as nonintrusive tools for measuring water saturation in two-dimensions (2-D). Homogeneous and heterogeneous sand configurations can be produced to visualize the complex physics of the unsaturated zone. The experimental procedure developed by Griffith, et al. (2011) was designed using now outdated and obsolete equipment. We have modernized this approach with new Parker Deadel linear actuator and programed projects/code for multiple configurations. We have also updated the Roper CCD software and image processing software with the latest in industry standards. Modernization of transmitted-light source, robotic equipment, redesigned experimental chambers, and newly developed analytical procedures have greatly reduced time and cost per experiment. We have verified the ability of the new equipment to generate reproducible heterogeneous sand-filled chambers and demonstrated the functionality of the new equipment and procedures by reproducing several gravity-driven fingering experiments conducted by Griffith (2008).

Teaching dermatoscopy of pigmented skin tumours to novices: comparison of analytic vs. heuristic approach.

PubMed

Tschandl, P; Kittler, H; Schmid, K; Zalaudek, I; Argenziano, G

2015-06-01

There are two strategies to approach the dermatoscopic diagnosis of pigmented skin tumours, namely the verbal-based analytic and the more visual-global heuristic method. It is not known if one or the other is more efficient in teaching dermatoscopy. To compare two teaching methods in short-term training of dermatoscopy to medical students. Fifty-seven medical students in the last year of the curriculum were given a 1-h lecture of either the heuristic- or the analytic-based teaching of dermatoscopy. Before and after this session, they were shown the same 50 lesions and asked to diagnose them and rate for chance of malignancy. Test lesions consisted of melanomas, basal cell carcinomas, nevi, seborrhoeic keratoses, benign vascular tumours and dermatofibromas. Performance measures were diagnostic accuracy regarding malignancy as measured by the area under the curves of receiver operating curves (range: 0-1), as well as per cent correct diagnoses (range: 0-100%). Diagnostic accuracy as well as per cent correct diagnoses increased by +0.21 and +32.9% (heuristic teaching) and +0.19 and +35.7% (analytic teaching) respectively (P for all <0.001). Neither for diagnostic accuracy (P = 0.585), nor for per cent correct diagnoses (P = 0.298) was a difference between the two groups. Short-term training of dermatoscopy to medical students allows significant improvement in diagnostic abilities. Choosing a heuristic or analytic method does not have an influence on this effect in short training using common pigmented skin lesions. © 2014 European Academy of Dermatology and Venereology.

Leveraging multidisciplinarity in a visual analytics graduate course.

PubMed

Elmqvist, Niklas; Ebert, David S

2012-01-01

Demand is growing in engineering, business, science, research, and industry for students with visual analytics expertise. However, teaching VA is challenging owing to the multidisciplinary nature of the topic, students' diverse backgrounds, and the corresponding requirements for instructors. This article reports best practices from a VA graduate course at Purdue University, where instructors leveraged these challenges to their advantage instead of trying to mitigate them.

Global processing takes time: A meta-analysis on local-global visual processing in ASD.

PubMed

Van der Hallen, Ruth; Evers, Kris; Brewaeys, Katrien; Van den Noortgate, Wim; Wagemans, Johan

2015-05-01

What does an individual with autism spectrum disorder (ASD) perceive first: the forest or the trees? In spite of 30 years of research and influential theories like the weak central coherence (WCC) theory and the enhanced perceptual functioning (EPF) account, the interplay of local and global visual processing in ASD remains only partly understood. Research findings vary in indicating a local processing bias or a global processing deficit, and often contradict each other. We have applied a formal meta-analytic approach and combined 56 articles that tested about 1,000 ASD participants and used a wide range of stimuli and tasks to investigate local and global visual processing in ASD. Overall, results show no enhanced local visual processing nor a deficit in global visual processing. Detailed analysis reveals a difference in the temporal pattern of the local-global balance, that is, slow global processing in individuals with ASD. Whereas task-dependent interaction effects are obtained, gender, age, and IQ of either participant groups seem to have no direct influence on performance. Based on the overview of the literature, suggestions are made for future research. (c) 2015 APA, all rights reserved).

Multi-scale modeling of solids as a composite of quantum mechanical (QM) and classical mechanical (CM) domains

NASA Astrophysics Data System (ADS)

Mallik, Aditi

2005-11-01

This thesis combines a creative project with an analytical discussion of that project. The creative project involves the creation of an approach to intermedia which I call "photonarrative." That is, photonarrative is a unique combining of various color photographs with quotations drawn from one particular work of literature. The work of literature to which the photographs are connected is not identified; nor do the photographs "illustrate" events from the work. In photonarrative, as I will show, the combining leads the viewer to a heightened level of interpretation and "seeing." The analytical portion of the thesis will include not only a recreation of the artistic process by which I designed and constructed this specific example of photonarrative, but also a theoretical consideration of the implications of this joining of the visual with the verbal for a general theory of aesthetics. In accordance with the interdisciplinary philosophy of the School of Arts and Humanities, this thesis involves a fusion of creative and critical acuity. The creative aspect involves a merging of verbal with visual art. The critical analysis touches on such disparate disciplines as literary theory, psychology, and aesthetics. (Abstract shortened by UMI.)

A Big Data and Learning Analytics Approach to Process-Level Feedback in Cognitive Simulations.

PubMed

Pecaric, Martin; Boutis, Kathy; Beckstead, Jason; Pusic, Martin

2017-02-01

Collecting and analyzing large amounts of process data for the purposes of education can be considered a big data/learning analytics (BD/LA) approach to improving learning. However, in the education of health care professionals, the application of BD/LA is limited to date. The authors discuss the potential advantages of the BD/LA approach for the process of learning via cognitive simulations. Using the lens of a cognitive model of radiograph interpretation with four phases (orientation, searching/scanning, feature detection, and decision making), they reanalyzed process data from a cognitive simulation of pediatric ankle radiography where 46 practitioners from three expertise levels classified 234 cases online. To illustrate the big data component, they highlight the data available in a digital environment (time-stamped, click-level process data). Learning analytics were illustrated using algorithmic computer-enabled approaches to process-level feedback.For each phase, the authors were able to identify examples of potentially useful BD/LA measures. For orientation, the trackable behavior of re-reviewing the clinical history was associated with increased diagnostic accuracy. For searching/scanning, evidence of skipping views was associated with an increased false-negative rate. For feature detection, heat maps overlaid on the radiograph can provide a metacognitive visualization of common novice errors. For decision making, the measured influence of sequence effects can reflect susceptibility to bias, whereas computer-generated path maps can provide insights into learners' diagnostic strategies.In conclusion, the augmented collection and dynamic analysis of learning process data within a cognitive simulation can improve feedback and prompt more precise reflection on a novice clinician's skill development.

Visualization Techniques for Computer Network Defense

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beaver, Justin M; Steed, Chad A; Patton, Robert M

2011-01-01

Effective visual analysis of computer network defense (CND) information is challenging due to the volume and complexity of both the raw and analyzed network data. A typical CND is comprised of multiple niche intrusion detection tools, each of which performs network data analysis and produces a unique alerting output. The state-of-the-practice in the situational awareness of CND data is the prevalent use of custom-developed scripts by Information Technology (IT) professionals to retrieve, organize, and understand potential threat events. We propose a new visual analytics framework, called the Oak Ridge Cyber Analytics (ORCA) system, for CND data that allows an operatormore » to interact with all detection tool outputs simultaneously. Aggregated alert events are presented in multiple coordinated views with timeline, cluster, and swarm model analysis displays. These displays are complemented with both supervised and semi-supervised machine learning classifiers. The intent of the visual analytics framework is to improve CND situational awareness, to enable an analyst to quickly navigate and analyze thousands of detected events, and to combine sophisticated data analysis techniques with interactive visualization such that patterns of anomalous activities may be more easily identified and investigated.« less

TopicLens: Efficient Multi-Level Visual Topic Exploration of Large-Scale Document Collections.

PubMed

Kim, Minjeong; Kang, Kyeongpil; Park, Deokgun; Choo, Jaegul; Elmqvist, Niklas

2017-01-01

Topic modeling, which reveals underlying topics of a document corpus, has been actively adopted in visual analytics for large-scale document collections. However, due to its significant processing time and non-interactive nature, topic modeling has so far not been tightly integrated into a visual analytics workflow. Instead, most such systems are limited to utilizing a fixed, initial set of topics. Motivated by this gap in the literature, we propose a novel interaction technique called TopicLens that allows a user to dynamically explore data through a lens interface where topic modeling and the corresponding 2D embedding are efficiently computed on the fly. To support this interaction in real time while maintaining view consistency, we propose a novel efficient topic modeling method and a semi-supervised 2D embedding algorithm. Our work is based on improving state-of-the-art methods such as nonnegative matrix factorization and t-distributed stochastic neighbor embedding. Furthermore, we have built a web-based visual analytics system integrated with TopicLens. We use this system to measure the performance and the visualization quality of our proposed methods. We provide several scenarios showcasing the capability of TopicLens using real-world datasets.

Visualizing molecular diffusion through passive permeability barriers in cells: conventional and novel approaches.

PubMed

Lin, Yu-Chun; Phua, Siew Cheng; Lin, Benjamin; Inoue, Takanari

2013-08-01

Diffusion barriers are universal solutions for cells to achieve distinct organizations, compositions, and activities within a limited space. The influence of diffusion barriers on the spatiotemporal dynamics of signaling molecules often determines cellular physiology and functions. Over the years, the passive permeability barriers in various subcellular locales have been characterized using elaborate analytical techniques. In this review, we will summarize the current state of knowledge on the various passive permeability barriers present in mammalian cells. We will conclude with a description of several conventional techniques and one new approach based on chemically inducible diffusion trap (CIDT) for probing permeable barriers. Copyright © 2013 Elsevier Ltd. All rights reserved.

"Dip-and-read" paper-based analytical devices using distance-based detection with color screening.

PubMed

Yamada, Kentaro; Citterio, Daniel; Henry, Charles S

2018-05-15

An improved paper-based analytical device (PAD) using color screening to enhance device performance is described. Current detection methods for PADs relying on the distance-based signalling motif can be slow due to the assay time being limited by capillary flow rates that wick fluid through the detection zone. For traditional distance-based detection motifs, analysis can take up to 45 min for a channel length of 5 cm. By using a color screening method, quantification with a distance-based PAD can be achieved in minutes through a "dip-and-read" approach. A colorimetric indicator line deposited onto a paper substrate using inkjet-printing undergoes a concentration-dependent colorimetric response for a given analyte. This color intensity-based response has been converted to a distance-based signal by overlaying a color filter with a continuous color intensity gradient matching the color of the developed indicator line. As a proof-of-concept, Ni quantification in welding fume was performed as a model assay. The results of multiple independent user testing gave mean absolute percentage error and average relative standard deviations of 10.5% and 11.2% respectively, which were an improvement over analysis based on simple visual color comparison with a read guide (12.2%, 14.9%). In addition to the analytical performance comparison, an interference study and a shelf life investigation were performed to further demonstrate practical utility. The developed system demonstrates an alternative detection approach for distance-based PADs enabling fast (∼10 min), quantitative, and straightforward assays.

Learning Visualization Strategies: A qualitative investigation

NASA Astrophysics Data System (ADS)

Halpern, Daniel; Oh, Kyong Eun; Tremaine, Marilyn; Chiang, James; Bemis, Karen; Silver, Deborah

2015-12-01

The following study investigates the range of strategies individuals develop to infer and interpret cross-sections of three-dimensional objects. We focus on the identification of mental representations and problem-solving processes made by 11 individuals with the goal of building training applications that integrate the strategies developed by the participants in our study. Our results suggest that although spatial transformation and perspective-taking techniques are useful for visualizing cross-section problems, these visual processes are augmented by analytical thinking. Further, our study shows that participants employ general analytic strategies for extended periods which evolve through practice into a set of progressively more expert strategies. Theoretical implications are discussed and five main findings are recommended for integration into the design of education software that facilitates visual learning and comprehension.

Toward a Shared Vocabulary for Visual Analysis: An Analytic Toolkit for Deconstructing the Visual Design of Graphic Novels

ERIC Educational Resources Information Center

Connors, Sean P.

2012-01-01

Literacy educators might advocate using graphic novels to develop students' visual literacy skills, but teachers who lack a vocabulary for engaging in close analysis of visual texts may be reluctant to teach them. Recognizing this, teacher educators should equip preservice teachers with a vocabulary for analyzing visual texts. This article…

Application of Andrew's Plots to Visualization of Multidimensional Data

ERIC Educational Resources Information Center

Grinshpun, Vadim

2016-01-01

Importance: The article raises a point of visual representation of big data, recently considered to be demanded for many scientific and real-life applications, and analyzes particulars for visualization of multi-dimensional data, giving examples of the visual analytics-related problems. Objectives: The purpose of this paper is to study application…

Integrated Circuits for Rapid Sample Processing and Electrochemical Detection of Biomarkers

NASA Astrophysics Data System (ADS)

Besant, Justin

The trade-off between speed and sensitivity of detection is a fundamental challenge in the design of point-of-care diagnostics. As the relevant molecules in many diseases exist natively at extremely low levels, many gold-standard diagnostic tests are designed with high sensitivity at the expense of long incubations needed to amplify the target analytes. The central aim of this thesis is to design new strategies to detect biologically relevant analytes with both high speed and sensitivity. The response time of a biosensor is limited by the ability of the target analyte to accumulate to detectable levels at the sensor surface. We overcome this limitation by designing a range of integrated devices to optimize the flux of the analyte to the sensor by increasing the effective analyte concentration, shortening the required diffusion distance, and confining the analyte in close proximity to the sensor. We couple these devices with novel ultrasensitive electrochemical transduction strategies to convert rare analytes into a detectable signal. We showcase the clinical utility of these approaches with several applications including cancer diagnosis, bacterial identification, and antibiotic susceptibility profiling. We design and optimize a device to isolate rare cancer cells from the bloodstream with near 100% efficiency and 10 000-fold specificity. We analyse pathogen specific nucleic acids by lysing bacteria in close proximity to an electrochemical sensor and find that this approach has 10-fold higher sensitivity than standard lysis in bulk solution. We design an electronic chip to readout the antibiotic susceptibility profile with an hour-long incubation by concentrating bacteria into nanoliter chambers with integrated electrodes. Finally, we report a strategy for ultrasensitive visual readout of nucleic acids as low as 100 fM within 10 minutes using an amplification cascade. The strategies presented could guide the development of fast, sensitive and low-cost diagnostics for diseases not previously detectable at the point-of-care.

A GIS-based decision support system for regional eco-security assessment and its application on the Tibetan Plateau.

PubMed

Xiaodan, Wang; Xianghao, Zhong; Pan, Gao

2010-10-01

Regional eco-security assessment is an intricate, challenging task. In previous studies, the integration of eco-environmental models and geographical information systems (GIS) usually takes two approaches: loose coupling and tight coupling. However, the present study used a full coupling approach to develop a GIS-based regional eco-security assessment decision support system (ESDSS). This was achieved by merging the pressure-state-response (PSR) model and the analytic hierarchy process (AHP) into ArcGIS 9 as a dynamic link library (DLL) using ArcObjects in ArcGIS and Visual Basic for Applications. Such an approach makes it easy to capitalize on the GIS visualization and spatial analysis functions, thereby significantly supporting the dynamic estimation of regional eco-security. A case study is presented for the Tibetan Plateau, known as the world's "third pole" after the Arctic and Antarctic. Results verified the usefulness and feasibility of the developed method. As a useful tool, the ESDSS can also help local managers to make scientifically-based and effective decisions about Tibetan eco-environmental protection and land use. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

[application of the analytical transmission electron microscopy techniques for detection, identification and visualization of localization of nanoparticles of titanium and cerium oxides in mammalian cells].

PubMed

Shebanova, A S; Bogdanov, A G; Ismagulova, T T; Feofanov, A V; Semenyuk, P I; Muronets, V I; Erokhina, M V; Onishchenko, G E; Kirpichnikov, M P; Shaitan, K V

2014-01-01

This work represents the results of the study on applicability of the modern methods of analytical transmission electron microscopy for detection, identification and visualization of localization of nanoparticles of titanium and cerium oxides in A549 cell, human lung adenocarcinoma cell line. A comparative analysis of images of the nanoparticles in the cells obtained in the bright field mode of transmission electron microscopy, under dark-field scanning transmission electron microscopy and high-angle annular dark field scanning transmission electron was performed. For identification of nanoparticles in the cells the analytical techniques, energy-dispersive X-ray spectroscopy and electron energy loss spectroscopy, were compared when used in the mode of obtaining energy spectrum from different particles and element mapping. It was shown that the method for electron tomography is applicable to confirm that nanoparticles are localized in the sample but not coated by contamination. The possibilities and fields of utilizing different techniques for analytical transmission electron microscopy for detection, visualization and identification of nanoparticles in the biological samples are discussed.

Next generation data harmonization

NASA Astrophysics Data System (ADS)

Armstrong, Chandler; Brown, Ryan M.; Chaves, Jillian; Czerniejewski, Adam; Del Vecchio, Justin; Perkins, Timothy K.; Rudnicki, Ron; Tauer, Greg

2015-05-01

Analysts are presented with a never ending stream of data sources. Often, subsets of data sources to solve problems are easily identified but the process to align data sets is time consuming. However, many semantic technologies do allow for fast harmonization of data to overcome these problems. These include ontologies that serve as alignment targets, visual tools and natural language processing that generate semantic graphs in terms of the ontologies, and analytics that leverage these graphs. This research reviews a developed prototype that employs all these approaches to perform analysis across disparate data sources documenting violent, extremist events.

A Strategy for Uncertainty Visualization Design

DTIC Science & Technology

2009-10-01

143–156, Magdeburg , Germany . [11] Thomson, J., Hetzler, E., MacEachren, A., Gahegan, M. and Pavel, M. (2005), A Typology for Visualizing Uncertainty...and Stasko [20] to bridge analytic gaps in visualization design, when tasks in the strategy overlap (and therefore complement) design frameworks

Urban Space Explorer: A Visual Analytics System for Urban Planning.

PubMed

Karduni, Alireza; Cho, Isaac; Wessel, Ginette; Ribarsky, William; Sauda, Eric; Dou, Wenwen

2017-01-01

Understanding people's behavior is fundamental to many planning professions (including transportation, community development, economic development, and urban design) that rely on data about frequently traveled routes, places, and social and cultural practices. Based on the results of a practitioner survey, the authors designed Urban Space Explorer, a visual analytics system that utilizes mobile social media to enable interactive exploration of public-space-related activity along spatial, temporal, and semantic dimensions.

2D-Visualization of metabolic activity with planar optical chemical sensors (optodes)

NASA Astrophysics Data System (ADS)

Meier, R. J.; Liebsch, G.

2015-12-01

Microbia plays an outstandingly important role in many hydrologic compartments, such as e.g. the benthic community in sediments, or biologically active microorganisms in the capillary fringe, in ground water, or soil. Oxygen, pH, and CO2 are key factors and indicators for microbial activity. They can be measured using optical chemical sensors. These sensors record changing fluorescence properties of specific indicator dyes. The signals can be measured in a non-contact mode, even through transparent walls, which is important for many lab-experiments. They can measure in closed (transparent) systems, without sampling or intruding into the sample. They do not consume the analytes while measuring, are fully reversible and able to measure in non-stirred solutions. These sensors can be applied as high precision fiberoptic sensors (for profiling), robust sensor spots, or as planar sensors for 2D visualization (imaging). Imaging enables to detect thousands of measurement spots at the same time and generate 2D analyte maps over a region of interest. It allows for comparing different regions within one recorded image, visualizing spatial analyte gradients, or more important to identify hot spots of metabolic activity. We present ready-to-use portable imaging systems for the analytes oxygen, pH, and CO2. They consist of a detector unit, planar sensor foils and a software for easy data recording and evaluation. Sensors foils for various analytes and measurement ranges enable visualizing metabolic activity or analyte changes in the desired range. Dynamics of metabolic activity can be detected in one shot or over long time periods. We demonstrate the potential of this analytical technique by presenting experiments on benthic disturbance-recovery dynamics in sediments and microbial degradation of organic material in the capillary fringe. We think this technique is a new tool to further understand how microbial and geochemical processes are linked in (not solely) hydrologic systems.

Text Stream Trend Analysis using Multiscale Visual Analytics with Applications to Social Media Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steed, Chad A; Beaver, Justin M; BogenII, Paul L.

In this paper, we introduce a new visual analytics system, called Matisse, that allows exploration of global trends in textual information streams with specific application to social media platforms. Despite the potential for real-time situational awareness using these services, interactive analysis of such semi-structured textual information is a challenge due to the high-throughput and high-velocity properties. Matisse addresses these challenges through the following contributions: (1) robust stream data management, (2) automated sen- timent/emotion analytics, (3) inferential temporal, geospatial, and term-frequency visualizations, and (4) a flexible drill-down interaction scheme that progresses from macroscale to microscale views. In addition to describing thesemore » contributions, our work-in-progress paper concludes with a practical case study focused on the analysis of Twitter 1% sample stream information captured during the week of the Boston Marathon bombings.« less

Modern data science for analytical chemical data - A comprehensive review.

PubMed

Szymańska, Ewa

2018-10-22

Efficient and reliable analysis of chemical analytical data is a great challenge due to the increase in data size, variety and velocity. New methodologies, approaches and methods are being proposed not only by chemometrics but also by other data scientific communities to extract relevant information from big datasets and provide their value to different applications. Besides common goal of big data analysis, different perspectives and terms on big data are being discussed in scientific literature and public media. The aim of this comprehensive review is to present common trends in the analysis of chemical analytical data across different data scientific fields together with their data type-specific and generic challenges. Firstly, common data science terms used in different data scientific fields are summarized and discussed. Secondly, systematic methodologies to plan and run big data analysis projects are presented together with their steps. Moreover, different analysis aspects like assessing data quality, selecting data pre-processing strategies, data visualization and model validation are considered in more detail. Finally, an overview of standard and new data analysis methods is provided and their suitability for big analytical chemical datasets shortly discussed. Copyright © 2018 Elsevier B.V. All rights reserved.

BiSet: Semantic Edge Bundling with Biclusters for Sensemaking.

PubMed

Sun, Maoyuan; Mi, Peng; North, Chris; Ramakrishnan, Naren

2016-01-01

Identifying coordinated relationships is an important task in data analytics. For example, an intelligence analyst might want to discover three suspicious people who all visited the same four cities. Existing techniques that display individual relationships, such as between lists of entities, require repetitious manual selection and significant mental aggregation in cluttered visualizations to find coordinated relationships. In this paper, we present BiSet, a visual analytics technique to support interactive exploration of coordinated relationships. In BiSet, we model coordinated relationships as biclusters and algorithmically mine them from a dataset. Then, we visualize the biclusters in context as bundled edges between sets of related entities. Thus, bundles enable analysts to infer task-oriented semantic insights about potentially coordinated activities. We make bundles as first class objects and add a new layer, "in-between", to contain these bundle objects. Based on this, bundles serve to organize entities represented in lists and visually reveal their membership. Users can interact with edge bundles to organize related entities, and vice versa, for sensemaking purposes. With a usage scenario, we demonstrate how BiSet supports the exploration of coordinated relationships in text analytics.

Iontophoresis and Flame Photometry: A Hybrid Interdisciplinary Experiment

ERIC Educational Resources Information Center

Sharp, Duncan; Cottam, Linzi; Bradley, Sarah; Brannigan, Jeanie; Davis, James

2010-01-01

The combination of reverse iontophoresis and flame photometry provides an engaging analytical experiment that gives first-year undergraduate students a flavor of modern drug delivery and analyte extraction techniques while reinforcing core analytical concepts. The experiment provides a highly visual demonstration of the iontophoresis technique and…

XCluSim: a visual analytics tool for interactively comparing multiple clustering results of bioinformatics data

PubMed Central

2015-01-01

Background Though cluster analysis has become a routine analytic task for bioinformatics research, it is still arduous for researchers to assess the quality of a clustering result. To select the best clustering method and its parameters for a dataset, researchers have to run multiple clustering algorithms and compare them. However, such a comparison task with multiple clustering results is cognitively demanding and laborious. Results In this paper, we present XCluSim, a visual analytics tool that enables users to interactively compare multiple clustering results based on the Visual Information Seeking Mantra. We build a taxonomy for categorizing existing techniques of clustering results visualization in terms of the Gestalt principles of grouping. Using the taxonomy, we choose the most appropriate interactive visualizations for presenting individual clustering results from different types of clustering algorithms. The efficacy of XCluSim is shown through case studies with a bioinformatician. Conclusions Compared to other relevant tools, XCluSim enables users to compare multiple clustering results in a more scalable manner. Moreover, XCluSim supports diverse clustering algorithms and dedicated visualizations and interactions for different types of clustering results, allowing more effective exploration of details on demand. Through case studies with a bioinformatics researcher, we received positive feedback on the functionalities of XCluSim, including its ability to help identify stably clustered items across multiple clustering results. PMID:26328893

Fusion Analytics: A Data Integration System for Public Health and Medical Disaster Response Decision Support

PubMed Central

Passman, Dina B.

2013-01-01

Objective The objective of this demonstration is to show conference attendees how they can integrate, analyze, and visualize diverse data type data from across a variety of systems by leveraging an off-the-shelf enterprise business intelligence (EBI) solution to support decision-making in disasters. Introduction Fusion Analytics is the data integration system developed by the Fusion Cell at the U.S. Department of Health and Human Services (HHS), Office of the Assistant Secretary for Preparedness and Response (ASPR). Fusion Analytics meaningfully augments traditional public and population health surveillance reporting by providing web-based data analysis and visualization tools. Methods Fusion Analytics serves as a one-stop-shop for the web-based data visualizations of multiple real-time data sources within ASPR. The 24-7 web availability makes it an ideal analytic tool for situational awareness and response allowing stakeholders to access the portal from any internet-enabled device without installing any software. The Fusion Analytics data integration system was built using off-the-shelf EBI software. Fusion Analytics leverages the full power of statistical analysis software and delivers reports to users in a secure web-based environment. Fusion Analytics provides an example of how public health staff can develop and deploy a robust public health informatics solution using an off-the shelf product and with limited development funding. It also provides the unique example of a public health information system that combines patient data for traditional disease surveillance with manpower and resource data to provide overall decision support for federal public health and medical disaster response operations. Conclusions We are currently in a unique position within public health. One the one hand, we have been gaining greater and greater access to electronic data of all kinds over the last few years. On the other, we are working in a time of reduced government spending to support leveraging this data for decision support with robust analytics and visualizations. Fusion Analytics provides an opportunity for attendees to see how various types of data are integrated into a single application for population health decision support. It also can provide them with ideas of how they can use their own staff to create analyses and reports that support their public health activities.

Visualization of the Mode Shapes of Pressure Oscillation in a Cylindrical Cavity

DOE Office of Scientific and Technical Information (OSTI.GOV)

He, Xin; Qi, Yunliang; Wang, Zhi

Our work describes a novel experimental method to visualize the mode shapes of pressure oscillation in a cylindrical cavity. Acoustic resonance in a cavity is a grand old problem that has been under investigation (using both analytical and numerical methods) for more than a century. In this article, a novel method based on high speed imaging of combustion chemiluminescence was presented to visualize the mode shapes of pressure oscillation in a cylindrical cavity. By generating high-temperature combustion gases and strong pressure waves simultaneously in a cylindrical cavity, the pressure oscillation can be inferred due to the chemiluminescence emissions of themore » combustion products. We can then visualized the mode shapes by reconstructing the images based on the amplitudes of the luminosity spectrum at the corresponding resonant frequencies. Up to 11 resonant mode shapes were clearly visualized, each matching very well with the analytical solutions.« less

Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases

PubMed Central

Satagopam, Venkata; Gu, Wei; Eifes, Serge; Gawron, Piotr; Ostaszewski, Marek; Gebel, Stephan; Barbosa-Silva, Adriano; Balling, Rudi; Schneider, Reinhard

2016-01-01

Abstract Translational medicine is a domain turning results of basic life science research into new tools and methods in a clinical environment, for example, as new diagnostics or therapies. Nowadays, the process of translation is supported by large amounts of heterogeneous data ranging from medical data to a whole range of -omics data. It is not only a great opportunity but also a great challenge, as translational medicine big data is difficult to integrate and analyze, and requires the involvement of biomedical experts for the data processing. We show here that visualization and interoperable workflows, combining multiple complex steps, can address at least parts of the challenge. In this article, we present an integrated workflow for exploring, analysis, and interpretation of translational medicine data in the context of human health. Three Web services—tranSMART, a Galaxy Server, and a MINERVA platform—are combined into one big data pipeline. Native visualization capabilities enable the biomedical experts to get a comprehensive overview and control over separate steps of the workflow. The capabilities of tranSMART enable a flexible filtering of multidimensional integrated data sets to create subsets suitable for downstream processing. A Galaxy Server offers visually aided construction of analytical pipelines, with the use of existing or custom components. A MINERVA platform supports the exploration of health and disease-related mechanisms in a contextualized analytical visualization system. We demonstrate the utility of our workflow by illustrating its subsequent steps using an existing data set, for which we propose a filtering scheme, an analytical pipeline, and a corresponding visualization of analytical results. The workflow is available as a sandbox environment, where readers can work with the described setup themselves. Overall, our work shows how visualization and interfacing of big data processing services facilitate exploration, analysis, and interpretation of translational medicine data. PMID:27441714

Modeling and evaluating user behavior in exploratory visual analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reda, Khairi; Johnson, Andrew E.; Papka, Michael E.

Empirical evaluation methods for visualizations have traditionally focused on assessing the outcome of the visual analytic process as opposed to characterizing how that process unfolds. There are only a handful of methods that can be used to systematically study how people use visualizations, making it difficult for researchers to capture and characterize the subtlety of cognitive and interaction behaviors users exhibit during visual analysis. To validate and improve visualization design, however, it is important for researchers to be able to assess and understand how users interact with visualization systems under realistic scenarios. This paper presents a methodology for modeling andmore » evaluating the behavior of users in exploratory visual analysis. We model visual exploration using a Markov chain process comprising transitions between mental, interaction, and computational states. These states and the transitions between them can be deduced from a variety of sources, including verbal transcripts, videos and audio recordings, and log files. This model enables the evaluator to characterize the cognitive and computational processes that are essential to insight acquisition in exploratory visual analysis, and reconstruct the dynamics of interaction between the user and the visualization system. We illustrate this model with two exemplar user studies, and demonstrate the qualitative and quantitative analytical tools it affords.« less

Visualizing Qualitative Information

ERIC Educational Resources Information Center

Slone, Debra J.

2009-01-01

The abundance of qualitative data in today's society and the need to easily scrutinize, digest, and share this information calls for effective visualization and analysis tools. Yet, no existing qualitative tools have the analytic power, visual effectiveness, and universality of familiar quantitative instruments like bar charts, scatter-plots, and…

Strategy for the elucidation of elemental compositions of trace analytes based on a mass resolution of 100,000 full width at half maximum.

PubMed

Kaufmann, Anton

2010-07-30

Elemental compositions (ECs) can be elucidated by evaluating the high-resolution mass spectra of unknown or suspected unfragmented analyte ions. Classical approaches utilize the exact mass of the monoisotopic peak (M + 0) and the relative abundance of isotope peaks (M + 1 and M + 2). The availability of high-resolution instruments like the Orbitrap currently permits mass resolutions up to 100,000 full width at half maximum. This not only allows the determination of relative isotopic abundances (RIAs), but also the extraction of other diagnostic information from the spectra, such as fully resolved signals originating from (34)S isotopes and fully or partially resolved signals related to (15)N isotopes (isotopic fine structure). Fully and partially resolved peaks can be evaluated by visual inspection of the measured peak profiles. This approach is shown to be capable of correctly discarding many of the EC candidates which were proposed by commercial EC calculating algorithms. Using this intuitive strategy significantly extends the upper mass range for the successful elucidation of ECs. Copyright 2010 John Wiley & Sons, Ltd.

The Mochi project: a field theory approach to plasma dynamics and self-organization

NASA Astrophysics Data System (ADS)

You, Setthivoine; von der Linden, Jens; Lavine, Eric Sander; Card, Alexander; Carroll, Evan

2016-10-01

The Mochi project is designed to study the interaction between plasma flows and magnetic fields from the point-of-view of canonical flux tubes. The Mochi Labjet experiment is being commissioned after achieving first plasma. Analytical and numerical tools are being developed to visualize canonical flux tubes. One analytical tool described here is a field theory approach to plasma dynamics and self-organization. A redefinition of the Lagrangian of a multi-particle system in fields reformulates the single-particle, kinetic, and fluid equations governing fluid and plasma dynamics as a single set of generalized Maxwell's equations and Ohm's law for canonical force-fields. The Lagrangian includes new terms representing the coupling between the motion of particle distributions, between distributions and electromagnetic fields, with relativistic contributions. The formulation shows that the concepts of self-organization and canonical helicity transport are applicable across single-particle, kinetic, and fluid regimes, at classical and relativistic scales. The theory gives the basis for comparing canonical helicity change to energy change in general systems. This work is supported by by US DOE Grant DE-SC0010340.

Empirical Analysis of the Subjective Impressions and Objective Measures of Domain Scientists’ Analytical Judgment Using Visualizations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dasgupta, Aritra; Burrows, Susannah M.; Han, Kyungsik

Scientists working in a particular domain often adhere to conventional data analysis and presentation methods and this leads to familiarity with these methods over time. But does high familiarity always lead to better analytical judgment? This question is especially relevant when visualizations are used in scientific tasks, as there can be discrepancies between visualization best practices and domain conventions. However, there is little empirical evidence of the relationships between scientists’ subjective impressions about familiar and unfamiliar visualizations and objective measures of their effect on scientific judgment. To address this gap and to study these factors, we focus on the climatemore » science domain, specifically on visualizations used for comparison of model performance. We present a comprehensive user study with 47 climate scientists where we explored the following factors: i) relationships between scientists’ familiarity, their perceived levels of com- fort, confidence, accuracy, and objective measures of accuracy, and ii) relationships among domain experience, visualization familiarity, and post-study preference.« less

Thinking graphically: Connecting vision and cognition during graph comprehension.

PubMed

Ratwani, Raj M; Trafton, J Gregory; Boehm-Davis, Deborah A

2008-03-01

Task analytic theories of graph comprehension account for the perceptual and conceptual processes required to extract specific information from graphs. Comparatively, the processes underlying information integration have received less attention. We propose a new framework for information integration that highlights visual integration and cognitive integration. During visual integration, pattern recognition processes are used to form visual clusters of information; these visual clusters are then used to reason about the graph during cognitive integration. In 3 experiments, the processes required to extract specific information and to integrate information were examined by collecting verbal protocol and eye movement data. Results supported the task analytic theories for specific information extraction and the processes of visual and cognitive integration for integrative questions. Further, the integrative processes scaled up as graph complexity increased, highlighting the importance of these processes for integration in more complex graphs. Finally, based on this framework, design principles to improve both visual and cognitive integration are described. PsycINFO Database Record (c) 2008 APA, all rights reserved

How I Learned to Swim: The Visual Journal as a Companion to Creative Inquiry

ERIC Educational Resources Information Center

Scott Shields, Sara

2016-01-01

In this paper, I discuss my engagement with a visual journal as a companion to creative research practice during my dissertation research. Grounded in arts based research methodologies; I explore visual journals in relationship to research, reflection and analytic processes. I begin with a discussion of the visual journal as an artifact of…

Spatial problem-solving strategies of middle school students: Wayfinding with geographic information systems

NASA Astrophysics Data System (ADS)

Wigglesworth, John C.

2000-06-01

Geographic Information Systems (GIS) is a powerful computer software package that emphasizes the use of maps and the management of spatially referenced environmental data archived in a systems data base. Professional applications of GIS have been in place since the 1980's, but only recently has GIS gained significant attention in the K--12 classroom. Students using GIS are able to manipulate and query data in order to solve all manners of spatial problems. Very few studies have examined how this technological innovation can support classroom learning. In particular, there has been little research on how experience in using the software correlates with a child's spatial cognition and his/her ability to understand spatial relationships. This study investigates the strategies used by middle school students to solve a wayfinding (route-finding) problem using the ArcView GIS software. The research design combined an individual background questionnaire, results from the Group Assessment of Logical Thinking (GALT) test, and analysis of reflective think-aloud sessions to define the characteristics of the strategies students' used to solve this particular class of spatial problem. Three uniquely different spatial problem solving strategies were identified. Visual/Concrete Wayfinders used a highly visual strategy; Logical/Abstract Wayfinders used GIS software tools to apply a more analytical and systematic approach; Transitional Wayfinders used an approach that showed evidence of one that was shifting from a visual strategy to one that was more analytical. The triangulation of data sources indicates that this progression of wayfinding strategy can be correlated both to Piagetian stages of logical thought and to experience with the use of maps. These findings suggest that GIS teachers must be aware that their students' performance will lie on a continuum that is based on cognitive development, spatial ability, and prior experience with maps. To be most effective, GIS teaching strategies and curriculum development should also represent a progression that correlates to the learners' current skills and experience.

Penetrating the Fog: Analytics in Learning and Education

ERIC Educational Resources Information Center

Siemens, George; Long, Phil

2011-01-01

Attempts to imagine the future of education often emphasize new technologies--ubiquitous computing devices, flexible classroom designs, and innovative visual displays. But the most dramatic factor shaping the future of higher education is something that people cannot actually touch or see: "big data and analytics." Learning analytics is still in…

Be the Data: Embodied Visual Analytics

ERIC Educational Resources Information Center

Chen, Xin; Self, Jessica Zeitz; House, Leanna; Wenskovitch, John; Sun, Maoyuan; Wycoff, Nathan; Evia, Jane Robertson; Leman, Scotland; North, Chris

2018-01-01

With the rise of big data, it is becoming increasingly important to educate groups of students at many educational levels about data analytics. In particular, students without a strong mathematical background may have an unenthusiastic attitude towards high-dimensional data and find it challenging to understand relevant complex analytical methods,…

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cook, Kris A.; Scholtz, Jean; Whiting, Mark A.

The VAST Challenge has been a popular venue for academic and industry participants for over ten years. Many participants comment that the majority of their time in preparing VAST Challenge entries is discovering elements in their software environments that need to be redesigned in order to solve the given task. Fortunately, there is no need to wait until the VAST Challenge is announced to test out software systems. The Visual Analytics Benchmark Repository contains all past VAST Challenge tasks, data, solutions and submissions. This paper details the various types of evaluations that may be conducted using the Repository information. Inmore » this paper we describe how developers can do informal evaluations of various aspects of their visual analytics environments using VAST Challenge information. Aspects that can be evaluated include the appropriateness of the software for various tasks, the various data types and formats that can be accommodated, the effectiveness and efficiency of the process supported by the software, and the intuitiveness of the visualizations and interactions. Researchers can compare their visualizations and interactions to those submitted to determine novelty. In addition, the paper provides pointers to various guidelines that software teams can use to evaluate the usability of their software. While these evaluations are not a replacement for formal evaluation methods, this information can be extremely useful during the development of visual analytics environments.« less

The Top 10 Challenges in Extreme-Scale Visual Analytics

PubMed Central

Wong, Pak Chung; Shen, Han-Wei; Johnson, Christopher R.; Chen, Chaomei; Ross, Robert B.

2013-01-01

In this issue of CG&A, researchers share their R&D findings and results on applying visual analytics (VA) to extreme-scale data. Having surveyed these articles and other R&D in this field, we’ve identified what we consider the top challenges of extreme-scale VA. To cater to the magazine’s diverse readership, our discussion evaluates challenges in all areas of the field, including algorithms, hardware, software, engineering, and social issues. PMID:24489426

Rational Design of Photonic Dust from Nanoporous Anodic Alumina Films: A Versatile Photonic Nanotool for Visual Sensing

PubMed Central

Chen, Yuting; Santos, Abel; Wang, Ye; Kumeria, Tushar; Ho, Daena; Li, Junsheng; Wang, Changhai; Losic, Dusan

2015-01-01

Herein, we present a systematic study on the development, optimisation and applicability of interferometrically coloured distributed Bragg reflectors based on nanoporous anodic alumina (NAA-DBRs) in the form of films and nanoporous microparticles as visual/colorimetric analytical tools. Firstly, we synthesise a complete palette of NAA-DBRs by galvanostatic pulse anodisation approach, in which the current density is altered in a periodic fashion in order to engineer the effective medium of the resulting photonic films in depth. NAA-DBR photonic films feature vivid colours that can be tuned across the UV-visible-NIR spectrum by structural engineering. Secondly, the effective medium of the resulting photonic films is assessed systematically by visual analysis and reflectometric interference spectroscopy (RIfS) in order to establish the most optimal nanoporous platforms to develop visual/colorimetric tools. Then, we demonstrate the applicability of NAA-DBR photonic films as a chemically selective sensing platform for visual detection of mercury(II) ions. Finally, we generate a new nanomaterial, so-called photonic dust, by breaking down NAA-DBRs films into nanoporous microparticles. The resulting microparticles (μP-NAA-DBRs) display vivid colours and are sensitive towards changes in their effective medium, opening new opportunities for developing advanced photonic nanotools for a broad range of applications. PMID:26245759

Rational Design of Photonic Dust from Nanoporous Anodic Alumina Films: A Versatile Photonic Nanotool for Visual Sensing

NASA Astrophysics Data System (ADS)

Chen, Yuting; Santos, Abel; Wang, Ye; Kumeria, Tushar; Ho, Daena; Li, Junsheng; Wang, Changhai; Losic, Dusan

2015-08-01

Herein, we present a systematic study on the development, optimisation and applicability of interferometrically coloured distributed Bragg reflectors based on nanoporous anodic alumina (NAA-DBRs) in the form of films and nanoporous microparticles as visual/colorimetric analytical tools. Firstly, we synthesise a complete palette of NAA-DBRs by galvanostatic pulse anodisation approach, in which the current density is altered in a periodic fashion in order to engineer the effective medium of the resulting photonic films in depth. NAA-DBR photonic films feature vivid colours that can be tuned across the UV-visible-NIR spectrum by structural engineering. Secondly, the effective medium of the resulting photonic films is assessed systematically by visual analysis and reflectometric interference spectroscopy (RIfS) in order to establish the most optimal nanoporous platforms to develop visual/colorimetric tools. Then, we demonstrate the applicability of NAA-DBR photonic films as a chemically selective sensing platform for visual detection of mercury(II) ions. Finally, we generate a new nanomaterial, so-called photonic dust, by breaking down NAA-DBRs films into nanoporous microparticles. The resulting microparticles (μP-NAA-DBRs) display vivid colours and are sensitive towards changes in their effective medium, opening new opportunities for developing advanced photonic nanotools for a broad range of applications.

Panoptes: web-based exploration of large scale genome variation data.

PubMed

Vauterin, Paul; Jeffery, Ben; Miles, Alistair; Amato, Roberto; Hart, Lee; Wright, Ian; Kwiatkowski, Dominic

2017-10-15

The size and complexity of modern large-scale genome variation studies demand novel approaches for exploring and sharing the data. In order to unlock the potential of these data for a broad audience of scientists with various areas of expertise, a unified exploration framework is required that is accessible, coherent and user-friendly. Panoptes is an open-source software framework for collaborative visual exploration of large-scale genome variation data and associated metadata in a web browser. It relies on technology choices that allow it to operate in near real-time on very large datasets. It can be used to browse rich, hybrid content in a coherent way, and offers interactive visual analytics approaches to assist the exploration. We illustrate its application using genome variation data of Anopheles gambiae, Plasmodium falciparum and Plasmodium vivax. Freely available at https://github.com/cggh/panoptes, under the GNU Affero General Public License. paul.vauterin@gmail.com. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Evaluation of unconfined-aquifer parameters from pumping test data by nonlinear least squares

NASA Astrophysics Data System (ADS)

Heidari, Manoutchehr; Wench, Allen

1997-05-01

Nonlinear least squares (NLS) with automatic differentiation was used to estimate aquifer parameters from drawdown data obtained from published pumping tests conducted in homogeneous, water-table aquifers. The method is based on a technique that seeks to minimize the squares of residuals between observed and calculated drawdown subject to bounds that are placed on the parameter of interest. The analytical model developed by Neuman for flow to a partially penetrating well of infinitesimal diameter situated in an infinite, homogeneous and anisotropic aquifer was used to obtain calculated drawdown. NLS was first applied to synthetic drawdown data from a hypothetical but realistic aquifer to demonstrate that the relevant hydraulic parameters (storativity, specific yield, and horizontal and vertical hydraulic conductivity) can be evaluated accurately. Next the method was used to estimate the parameters at three field sites with widely varying hydraulic properties. NLS produced unbiased estimates of the aquifer parameters that are close to the estimates obtained with the same data using a visual curve-matching approach. Small differences in the estimates are a consequence of subjective interpretation introduced in the visual approach.

Evaluation of unconfined-aquifer parameters from pumping test data by nonlinear least squares

USGS Publications Warehouse

Heidari, M.; Moench, A.

1997-01-01

Nonlinear least squares (NLS) with automatic differentiation was used to estimate aquifer parameters from drawdown data obtained from published pumping tests conducted in homogeneous, water-table aquifers. The method is based on a technique that seeks to minimize the squares of residuals between observed and calculated drawdown subject to bounds that are placed on the parameter of interest. The analytical model developed by Neuman for flow to a partially penetrating well of infinitesimal diameter situated in an infinite, homogeneous and anisotropic aquifer was used to obtain calculated drawdown. NLS was first applied to synthetic drawdown data from a hypothetical but realistic aquifer to demonstrate that the relevant hydraulic parameters (storativity, specific yield, and horizontal and vertical hydraulic conductivity) can be evaluated accurately. Next the method was used to estimate the parameters at three field sites with widely varying hydraulic properties. NLS produced unbiased estimates of the aquifer parameters that are close to the estimates obtained with the same data using a visual curve-matching approach. Small differences in the estimates are a consequence of subjective interpretation introduced in the visual approach.

EINVis: a visualization tool for analyzing and exploring genetic interactions in large-scale association studies.

PubMed

Wu, Yubao; Zhu, Xiaofeng; Chen, Jian; Zhang, Xiang

2013-11-01

Epistasis (gene-gene interaction) detection in large-scale genetic association studies has recently drawn extensive research interests as many complex traits are likely caused by the joint effect of multiple genetic factors. The large number of possible interactions poses both statistical and computational challenges. A variety of approaches have been developed to address the analytical challenges in epistatic interaction detection. These methods usually output the identified genetic interactions and store them in flat file formats. It is highly desirable to develop an effective visualization tool to further investigate the detected interactions and unravel hidden interaction patterns. We have developed EINVis, a novel visualization tool that is specifically designed to analyze and explore genetic interactions. EINVis displays interactions among genetic markers as a network. It utilizes a circular layout (specially, a tree ring view) to simultaneously visualize the hierarchical interactions between single nucleotide polymorphisms (SNPs), genes, and chromosomes, and the network structure formed by these interactions. Using EINVis, the user can distinguish marginal effects from interactions, track interactions involving more than two markers, visualize interactions at different levels, and detect proxy SNPs based on linkage disequilibrium. EINVis is an effective and user-friendly free visualization tool for analyzing and exploring genetic interactions. It is publicly available with detailed documentation and online tutorial on the web at http://filer.case.edu/yxw407/einvis/. © 2013 WILEY PERIODICALS, INC.

A Theory of Control for a Class of Electronic Power Processing Systems: Energy-Storage DC-To-DC Converters. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Burns, W. W., III

1977-01-01

An analytically derived approach to the control of energy-storage dc-to-dc converters, which enables improved system performance and an extensive understanding of the manner in which this improved performance is accomplished, is presented. The control approach is derived from a state-plane analysis of dc-to-dc converter power stages which enables a graphical visualization of the movement of the system state during both steady state and transient operation. This graphical representation of the behavior of dc-to-dc converter systems yields considerable qualitative insight into the cause and effect relationships which exist between various commonly used converter control functions and the system performance which results from them.

The LifeWatch approach to the exploration of distributed species information

PubMed Central

Fuentes, Daniel; Fiore, Nicola

2014-01-01

Abstract This paper introduces a new method of automatically extracting, integrating and presenting information regarding species from the most relevant online taxonomic resources. First, the information is extracted and joined using data wrappers and integration solutions. Then, an analytical tool is used to provide a visual representation of the data. The information is then integrated into a user friendly content management system. The proposal has been implemented using data from the Global Biodiversity Information Facility (GBIF), the Catalogue of Life (CoL), the World Register of Marine Species (WoRMS), the Integrated Taxonomic Information System (ITIS) and the Global Names Index (GNI). The approach improves data quality, avoiding taxonomic and nomenclature errors whilst increasing the availability and accessibility of the information. PMID:25589865

Prototyping Visual Learning Analytics Guided by an Educational Theory Informed Goal

ERIC Educational Resources Information Center

Hillaire, Garron; Rappolt-Schlichtmann, Gabrielle; Ducharme, Kim

2016-01-01

Prototype work can support the creation of data visualizations throughout the research and development process through paper prototypes with sketching, designed prototypes with graphic design tools, and functional prototypes to explore how the implementation will work. One challenging aspect of data visualization work is coordinating the expertise…

Encounter Detection Using Visual Analytics to Improve Maritime Domain Awareness

DTIC Science & Technology

2015-06-01

assigned to be processed in a record set consisting of all the records within a one degree of latitude by one degree of longitude square box. For the case...0.002 3 30 185 0.001 4 30 370 0.002 37 a degree of latitude by a tenth of a degree of longitude . This prototype further reduces the processing ...STATEMENT Approved for public release; distribution is unlimited 12b. DISTRIBUTION CODE 13. ABSTRACT (maximum 200 words) A visual analytics process

PlanetSense: A Real-time Streaming and Spatio-temporal Analytics Platform for Gathering Geo-spatial Intelligence from Open Source Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thakur, Gautam S; Bhaduri, Budhendra L; Piburn, Jesse O

Geospatial intelligence has traditionally relied on the use of archived and unvarying data for planning and exploration purposes. In consequence, the tools and methods that are architected to provide insight and generate projections only rely on such datasets. Albeit, if this approach has proven effective in several cases, such as land use identification and route mapping, it has severely restricted the ability of researchers to inculcate current information in their work. This approach is inadequate in scenarios requiring real-time information to act and to adjust in ever changing dynamic environments, such as evacuation and rescue missions. In this work, wemore » propose PlanetSense, a platform for geospatial intelligence that is built to harness the existing power of archived data and add to that, the dynamics of real-time streams, seamlessly integrated with sophisticated data mining algorithms and analytics tools for generating operational intelligence on the fly. The platform has four main components i) GeoData Cloud a data architecture for storing and managing disparate datasets; ii) Mechanism to harvest real-time streaming data; iii) Data analytics framework; iv) Presentation and visualization through web interface and RESTful services. Using two case studies, we underpin the necessity of our platform in modeling ambient population and building occupancy at scale.« less

Planetary Surface Visualization and Analytics

NASA Astrophysics Data System (ADS)

Law, E. S.; Solar System Treks Team

2018-04-01

An introduction and update of the Solar System Treks Project which provides a suite of interactive visualization and analysis tools to enable users (engineers, scientists, public) to access large amounts of mapped planetary data products.

Analytic information processing style in epilepsy patients.

PubMed

Buonfiglio, Marzia; Di Sabato, Francesco; Mandillo, Silvia; Albini, Mariarita; Di Bonaventura, Carlo; Giallonardo, Annateresa; Avanzini, Giuliano

2017-08-01

Relevant to the study of epileptogenesis is learning processing, given the pivotal role that neuroplasticity assumes in both mechanisms. Recently, evoked potential analyses showed a link between analytic cognitive style and altered neural excitability in both migraine and healthy subjects, regardless of cognitive impairment or psychological disorders. In this study we evaluated analytic/global and visual/auditory perceptual dimensions of cognitive style in patients with epilepsy. Twenty-five cryptogenic temporal lobe epilepsy (TLE) patients matched with 25 idiopathic generalized epilepsy (IGE) sufferers and 25 healthy volunteers were recruited and participated in three cognitive style tests: "Sternberg-Wagner Self-Assessment Inventory", the C. Cornoldi test series called AMOS, and the Mariani Learning style Questionnaire. Our results demonstrate a significant association between analytic cognitive style and both IGE and TLE and respectively a predominant auditory and visual analytic style (ANOVA: p values <0,0001). These findings should encourage further research to investigate information processing style and its neurophysiological correlates in epilepsy. Copyright © 2017 Elsevier Inc. All rights reserved.

Oak Ridge Bio-surveillance Toolkit (ORBiT): Integrating Big-Data Analytics with Visual Analysis for Public Health Dynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ramanathan, Arvind; Pullum, Laura L; Steed, Chad A

In this position paper, we describe the design and implementation of the Oak Ridge Bio-surveillance Toolkit (ORBiT): a collection of novel statistical and machine learning tools implemented for (1) integrating heterogeneous traditional (e.g. emergency room visits, prescription sales data, etc.) and non-traditional (social media such as Twitter and Instagram) data sources, (2) analyzing large-scale datasets and (3) presenting the results from the analytics as a visual interface for the end-user to interact and provide feedback. We present examples of how ORBiT can be used to summarize ex- tremely large-scale datasets effectively and how user interactions can translate into the datamore » analytics process for bio-surveillance. We also present a strategy to estimate parameters relevant to dis- ease spread models from near real time data feeds and show how these estimates can be integrated with disease spread models for large-scale populations. We conclude with a perspective on how integrating data and visual analytics could lead to better forecasting and prediction of disease spread as well as improved awareness of disease susceptible regions.« less

Data analytics and parallel-coordinate materials property charts

NASA Astrophysics Data System (ADS)

Rickman, Jeffrey M.

2018-01-01

It is often advantageous to display material properties relationships in the form of charts that highlight important correlations and thereby enhance our understanding of materials behavior and facilitate materials selection. Unfortunately, in many cases, these correlations are highly multidimensional in nature, and one typically employs low-dimensional cross-sections of the property space to convey some aspects of these relationships. To overcome some of these difficulties, in this work we employ methods of data analytics in conjunction with a visualization strategy, known as parallel coordinates, to represent better multidimensional materials data and to extract useful relationships among properties. We illustrate the utility of this approach by the construction and systematic analysis of multidimensional materials properties charts for metallic and ceramic systems. These charts simplify the description of high-dimensional geometry, enable dimensional reduction and the identification of significant property correlations and underline distinctions among different materials classes.

Dual-domain mass-transfer parameters from electrical hysteresis: theory and analytical approach applied to laboratory, synthetic streambed, and groundwater experiments

USGS Publications Warehouse

Briggs, Martin A.; Day-Lewis, Frederick D.; Ong, John B.; Harvey, Judson W.; Lane, John W.

2014-01-01

Models of dual-domain mass transfer (DDMT) are used to explain anomalous aquifer transport behavior such as the slow release of contamination and solute tracer tailing. Traditional tracer experiments to characterize DDMT are performed at the flow path scale (meters), which inherently incorporates heterogeneous exchange processes; hence, estimated “effective” parameters are sensitive to experimental design (i.e., duration and injection velocity). Recently, electrical geophysical methods have been used to aid in the inference of DDMT parameters because, unlike traditional fluid sampling, electrical methods can directly sense less-mobile solute dynamics and can target specific points along subsurface flow paths. Here we propose an analytical framework for graphical parameter inference based on a simple petrophysical model explaining the hysteretic relation between measurements of bulk and fluid conductivity arising in the presence of DDMT at the local scale. Analysis is graphical and involves visual inspection of hysteresis patterns to (1) determine the size of paired mobile and less-mobile porosities and (2) identify the exchange rate coefficient through simple curve fitting. We demonstrate the approach using laboratory column experimental data, synthetic streambed experimental data, and field tracer-test data. Results from the analytical approach compare favorably with results from calibration of numerical models and also independent measurements of mobile and less-mobile porosity. We show that localized electrical hysteresis patterns resulting from diffusive exchange are independent of injection velocity, indicating that repeatable parameters can be extracted under varied experimental designs, and these parameters represent the true intrinsic properties of specific volumes of porous media of aquifers and hyporheic zones.

Dual-domain mass-transfer parameters from electrical hysteresis: Theory and analytical approach applied to laboratory, synthetic streambed, and groundwater experiments

NASA Astrophysics Data System (ADS)

Briggs, Martin A.; Day-Lewis, Frederick D.; Ong, John B.; Harvey, Judson W.; Lane, John W.

2014-10-01

Models of dual-domain mass transfer (DDMT) are used to explain anomalous aquifer transport behavior such as the slow release of contamination and solute tracer tailing. Traditional tracer experiments to characterize DDMT are performed at the flow path scale (meters), which inherently incorporates heterogeneous exchange processes; hence, estimated "effective" parameters are sensitive to experimental design (i.e., duration and injection velocity). Recently, electrical geophysical methods have been used to aid in the inference of DDMT parameters because, unlike traditional fluid sampling, electrical methods can directly sense less-mobile solute dynamics and can target specific points along subsurface flow paths. Here we propose an analytical framework for graphical parameter inference based on a simple petrophysical model explaining the hysteretic relation between measurements of bulk and fluid conductivity arising in the presence of DDMT at the local scale. Analysis is graphical and involves visual inspection of hysteresis patterns to (1) determine the size of paired mobile and less-mobile porosities and (2) identify the exchange rate coefficient through simple curve fitting. We demonstrate the approach using laboratory column experimental data, synthetic streambed experimental data, and field tracer-test data. Results from the analytical approach compare favorably with results from calibration of numerical models and also independent measurements of mobile and less-mobile porosity. We show that localized electrical hysteresis patterns resulting from diffusive exchange are independent of injection velocity, indicating that repeatable parameters can be extracted under varied experimental designs, and these parameters represent the true intrinsic properties of specific volumes of porous media of aquifers and hyporheic zones.

SensePath: Understanding the Sensemaking Process Through Analytic Provenance.

PubMed

Nguyen, Phong H; Xu, Kai; Wheat, Ashley; Wong, B L William; Attfield, Simon; Fields, Bob

2016-01-01

Sensemaking is described as the process of comprehension, finding meaning and gaining insight from information, producing new knowledge and informing further action. Understanding the sensemaking process allows building effective visual analytics tools to make sense of large and complex datasets. Currently, it is often a manual and time-consuming undertaking to comprehend this: researchers collect observation data, transcribe screen capture videos and think-aloud recordings, identify recurring patterns, and eventually abstract the sensemaking process into a general model. In this paper, we propose a general approach to facilitate such a qualitative analysis process, and introduce a prototype, SensePath, to demonstrate the application of this approach with a focus on browser-based online sensemaking. The approach is based on a study of a number of qualitative research sessions including observations of users performing sensemaking tasks and post hoc analyses to uncover their sensemaking processes. Based on the study results and a follow-up participatory design session with HCI researchers, we decided to focus on the transcription and coding stages of thematic analysis. SensePath automatically captures user's sensemaking actions, i.e., analytic provenance, and provides multi-linked views to support their further analysis. A number of other requirements elicited from the design session are also implemented in SensePath, such as easy integration with existing qualitative analysis workflow and non-intrusive for participants. The tool was used by an experienced HCI researcher to analyze two sensemaking sessions. The researcher found the tool intuitive and considerably reduced analysis time, allowing better understanding of the sensemaking process.

The VAST Challenge: History, Scope, and Outcomes: An introduction to the Special Issue

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cook, Kristin A.; Grinstein, Georges; Whiting, Mark A.

2014-10-01

Visual analytics aims to facilitate human insight from complex data via a combination of visual representations, interaction techniques, and supporting algorithms. To create new tools and techniques that achieve this goal requires that researchers have an understanding of analytical questions to be addressed, data that illustrates the complexities and ambiguities found in realistic analytic settings, and methods for evaluating whether the plausible insights are gained through use of the new methods. However, researchers do not, generally speaking, have access to analysts who can articulate their problems or operational data that is used for analysis. To fill this gap, the Visualmore » Analytics Science and Technology (VAST) Challenge has been held annually since 2006. The VAST Challenge provides an opportunity for researchers to experiment with realistic but not real problems, using realistic synthetic data with known events embedded. Since its inception, the VAST Challenge has evolved along with the visual analytics research community to pose more complex challenges, ranging from text analysis to video analysis to large scale network log analysis. The seven years of the VAST Challenge have seen advancements in research and development, education, evaluation, and in the challenge process itself. This special issue of Information Visualization highlights some of the noteworthy advancements in each of these areas. Some of these papers focus on important research questions related to the challenge itself, and other papers focus on innovative research that has been shaped by participation in the challenge. This paper describes the VAST Challenge process and benefits in detail. It also provides an introduction to and context for the remaining papers in the issue.« less

Big Data Geo-Analytical Tool Development for Spatial Analysis Uncertainty Visualization and Quantification Needs

NASA Astrophysics Data System (ADS)

Rose, K.; Bauer, J. R.; Baker, D. V.

2015-12-01

As big data computing capabilities are increasingly paired with spatial analytical tools and approaches, there is a need to ensure uncertainty associated with the datasets used in these analyses is adequately incorporated and portrayed in results. Often the products of spatial analyses, big data and otherwise, are developed using discontinuous, sparse, and often point-driven data to represent continuous phenomena. Results from these analyses are generally presented without clear explanations of the uncertainty associated with the interpolated values. The Variable Grid Method (VGM) offers users with a flexible approach designed for application to a variety of analyses where users there is a need to study, evaluate, and analyze spatial trends and patterns while maintaining connection to and communicating the uncertainty in the underlying spatial datasets. The VGM outputs a simultaneous visualization representative of the spatial data analyses and quantification of underlying uncertainties, which can be calculated using data related to sample density, sample variance, interpolation error, uncertainty calculated from multiple simulations. In this presentation we will show how we are utilizing Hadoop to store and perform spatial analysis through the development of custom Spark and MapReduce applications that incorporate ESRI Hadoop libraries. The team will present custom 'Big Data' geospatial applications that run on the Hadoop cluster and integrate with ESRI ArcMap with the team's probabilistic VGM approach. The VGM-Hadoop tool has been specially built as a multi-step MapReduce application running on the Hadoop cluster for the purpose of data reduction. This reduction is accomplished by generating multi-resolution, non-overlapping, attributed topology that is then further processed using ESRI's geostatistical analyst to convey a probabilistic model of a chosen study region. Finally, we will share our approach for implementation of data reduction and topology generation via custom multi-step Hadoop applications, performance benchmarking comparisons, and Hadoop-centric opportunities for greater parallelization of geospatial operations. The presentation includes examples of the approach being applied to a range of subsurface, geospatial studies (e.g. induced seismicity risk).

Decision exploration lab: a visual analytics solution for decision management.

PubMed

Broeksema, Bertjan; Baudel, Thomas; Telea, Arthur G; Crisafulli, Paolo

2013-12-01

We present a visual analytics solution designed to address prevalent issues in the area of Operational Decision Management (ODM). In ODM, which has its roots in Artificial Intelligence (Expert Systems) and Management Science, it is increasingly important to align business decisions with business goals. In our work, we consider decision models (executable models of the business domain) as ontologies that describe the business domain, and production rules that describe the business logic of decisions to be made over this ontology. Executing a decision model produces an accumulation of decisions made over time for individual cases. We are interested, first, to get insight in the decision logic and the accumulated facts by themselves. Secondly and more importantly, we want to see how the accumulated facts reveal potential divergences between the reality as captured by the decision model, and the reality as captured by the executed decisions. We illustrate the motivation, added value for visual analytics, and our proposed solution and tooling through a business case from the car insurance industry.

MeRy-B: a web knowledgebase for the storage, visualization, analysis and annotation of plant NMR metabolomic profiles

PubMed Central

2011-01-01

Background Improvements in the techniques for metabolomics analyses and growing interest in metabolomic approaches are resulting in the generation of increasing numbers of metabolomic profiles. Platforms are required for profile management, as a function of experimental design, and for metabolite identification, to facilitate the mining of the corresponding data. Various databases have been created, including organism-specific knowledgebases and analytical technique-specific spectral databases. However, there is currently no platform meeting the requirements for both profile management and metabolite identification for nuclear magnetic resonance (NMR) experiments. Description MeRy-B, the first platform for plant 1H-NMR metabolomic profiles, is designed (i) to provide a knowledgebase of curated plant profiles and metabolites obtained by NMR, together with the corresponding experimental and analytical metadata, (ii) for queries and visualization of the data, (iii) to discriminate between profiles with spectrum visualization tools and statistical analysis, (iv) to facilitate compound identification. It contains lists of plant metabolites and unknown compounds, with information about experimental conditions, the factors studied and metabolite concentrations for several plant species, compiled from more than one thousand annotated NMR profiles for various organs or tissues. Conclusion MeRy-B manages all the data generated by NMR-based plant metabolomics experiments, from description of the biological source to identification of the metabolites and determinations of their concentrations. It is the first database allowing the display and overlay of NMR metabolomic profiles selected through queries on data or metadata. MeRy-B is available from http://www.cbib.u-bordeaux2.fr/MERYB/index.php. PMID:21668943

The Preference of Visualization in Teaching and Learning Absolute Value

ERIC Educational Resources Information Center

Konyalioglu, Alper Cihan; Aksu, Zeki; Senel, Esma Ozge

2012-01-01

Visualization is mostly despised although it complements and--sometimes--guides the analytical process. This study mainly investigates teachers' preferences concerning the use of the visualization method and determines the extent to which they encourage their students to make use of it within the problem-solving process. This study was conducted…

DIA2: Web-based Cyberinfrastructure for Visual Analysis of Funding Portfolios.

PubMed

Madhavan, Krishna; Elmqvist, Niklas; Vorvoreanu, Mihaela; Chen, Xin; Wong, Yuetling; Xian, Hanjun; Dong, Zhihua; Johri, Aditya

2014-12-01

We present a design study of the Deep Insights Anywhere, Anytime (DIA2) platform, a web-based visual analytics system that allows program managers and academic staff at the U.S. National Science Foundation to search, view, and analyze their research funding portfolio. The goal of this system is to facilitate users' understanding of both past and currently active research awards in order to make more informed decisions of their future funding. This user group is characterized by high domain expertise yet not necessarily high literacy in visualization and visual analytics-they are essentially casual experts-and thus require careful visual and information design, including adhering to user experience standards, providing a self-instructive interface, and progressively refining visualizations to minimize complexity. We discuss the challenges of designing a system for casual experts and highlight how we addressed this issue by modeling the organizational structure and workflows of the NSF within our system. We discuss each stage of the design process, starting with formative interviews, prototypes, and finally live deployments and evaluation with stakeholders.

Real-Time Visualization of Network Behaviors for Situational Awareness

DOE Office of Scientific and Technical Information (OSTI.GOV)

Best, Daniel M.; Bohn, Shawn J.; Love, Douglas V.

Plentiful, complex, and dynamic data make understanding the state of an enterprise network difficult. Although visualization can help analysts understand baseline behaviors in network traffic and identify off-normal events, visual analysis systems often do not scale well to operational data volumes (in the hundreds of millions to billions of transactions per day) nor to analysis of emergent trends in real-time data. We present a system that combines multiple, complementary visualization techniques coupled with in-stream analytics, behavioral modeling of network actors, and a high-throughput processing platform called MeDICi. This system provides situational understanding of real-time network activity to help analysts takemore » proactive response steps. We have developed these techniques using requirements gathered from the government users for which the tools are being developed. By linking multiple visualization tools to a streaming analytic pipeline, and designing each tool to support a particular kind of analysis (from high-level awareness to detailed investigation), analysts can understand the behavior of a network across multiple levels of abstraction.« less

ProteoLens: a visual analytic tool for multi-scale database-driven biological network data mining.

PubMed

Huan, Tianxiao; Sivachenko, Andrey Y; Harrison, Scott H; Chen, Jake Y

2008-08-12

New systems biology studies require researchers to understand how interplay among myriads of biomolecular entities is orchestrated in order to achieve high-level cellular and physiological functions. Many software tools have been developed in the past decade to help researchers visually navigate large networks of biomolecular interactions with built-in template-based query capabilities. To further advance researchers' ability to interrogate global physiological states of cells through multi-scale visual network explorations, new visualization software tools still need to be developed to empower the analysis. A robust visual data analysis platform driven by database management systems to perform bi-directional data processing-to-visualizations with declarative querying capabilities is needed. We developed ProteoLens as a JAVA-based visual analytic software tool for creating, annotating and exploring multi-scale biological networks. It supports direct database connectivity to either Oracle or PostgreSQL database tables/views, on which SQL statements using both Data Definition Languages (DDL) and Data Manipulation languages (DML) may be specified. The robust query languages embedded directly within the visualization software help users to bring their network data into a visualization context for annotation and exploration. ProteoLens supports graph/network represented data in standard Graph Modeling Language (GML) formats, and this enables interoperation with a wide range of other visual layout tools. The architectural design of ProteoLens enables the de-coupling of complex network data visualization tasks into two distinct phases: 1) creating network data association rules, which are mapping rules between network node IDs or edge IDs and data attributes such as functional annotations, expression levels, scores, synonyms, descriptions etc; 2) applying network data association rules to build the network and perform the visual annotation of graph nodes and edges according to associated data values. We demonstrated the advantages of these new capabilities through three biological network visualization case studies: human disease association network, drug-target interaction network and protein-peptide mapping network. The architectural design of ProteoLens makes it suitable for bioinformatics expert data analysts who are experienced with relational database management to perform large-scale integrated network visual explorations. ProteoLens is a promising visual analytic platform that will facilitate knowledge discoveries in future network and systems biology studies.

A visual identification key utilizing both gestalt and analytic approaches to identification of Carices present in North America (Plantae, Cyperaceae)

PubMed Central

2013-01-01

Abstract Images are a critical part of the identification process because they enable direct, immediate and relatively unmediated comparisons between a specimen being identified and one or more reference specimens. The Carices Interactive Visual Identification Key (CIVIK) is a novel tool for identification of North American Carex species, the largest vascular plant genus in North America, and two less numerous closely-related genera, Cymophyllus and Kobresia. CIVIK incorporates 1288 high-resolution tiled image sets that allow users to zoom in to view minute structures that are crucial at times for identification in these genera. Morphological data are derived from the earlier Carex Interactive Identification Key (CIIK) which in turn used data from the Flora of North America treatments. In this new iteration, images can be viewed in a grid or histogram format, allowing multiple representations of data. In both formats the images are fully zoomable. PMID:24723777

HitWalker2: visual analytics for precision medicine and beyond.

PubMed

Bottomly, Daniel; McWeeney, Shannon K; Wilmot, Beth

2016-04-15

The lack of visualization frameworks to guide interpretation and facilitate discovery is a potential bottleneck for precision medicine, systems genetics and other studies. To address this we have developed an interactive, reproducible, web-based prioritization approach that builds on our earlier work. HitWalker2 is highly flexible and can utilize many data types and prioritization methods based upon available data and desired questions, allowing it to be utilized in a diverse range of studies such as cancer, infectious disease and psychiatric disorders. Source code is freely available at https://github.com/biodev/HitWalker2 and implemented using Python/Django, Neo4j and Javascript (D3.js and jQuery). We support major open source browsers (e.g. Firefox and Chromium/Chrome). wilmotb@ohsu.edu Supplementary data are available at Bioinformatics online. Additional information/instructions are available at https://github.com/biodev/HitWalker2/wiki. © The Author 2015. Published by Oxford University Press.

Multi-objective evolutionary optimization for constructing neural networks for virtual reality visual data mining: application to geophysical prospecting.

PubMed

Valdés, Julio J; Barton, Alan J

2007-05-01

A method for the construction of virtual reality spaces for visual data mining using multi-objective optimization with genetic algorithms on nonlinear discriminant (NDA) neural networks is presented. Two neural network layers (the output and the last hidden) are used for the construction of simultaneous solutions for: (i) a supervised classification of data patterns and (ii) an unsupervised similarity structure preservation between the original data matrix and its image in the new space. A set of spaces are constructed from selected solutions along the Pareto front. This strategy represents a conceptual improvement over spaces computed by single-objective optimization. In addition, genetic programming (in particular gene expression programming) is used for finding analytic representations of the complex mappings generating the spaces (a composition of NDA and orthogonal principal components). The presented approach is domain independent and is illustrated via application to the geophysical prospecting of caves.

Visualizing the orientational dependence of an intermolecular potential

NASA Astrophysics Data System (ADS)

Sweetman, Adam; Rashid, Mohammad A.; Jarvis, Samuel P.; Dunn, Janette L.; Rahe, Philipp; Moriarty, Philip

2016-02-01

Scanning probe microscopy can now be used to map the properties of single molecules with intramolecular precision by functionalization of the apex of the scanning probe tip with a single atom or molecule. Here we report on the mapping of the three-dimensional potential between fullerene (C60) molecules in different relative orientations, with sub-Angstrom resolution, using dynamic force microscopy (DFM). We introduce a visualization method which is capable of directly imaging the variation in equilibrium binding energy of different molecular orientations. We model the interaction using both a simple approach based around analytical Lennard-Jones potentials, and with dispersion-force-corrected density functional theory (DFT), and show that the positional variation in the binding energy between the molecules is dominated by the onset of repulsive interactions. Our modelling suggests that variations in the dispersion interaction are masked by repulsive interactions even at displacements significantly larger than the equilibrium intermolecular separation.

A digital future for the history of psychology?

PubMed

Green, Christopher D

2016-08-01

This article discusses the role that digital approaches to the history of psychology are likely to play in the near future. A tentative hierarchy of digital methods is proposed. A few examples are briefly described: a digital repository, a simple visualization using ready-made online database and tools, and more complex visualizations requiring the assembly of the database and, possibly, the analytic tools by the researcher. The relationship of digital history to the old "New Economic History" (Cliometrics) is considered. The question of whether digital history and traditional history need be at odds or, instead, might complement each other is woven throughout. The rapidly expanding territory of digital humanistic research outside of psychology is briefly discussed. Finally, the challenging current employment trends in history and the humanities more broadly are considered, along with the role that digital skills might play in mitigating those factors for prospective academic workers. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Using Maps in Web Analytics to Evaluate the Impact of Web-Based Extension Programs

ERIC Educational Resources Information Center

Veregin, Howard

2015-01-01

Maps can be a valuable addition to the Web analytics toolbox for Extension programs that use the Web to disseminate information. Extension professionals use Web analytics tools to evaluate program impacts. Maps add a unique perspective through visualization and analysis of geographic patterns and their relationships to other variables. Maps can…

From Streaming Data to Streaming Insights: The Impact of Data Velocities on Mental Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Endert, Alexander; Pike, William A.; Cook, Kristin A.

The rise of Big Data has influenced the design and technical implementation of visual analytic tools required to handle the increased volumes, velocities, and varieties of data. This has required a set of data management and computational advancements to allow us to store and compute on such datasets. However, as the ultimate goal of visual analytic technology is to enable the discovery and creation of insights from the users, an under-explored area is understanding how these datasets impact their mental models. That is, how have the analytic processes and strategies of users changed? How have users changed their perception ofmore » how to leverage, and ask questions of, these datasets?« less

Slushy weightings for the optimal pilot model. [considering visual tracking task

NASA Technical Reports Server (NTRS)

Dillow, J. D.; Picha, D. G.; Anderson, R. O.

1975-01-01

A pilot model is described which accounts for the effect of motion cues in a well defined visual tracking task. The effect of visual and motion cues are accounted for in the model in two ways. First, the observation matrix in the pilot model is structured to account for the visual and motion inputs presented to the pilot. Secondly, the weightings in the quadratic cost function associated with the pilot model are modified to account for the pilot's perception of the variables he considers important in the task. Analytic results obtained using the pilot model are compared to experimental results and in general good agreement is demonstrated. The analytic model yields small improvements in tracking performance with the addition of motion cues for easily controlled task dynamics and large improvements in tracking performance with the addition of motion cues for difficult task dynamics.

Visual analytics techniques for large multi-attribute time series data

NASA Astrophysics Data System (ADS)

Hao, Ming C.; Dayal, Umeshwar; Keim, Daniel A.

2008-01-01

Time series data commonly occur when variables are monitored over time. Many real-world applications involve the comparison of long time series across multiple variables (multi-attributes). Often business people want to compare this year's monthly sales with last year's sales to make decisions. Data warehouse administrators (DBAs) want to know their daily data loading job performance. DBAs need to detect the outliers early enough to act upon them. In this paper, two new visual analytic techniques are introduced: The color cell-based Visual Time Series Line Charts and Maps highlight significant changes over time in a long time series data and the new Visual Content Query facilitates finding the contents and histories of interesting patterns and anomalies, which leads to root cause identification. We have applied both methods to two real-world applications to mine enterprise data warehouse and customer credit card fraud data to illustrate the wide applicability and usefulness of these techniques.

Telescopic multi-resolution augmented reality

NASA Astrophysics Data System (ADS)

Jenkins, Jeffrey; Frenchi, Christopher; Szu, Harold

2014-05-01

To ensure a self-consistent scaling approximation, the underlying microscopic fluctuation components can naturally influence macroscopic means, which may give rise to emergent observable phenomena. In this paper, we describe a consistent macroscopic (cm-scale), mesoscopic (micron-scale), and microscopic (nano-scale) approach to introduce Telescopic Multi-Resolution (TMR) into current Augmented Reality (AR) visualization technology. We propose to couple TMR-AR by introducing an energy-matter interaction engine framework that is based on known Physics, Biology, Chemistry principles. An immediate payoff of TMR-AR is a self-consistent approximation of the interaction between microscopic observables and their direct effect on the macroscopic system that is driven by real-world measurements. Such an interdisciplinary approach enables us to achieve more than multiple scale, telescopic visualization of real and virtual information but also conducting thought experiments through AR. As a result of the consistency, this framework allows us to explore a large dimensionality parameter space of measured and unmeasured regions. Towards this direction, we explore how to build learnable libraries of biological, physical, and chemical mechanisms. Fusing analytical sensors with TMR-AR libraries provides a robust framework to optimize testing and evaluation through data-driven or virtual synthetic simulations. Visualizing mechanisms of interactions requires identification of observable image features that can indicate the presence of information in multiple spatial and temporal scales of analog data. The AR methodology was originally developed to enhance pilot-training as well as `make believe' entertainment industries in a user-friendly digital environment We believe TMR-AR can someday help us conduct thought experiments scientifically, to be pedagogically visualized in a zoom-in-and-out, consistent, multi-scale approximations.

3D Building Evacuation Route Modelling and Visualization

NASA Astrophysics Data System (ADS)

Chan, W.; Armenakis, C.

2014-11-01

The most common building evacuation approach currently applied is to have evacuation routes planned prior to these emergency events. These routes are usually the shortest and most practical path from each building room to the closest exit. The problem with this approach is that it is not adaptive. It is not responsively configurable relative to the type, intensity, or location of the emergency risk. Moreover, it does not provide any information to the affected persons or to the emergency responders while not allowing for the review of simulated hazard scenarios and alternative evacuation routes. In this paper we address two main tasks. The first is the modelling of the spatial risk caused by a hazardous event leading to choosing the optimal evacuation route for a set of options. The second is to generate a 3D visual representation of the model output. A multicriteria decision making (MCDM) approach is used to model the risk aiming at finding the optimal evacuation route. This is achieved by using the analytical hierarchy process (AHP) on the criteria describing the different alternative evacuation routes. The best route is then chosen to be the alternative with the least cost. The 3D visual representation of the model displays the building, the surrounding environment, the evacuee's location, the hazard location, the risk areas and the optimal evacuation pathway to the target safety location. The work has been performed using ESRI's ArcGIS. Using the developed models, the user can input the location of the hazard and the location of the evacuee. The system then determines the optimum evacuation route and displays it in 3D.

Parameter Estimation of Computationally Expensive Watershed Models Through Efficient Multi-objective Optimization and Interactive Decision Analytics

NASA Astrophysics Data System (ADS)

Akhtar, Taimoor; Shoemaker, Christine

2016-04-01

Watershed model calibration is inherently a multi-criteria problem. Conflicting trade-offs exist between different quantifiable calibration criterions indicating the non-existence of a single optimal parameterization. Hence, many experts prefer a manual approach to calibration where the inherent multi-objective nature of the calibration problem is addressed through an interactive, subjective, time-intensive and complex decision making process. Multi-objective optimization can be used to efficiently identify multiple plausible calibration alternatives and assist calibration experts during the parameter estimation process. However, there are key challenges to the use of multi objective optimization in the parameter estimation process which include: 1) multi-objective optimization usually requires many model simulations, which is difficult for complex simulation models that are computationally expensive; and 2) selection of one from numerous calibration alternatives provided by multi-objective optimization is non-trivial. This study proposes a "Hybrid Automatic Manual Strategy" (HAMS) for watershed model calibration to specifically address the above-mentioned challenges. HAMS employs a 3-stage framework for parameter estimation. Stage 1 incorporates the use of an efficient surrogate multi-objective algorithm, GOMORS, for identification of numerous calibration alternatives within a limited simulation evaluation budget. The novelty of HAMS is embedded in Stages 2 and 3 where an interactive visual and metric based analytics framework is available as a decision support tool to choose a single calibration from the numerous alternatives identified in Stage 1. Stage 2 of HAMS provides a goodness-of-fit measure / metric based interactive framework for identification of a small subset (typically less than 10) of meaningful and diverse set of calibration alternatives from the numerous alternatives obtained in Stage 1. Stage 3 incorporates the use of an interactive visual analytics framework for decision support in selection of one parameter combination from the alternatives identified in Stage 2. HAMS is applied for calibration of flow parameters of a SWAT model, (Soil and Water Assessment Tool) designed to simulate flow in the Cannonsville watershed in upstate New York. Results from the application of HAMS to Cannonsville indicate that efficient multi-objective optimization and interactive visual and metric based analytics can bridge the gap between the effective use of both automatic and manual strategies for parameter estimation of computationally expensive watershed models.

Integrating Heterogeneous Healthcare Datasets and Visual Analytics for Disease Bio-surveillance and Dynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ramanathan, Arvind; Pullum, Laura L; Steed, Chad A

2013-01-01

n this paper, we present an overview of the big data chal- lenges in disease bio-surveillance and then discuss the use of visual analytics for integrating data and turning it into knowl- edge. We will explore two integration scenarios: (1) combining text and multimedia sources to improve situational awareness and (2) enhancing disease spread model data with real-time bio-surveillance data. Together, the proposed integration methodologies can improve awareness about when, where and how emerging diseases can affect wide geographic regions.

Semantic Interaction for Visual Analytics: Toward Coupling Cognition and Computation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Endert, Alexander

2014-07-01

The dissertation discussed in this article [1] was written in the midst of an era of digitization. The world is becoming increasingly instrumented with sensors, monitoring, and other methods for generating data describing social, physical, and natural phenomena. Thus, data exist with the potential of being analyzed to uncover, or discover, the phenomena from which it was created. However, as the analytic models leveraged to analyze these data continue to increase in complexity and computational capability, how can visualizations and user interaction methodologies adapt and evolve to continue to foster discovery and sensemaking?

Visual business ecosystem intelligence: lessons from the field.

PubMed

Basole, Rahul C

2014-01-01

Macroscopic insight into business ecosystems is becoming increasingly important. With the emergence of new digital business data, opportunities exist to develop rich, interactive visual-analytics tools. Georgia Institute of Technology researchers have been developing and implementing visual business ecosystem intelligence tools in corporate settings. This article discusses the challenges they faced, the lessons learned, and opportunities for future research.

A Virtual World of Visualization

NASA Technical Reports Server (NTRS)

1998-01-01

In 1990, Sterling Software, Inc., developed the Flow Analysis Software Toolkit (FAST) for NASA Ames on contract. FAST is a workstation based modular analysis and visualization tool. It is used to visualize and animate grids and grid oriented data, typically generated by finite difference, finite element and other analytical methods. FAST is now available through COSMIC, NASA's software storehouse.

Cultivating Research Skills: An interdisciplinary approach in training and supporting energy research

NASA Astrophysics Data System (ADS)

Winkler, H.; Carbajales-Dale, P.; Alschbach, E.

2013-12-01

Geoscience and energy research has essentially separate and diverse tracks and traditions, making the education process labor-intensive and burdensome. Using a combined forces approach to training, a multidisciplinary workshop on information and data sources and research skills was developed and offered through several departments at Stanford University. The popular workshops taught required skills to scientists - giving training on new technologies, access to restricted energy-related scientific and government databases, search strategies for data-driven resources, and visualization and geospatial analytics. Feedback and data suggest these workshops were fundamental as they set the foundation for subsequent learning opportunities for students and faculty. This session looks at the integration of the information workshops within multiple energy and geoscience programs and the importance of formally cultivating research and information skills.

Visual Analytics of integrated Data Systems for Space Weather Purposes

NASA Astrophysics Data System (ADS)

Rosa, Reinaldo; Veronese, Thalita; Giovani, Paulo

Analysis of information from multiple data sources obtained through high resolution instrumental measurements has become a fundamental task in all scientific areas. The development of expert methods able to treat such multi-source data systems, with both large variability and measurement extension, is a key for studying complex scientific phenomena, especially those related to systemic analysis in space and environmental sciences. In this talk, we present a time series generalization introducing the concept of generalized numerical lattice, which represents a discrete sequence of temporal measures for a given variable. In this novel representation approach each generalized numerical lattice brings post-analytical data information. We define a generalized numerical lattice as a set of three parameters representing the following data properties: dimensionality, size and post-analytical measure (e.g., the autocorrelation, Hurst exponent, etc)[1]. From this representation generalization, any multi-source database can be reduced to a closed set of classified time series in spatiotemporal generalized dimensions. As a case study, we show a preliminary application in space science data, highlighting the possibility of a real time analysis expert system. In this particular application, we have selected and analyzed, using detrended fluctuation analysis (DFA), several decimetric solar bursts associated to X flare-classes. The association with geomagnetic activity is also reported. DFA method is performed in the framework of a radio burst automatic monitoring system. Our results may characterize the variability pattern evolution, computing the DFA scaling exponent, scanning the time series by a short windowing before the extreme event [2]. For the first time, the application of systematic fluctuation analysis for space weather purposes is presented. The prototype for visual analytics is implemented in a Compute Unified Device Architecture (CUDA) by using the K20 Nvidia graphics processing units (GPUs) to reduce the integrated analysis runtime. [1] Veronese et al. doi: 10.6062/jcis.2009.01.02.0021, 2010. [2] Veronese et al. doi:http://dx.doi.org/10.1016/j.jastp.2010.09.030, 2011.

PANTHER. Pattern ANalytics To support High-performance Exploitation and Reasoning.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Czuchlewski, Kristina Rodriguez; Hart, William E.

Sandia has approached the analysis of big datasets with an integrated methodology that uses computer science, image processing, and human factors to exploit critical patterns and relationships in large datasets despite the variety and rapidity of information. The work is part of a three-year LDRD Grand Challenge called PANTHER (Pattern ANalytics To support High-performance Exploitation and Reasoning). To maximize data analysis capability, Sandia pursued scientific advances across three key technical domains: (1) geospatial-temporal feature extraction via image segmentation and classification; (2) geospatial-temporal analysis capabilities tailored to identify and process new signatures more efficiently; and (3) domain- relevant models of humanmore » perception and cognition informing the design of analytic systems. Our integrated results include advances in geographical information systems (GIS) in which we discover activity patterns in noisy, spatial-temporal datasets using geospatial-temporal semantic graphs. We employed computational geometry and machine learning to allow us to extract and predict spatial-temporal patterns and outliers from large aircraft and maritime trajectory datasets. We automatically extracted static and ephemeral features from real, noisy synthetic aperture radar imagery for ingestion into a geospatial-temporal semantic graph. We worked with analysts and investigated analytic workflows to (1) determine how experiential knowledge evolves and is deployed in high-demand, high-throughput visual search workflows, and (2) better understand visual search performance and attention. Through PANTHER, Sandia's fundamental rethinking of key aspects of geospatial data analysis permits the extraction of much richer information from large amounts of data. The project results enable analysts to examine mountains of historical and current data that would otherwise go untouched, while also gaining meaningful, measurable, and defensible insights into overlooked relationships and patterns. The capability is directly relevant to the nation's nonproliferation remote-sensing activities and has broad national security applications for military and intelligence- gathering organizations.« less

Visualizing Confidence in Cluster-Based Ensemble Weather Forecast Analyses.

PubMed

Kumpf, Alexander; Tost, Bianca; Baumgart, Marlene; Riemer, Michael; Westermann, Rudiger; Rautenhaus, Marc

2018-01-01

In meteorology, cluster analysis is frequently used to determine representative trends in ensemble weather predictions in a selected spatio-temporal region, e.g., to reduce a set of ensemble members to simplify and improve their analysis. Identified clusters (i.e., groups of similar members), however, can be very sensitive to small changes of the selected region, so that clustering results can be misleading and bias subsequent analyses. In this article, we - a team of visualization scientists and meteorologists-deliver visual analytics solutions to analyze the sensitivity of clustering results with respect to changes of a selected region. We propose an interactive visual interface that enables simultaneous visualization of a) the variation in composition of identified clusters (i.e., their robustness), b) the variability in cluster membership for individual ensemble members, and c) the uncertainty in the spatial locations of identified trends. We demonstrate that our solution shows meteorologists how representative a clustering result is, and with respect to which changes in the selected region it becomes unstable. Furthermore, our solution helps to identify those ensemble members which stably belong to a given cluster and can thus be considered similar. In a real-world application case we show how our approach is used to analyze the clustering behavior of different regions in a forecast of "Tropical Cyclone Karl", guiding the user towards the cluster robustness information required for subsequent ensemble analysis.

Using secure web services to visualize poison center data for nationwide biosurveillance: a case study.

PubMed

Savel, Thomas G; Bronstein, Alvin; Duck, William; Rhodes, M Barry; Lee, Brian; Stinn, John; Worthen, Katherine

2010-01-01

Real-time surveillance systems are valuable for timely response to public health emergencies. It has been challenging to leverage existing surveillance systems in state and local communities, and, using a centralized architecture, add new data sources and analytical capacity. Because this centralized model has proven to be difficult to maintain and enhance, the US Centers for Disease Control and Prevention (CDC) has been examining the ability to use a federated model based on secure web services architecture, with data stewardship remaining with the data provider. As a case study for this approach, the American Association of Poison Control Centers and the CDC extended an existing data warehouse via a secure web service, and shared aggregate clinical effects and case counts data by geographic region and time period. To visualize these data, CDC developed a web browser-based interface, Quicksilver, which leveraged the Google Maps API and Flot, a javascript plotting library. Two iterations of the NPDS web service were completed in 12 weeks. The visualization client, Quicksilver, was developed in four months. This implementation of web services combined with a visualization client represents incremental positive progress in transitioning national data sources like BioSense and NPDS to a federated data exchange model. Quicksilver effectively demonstrates how the use of secure web services in conjunction with a lightweight, rapidly deployed visualization client can easily integrate isolated data sources for biosurveillance.

The discriminant pixel approach: a new tool for the rational interpretation of GCxGC-MS chromatograms.

PubMed

Vial, Jérôme; Pezous, Benoît; Thiébaut, Didier; Sassiat, Patrick; Teillet, Béatrice; Cahours, Xavier; Rivals, Isabelle

2011-01-30

GCxGC is now recognized as the most suited analytical technique for the characterization of complex mixtures of volatile compounds; it is implemented worldwide in academic and industrial laboratories. However, in the frame of comprehensive analysis of non-target analytes, going beyond the visual examination of the color plots remains challenging for most users. We propose a strategy that aims at classifying chromatograms according to the chemical composition of the samples while determining the origin of the discrimination between different classes of samples: the discriminant pixel approach. After data pre-processing and time-alignment, the discriminatory power of each chromatogram pixel for a given class was defined as its correlation with the membership to this class. Using a peak finding algorithm, the most discriminant pixels were then linked to chromatographic peaks. Finally, crosschecking with mass spectrometry data enabled to establish relationships with compounds that could consequently be considered as candidate class markers. This strategy was applied to a large experimental data set of 145 GCxGC-MS chromatograms of tobacco extracts corresponding to three distinct classes of tobacco. Copyright © 2010 Elsevier B.V. All rights reserved.

Coping with Volume and Variety in Temporal Event Sequences: Strategies for Sharpening Analytic Focus.

PubMed

Fan Du; Shneiderman, Ben; Plaisant, Catherine; Malik, Sana; Perer, Adam

2017-06-01

The growing volume and variety of data presents both opportunities and challenges for visual analytics. Addressing these challenges is needed for big data to provide valuable insights and novel solutions for business, security, social media, and healthcare. In the case of temporal event sequence analytics it is the number of events in the data and variety of temporal sequence patterns that challenges users of visual analytic tools. This paper describes 15 strategies for sharpening analytic focus that analysts can use to reduce the data volume and pattern variety. Four groups of strategies are proposed: (1) extraction strategies, (2) temporal folding, (3) pattern simplification strategies, and (4) iterative strategies. For each strategy, we provide examples of the use and impact of this strategy on volume and/or variety. Examples are selected from 20 case studies gathered from either our own work, the literature, or based on email interviews with individuals who conducted the analyses and developers who observed analysts using the tools. Finally, we discuss how these strategies might be combined and report on the feedback from 10 senior event sequence analysts.

EmailTime: visual analytics and statistics for temporal email

NASA Astrophysics Data System (ADS)

Erfani Joorabchi, Minoo; Yim, Ji-Dong; Shaw, Christopher D.

2011-01-01

Although the discovery and analysis of communication patterns in large and complex email datasets are difficult tasks, they can be a valuable source of information. We present EmailTime, a visual analysis tool of email correspondence patterns over the course of time that interactively portrays personal and interpersonal networks using the correspondence in the email dataset. Our approach is to put time as a primary variable of interest, and plot emails along a time line. EmailTime helps email dataset explorers interpret archived messages by providing zooming, panning, filtering and highlighting etc. To support analysis, it also measures and visualizes histograms, graph centrality and frequency on the communication graph that can be induced from the email collection. This paper describes EmailTime's capabilities, along with a large case study with Enron email dataset to explore the behaviors of email users within different organizational positions from January 2000 to December 2001. We defined email behavior as the email activity level of people regarding a series of measured metrics e.g. sent and received emails, numbers of email addresses, etc. These metrics were calculated through EmailTime. Results showed specific patterns in the use email within different organizational positions. We suggest that integrating both statistics and visualizations in order to display information about the email datasets may simplify its evaluation.

High-Throughput Biophysical Analysis and Data Visualization of Conformational Stability of an IgG1 Monoclonal Antibody (mAb) After Deglycosylation

PubMed Central

Alsenaidy, Mohammad A.; Kim, Jae Hyun; Majumdar, Ranajoy; Weis, David D.; Joshi, Sangeeta B.; Tolbert, Thomas J.; Middaugh, C. Russell; Volkin, David B.

2013-01-01

The structural integrity and conformational stability of an IgG1 monoclonal antibody (mAb), after partial and complete enzymatic removal of the N-linked Fc glycan, was compared to the untreated mAb over a wide range of temperature (10° to 90°C) and solution pH (3 to 8) using circular dichroism, fluorescence spectroscopy, and static light scattering combined with data visualization employing empirical phase diagrams (EPDs). Subtle to larger stability differences between the different glycoforms were observed. Improved detection of physical stability differences was then demonstrated over narrower pH range (4.0-6.0) using smaller temperature increments, especially when combined with an alternative data visualization method (radar plots). Differential scanning calorimetry and differential scanning fluorimetry were then utilized and also showed an improved ability to detect differences in mAb glycoform physical stability. Based on these results, a two-step methodology was used in which mAb glycoform conformational stability is first screened with a wide variety of instruments and environmental stresses, followed by a second evaluation with optimally sensitive experimental conditions, analytical techniques and data visualization methods. With this approach, high-throughput biophysical analysis to assess relatively subtle conformational stability differences in protein glycoforms is demonstrated. PMID:24114789

Using Text Analytics of AJPE Article Titles to Reveal Trends In Pharmacy Education Over the Past Two Decades.

PubMed

Pedrami, Farnoush; Asenso, Pamela; Devi, Sachin

2016-08-25

Objective. To identify trends in pharmacy education during last two decades using text mining. Methods. Articles published in the American Journal of Pharmaceutical Education (AJPE) in the past two decades were compiled in a database. Custom text analytics software was written using Visual Basic programming language in the Visual Basic for Applications (VBA) editor of Excel 2007. Frequency of words appearing in article titles was calculated using the custom VBA software. Data were analyzed to identify the emerging trends in pharmacy education. Results. Three educational trends emerged: active learning, interprofessional, and cultural competency. Conclusion. The text analytics program successfully identified trends in article topics and may be a useful compass to predict the future course of pharmacy education.

Integration of GIS and Bim for Indoor Geovisual Analytics

NASA Astrophysics Data System (ADS)

Wu, B.; Zhang, S.

2016-06-01

This paper presents an endeavour of integration of GIS (Geographical Information System) and BIM (Building Information Modelling) for indoor geovisual analytics. The merits of two types of technologies, GIS and BIM are firstly analysed in the context of indoor environment. GIS has well-developed capabilities of spatial analysis such as network analysis, while BIM has the advantages for indoor 3D modelling and dynamic simulation. This paper firstly investigates the important aspects for integrating GIS and BIM. Different data standards and formats such as the IFC (Industry Foundation Classes) and GML (Geography Markup Language) are discussed. Their merits and limitations in data transformation between GIS and BIM are analysed in terms of semantic and geometric information. An optimized approach for data exchange between GIS and BIM datasets is then proposed. After that, a strategy of using BIM for 3D indoor modelling, GIS for spatial analysis, and BIM again for visualization and dynamic simulation of the analysis results is presented. Based on the developments, this paper selects a typical problem, optimized indoor emergency evacuation, to demonstrate the integration of GIS and BIM for indoor geovisual analytics. The block Z of the Hong Kong Polytechnic University is selected as a test site. Detailed indoor and outdoor 3D models of the block Z are created using a BIM software Revit. The 3D models are transferred to a GIS software ArcGIS to carry out spatial analysis. Optimized evacuation plans considering dynamic constraints are generated based on network analysis in ArcGIS assuming there is a fire accident inside the building. The analysis results are then transferred back to BIM software for visualization and dynamic simulation. The developed methods and results are of significance to facilitate future development of GIS and BIM integrated solutions in various applications.

Evaluation of algorithms for point cloud surface reconstruction through the analysis of shape parameters

NASA Astrophysics Data System (ADS)

Cao, Lu; Verbeek, Fons J.

2012-03-01

In computer graphics and visualization, reconstruction of a 3D surface from a point cloud is an important research area. As the surface contains information that can be measured, i.e. expressed in features, the application of surface reconstruction can be potentially important for application in bio-imaging. Opportunities in this application area are the motivation for this study. In the past decade, a number of algorithms for surface reconstruction have been proposed. Generally speaking, these methods can be separated into two categories: i.e., explicit representation and implicit approximation. Most of the aforementioned methods are firmly based in theory; however, so far, no analytical evaluation between these methods has been presented. The straightforward way of evaluation has been by convincing through visual inspection. Through evaluation we search for a method that can precisely preserve the surface characteristics and that is robust in the presence of noise. The outcome will be used to improve reliability in surface reconstruction of biological models. We, therefore, use an analytical approach by selecting features as surface descriptors and measure these features in varying conditions. We selected surface distance, surface area and surface curvature as three major features to compare quality of the surface created by the different algorithms. Our starting point has been ground truth values obtained from analytical shapes such as the sphere and the ellipsoid. In this paper we present four classical surface reconstruction methods from the two categories mentioned above, i.e. the Power Crust, the Robust Cocone, the Fourier-based method and the Poisson reconstruction method. The results obtained from our experiments indicate that Poisson reconstruction method performs the best in the presence of noise.

A digital photogrammetric method for measuring horizontal surficial movements on the slumgullion earthflow, Hinsdale county, Colorado

USGS Publications Warehouse

Powers, P.S.; Chiarle, M.; Savage, W.Z.

1996-01-01

The traditional approach to making aerial photographic measurements uses analog or analytic photogrammetric equipment. We have developed a digital method for making measurements from aerial photographs which uses geographic information system (GIS) software, and primarily DOS-based personal computers. This method, which is based on the concept that a direct visual comparison can be made between images derived from two sets of aerial photographs taken at different times, was applied to the surface of the active portion of the Slumgullion earthflow in Colorado to determine horizontal displacement vectors from the movements of visually identifiable objects, such as trees and large rocks. Using this method, more of the slide surface can be mapped in a shorter period of time than using the standard photogrammetric approach. More than 800 horizontal displacement vectors were determined on the active earthflow surface using images produced by our digital photogrammetric technique and 1985 (1:12,000-scale) and 1990 (1:6,000-scale) aerial photographs. The resulting displacement field shows, with a 2-m measurement error (??? 10%), that the fastest moving portion of the landslide underwent 15-29 m of horizontal displacement between 1985 and 1990. Copyright ?? 1996 Elsevier Science Ltd.

Survey of Network Visualization Tools

DTIC Science & Technology

2007-12-01

Dimensionality • 2D Comments: Deployment Type: • Components for tool building • Standalone Tool OS: • Windows Extensibility • ActiveX ...Visual Basic Comments: Interoperability Daisy is fully compliant with Microsoft’s ActiveX , therefore, other Windows based programs can...other functions that improve analytic decision making. Available in ActiveX , C++, Java, and .NET editions. • Tom Sawyer Visualization: Enables you to

Increasing the value of geospatial informatics with open approaches for Big Data

NASA Astrophysics Data System (ADS)

Percivall, G.; Bermudez, L. E.

2017-12-01

Open approaches to big data provide geoscientists with new capabilities to address problems of unmatched size and complexity. Consensus approaches for Big Geo Data have been addressed in multiple international workshops and testbeds organized by the Open Geospatial Consortium (OGC) in the past year. Participants came from government (NASA, ESA, USGS, NOAA, DOE); research (ORNL, NCSA, IU, JPL, CRIM, RENCI); industry (ESRI, Digital Globe, IBM, rasdaman); standards (JTC 1/NIST); and open source software communities. Results from the workshops and testbeds are documented in Testbed reports and a White Paper published by the OGC. The White Paper identifies the following set of use cases: Collection and Ingest: Remote sensed data processing; Data stream processing Prepare and Structure: SQL and NoSQL databases; Data linking; Feature identification Analytics and Visualization: Spatial-temporal analytics; Machine Learning; Data Exploration Modeling and Prediction: Integrated environmental models; Urban 4D models. Open implementations were developed in the Arctic Spatial Data Pilot using Discrete Global Grid Systems (DGGS) and in Testbeds using WPS and ESGF to publish climate predictions. Further development activities to advance open implementations of Big Geo Data include the following: Open Cloud Computing: Avoid vendor lock-in through API interoperability and Application portability. Open Source Extensions: Implement geospatial data representations in projects from Apache, Location Tech, and OSGeo. Investigate parallelization strategies for N-Dimensional spatial data. Geospatial Data Representations: Schemas to improve processing and analysis using geospatial concepts: Features, Coverages, DGGS. Use geospatial encodings like NetCDF and GeoPackge. Big Linked Geodata: Use linked data methods scaled to big geodata. Analysis Ready Data: Support "Download as last resort" and "Analytics as a service". Promote elements common to "datacubes."

Giving raw data a chance to talk: a demonstration of exploratory visual analytics with a pediatric research database using Microsoft Live Labs Pivot to promote cohort discovery, research, and quality assessment.

PubMed

Viangteeravat, Teeradache; Nagisetty, Naga Satya V Rao

2014-01-01

Secondary use of large and open data sets provides researchers with an opportunity to address high-impact questions that would otherwise be prohibitively expensive and time consuming to study. Despite the availability of data, generating hypotheses from huge data sets is often challenging, and the lack of complex analysis of data might lead to weak hypotheses. To overcome these issues and to assist researchers in building hypotheses from raw data, we are working on a visual and analytical platform called PRD Pivot. PRD Pivot is a de-identified pediatric research database designed to make secondary use of rich data sources, such as the electronic health record (EHR). The development of visual analytics using Microsoft Live Labs Pivot makes the process of data elaboration, information gathering, knowledge generation, and complex information exploration transparent to tool users and provides researchers with the ability to sort and filter by various criteria, which can lead to strong, novel hypotheses.

Giving Raw Data a Chance to Talk: A Demonstration of Exploratory Visual Analytics with a Pediatric Research Database Using Microsoft Live Labs Pivot to Promote Cohort Discovery, Research, and Quality Assessment

PubMed Central

Viangteeravat, Teeradache; Nagisetty, Naga Satya V. Rao

2014-01-01

Secondary use of large and open data sets provides researchers with an opportunity to address high-impact questions that would otherwise be prohibitively expensive and time consuming to study. Despite the availability of data, generating hypotheses from huge data sets is often challenging, and the lack of complex analysis of data might lead to weak hypotheses. To overcome these issues and to assist researchers in building hypotheses from raw data, we are working on a visual and analytical platform called PRD Pivot. PRD Pivot is a de-identified pediatric research database designed to make secondary use of rich data sources, such as the electronic health record (EHR). The development of visual analytics using Microsoft Live Labs Pivot makes the process of data elaboration, information gathering, knowledge generation, and complex information exploration transparent to tool users and provides researchers with the ability to sort and filter by various criteria, which can lead to strong, novel hypotheses. PMID:24808811

Evaluation of Visual Analytics Environments: The Road to the Visual Analytics Science and Technology Challenge Evaluation Methodology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scholtz, Jean; Plaisant, Catherine; Whiting, Mark A.

The evaluation of visual analytics environments was a topic in Illuminating the Path [Thomas 2005] as a critical aspect of moving research into practice. For a thorough understanding of the utility of the systems available, evaluation not only involves assessing the visualizations, interactions or data processing algorithms themselves, but also the complex processes that a tool is meant to support (such as exploratory data analysis and reasoning, communication through visualization, or collaborative data analysis [Lam 2012; Carpendale 2007]). Researchers and practitioners in the field have long identified many of the challenges faced when planning, conducting, and executing an evaluation ofmore » a visualization tool or system [Plaisant 2004]. Evaluation is needed to verify that algorithms and software systems work correctly and that they represent improvements over the current infrastructure. Additionally to effectively transfer new software into a working environment, it is necessary to ensure that the software has utility for the end-users and that the software can be incorporated into the end-user’s infrastructure and work practices. Evaluation test beds require datasets, tasks, metrics and evaluation methodologies. As noted in [Thomas 2005] it is difficult and expensive for any one researcher to setup an evaluation test bed so in many cases evaluation is setup for communities of researchers or for various research projects or programs. Examples of successful community evaluations can be found [Chinchor 1993; Voorhees 2007; FRGC 2012]. As visual analytics environments are intended to facilitate the work of human analysts, one aspect of evaluation needs to focus on the utility of the software to the end-user. This requires representative users, representative tasks, and metrics that measure the utility to the end-user. This is even more difficult as now one aspect of the test methodology is access to representative end-users to participate in the evaluation. In many cases the sensitive nature of data and tasks and difficult access to busy analysts puts even more of a burden on researchers to complete this type of evaluation. User-centered design goes beyond evaluation and starts with the user [Beyer 1997, Shneiderman 2009]. Having some knowledge of the type of data, tasks, and work practices helps researchers and developers know the correct paths to pursue in their work. When access to the end-users is problematic at best and impossible at worst, user-centered design becomes difficult. Researchers are unlikely to go to work on the type of problems faced by inaccessible users. Commercial vendors have difficulties evaluating and improving their products when they cannot observe real users working with their products. In well-established fields such as web site design or office software design, user-interface guidelines have been developed based on the results of empirical studies or the experience of experts. Guidelines can speed up the design process and replace some of the need for observation of actual users [heuristics review references]. In 2006 when the visual analytics community was initially getting organized, no such guidelines existed. Therefore, we were faced with the problem of developing an evaluation framework for the field of visual analytics that would provide representative situations and datasets, representative tasks and utility metrics, and finally a test methodology which would include a surrogate for representative users, increase interest in conducting research in the field, and provide sufficient feedback to the researchers so that they could improve their systems.« less

Many-objective optimization and visual analytics reveal key trade-offs for London's water supply

NASA Astrophysics Data System (ADS)

Matrosov, Evgenii S.; Huskova, Ivana; Kasprzyk, Joseph R.; Harou, Julien J.; Lambert, Chris; Reed, Patrick M.

2015-12-01

In this study, we link a water resource management simulator to multi-objective search to reveal the key trade-offs inherent in planning a real-world water resource system. We consider new supplies and demand management (conservation) options while seeking to elucidate the trade-offs between the best portfolios of schemes to satisfy projected water demands. Alternative system designs are evaluated using performance measures that minimize capital and operating costs and energy use while maximizing resilience, engineering and environmental metrics, subject to supply reliability constraints. Our analysis shows many-objective evolutionary optimization coupled with state-of-the art visual analytics can help planners discover more diverse water supply system designs and better understand their inherent trade-offs. The approach is used to explore future water supply options for the Thames water resource system (including London's water supply). New supply options include a new reservoir, water transfers, artificial recharge, wastewater reuse and brackish groundwater desalination. Demand management options include leakage reduction, compulsory metering and seasonal tariffs. The Thames system's Pareto approximate portfolios cluster into distinct groups of water supply options; for example implementing a pipe refurbishment program leads to higher capital costs but greater reliability. This study highlights that traditional least-cost reliability constrained design of water supply systems masks asset combinations whose benefits only become apparent when more planning objectives are considered.

Integrated genome browser: visual analytics platform for genomics.

PubMed

Freese, Nowlan H; Norris, David C; Loraine, Ann E

2016-07-15

Genome browsers that support fast navigation through vast datasets and provide interactive visual analytics functions can help scientists achieve deeper insight into biological systems. Toward this end, we developed Integrated Genome Browser (IGB), a highly configurable, interactive and fast open source desktop genome browser. Here we describe multiple updates to IGB, including all-new capabilities to display and interact with data from high-throughput sequencing experiments. To demonstrate, we describe example visualizations and analyses of datasets from RNA-Seq, ChIP-Seq and bisulfite sequencing experiments. Understanding results from genome-scale experiments requires viewing the data in the context of reference genome annotations and other related datasets. To facilitate this, we enhanced IGB's ability to consume data from diverse sources, including Galaxy, Distributed Annotation and IGB-specific Quickload servers. To support future visualization needs as new genome-scale assays enter wide use, we transformed the IGB codebase into a modular, extensible platform for developers to create and deploy all-new visualizations of genomic data. IGB is open source and is freely available from http://bioviz.org/igb aloraine@uncc.edu. © The Author 2016. Published by Oxford University Press.

Data-Driven Geospatial Visual Analytics for Real-Time Urban Flooding Decision Support

NASA Astrophysics Data System (ADS)

Liu, Y.; Hill, D.; Rodriguez, A.; Marini, L.; Kooper, R.; Myers, J.; Wu, X.; Minsker, B. S.

2009-12-01

Urban flooding is responsible for the loss of life and property as well as the release of pathogens and other pollutants into the environment. Previous studies have shown that spatial distribution of intense rainfall significantly impacts the triggering and behavior of urban flooding. However, no general purpose tools yet exist for deriving rainfall data and rendering them in real-time at the resolution of hydrologic units used for analyzing urban flooding. This paper presents a new visual analytics system that derives and renders rainfall data from the NEXRAD weather radar system at the sewershed (i.e. urban hydrologic unit) scale in real-time for a Chicago stormwater management project. We introduce a lightweight Web 2.0 approach which takes advantages of scientific workflow management and publishing capabilities developed at NCSA (National Center for Supercomputing Applications), streaming data-aware semantic content management repository, web-based Google Earth/Map and time-aware KML (Keyhole Markup Language). A collection of polygon-based virtual sensors is created from the NEXRAD Level II data using spatial, temporal and thematic transformations at the sewershed level in order to produce persistent virtual rainfall data sources for the animation. Animated color-coded rainfall map in the sewershed can be played in real-time as a movie using time-aware KML inside the web browser-based Google Earth for visually analyzing the spatiotemporal patterns of the rainfall intensity in the sewershed. Such system provides valuable information for situational awareness and improved decision support during extreme storm events in an urban area. Our further work includes incorporating additional data (such as basement flooding events data) or physics-based predictive models that can be used for more integrated data-driven decision support.

Comparative Characterization of Crofelemer Samples Using Data Mining and Machine Learning Approaches With Analytical Stability Data Sets.

PubMed

Nariya, Maulik K; Kim, Jae Hyun; Xiong, Jian; Kleindl, Peter A; Hewarathna, Asha; Fisher, Adam C; Joshi, Sangeeta B; Schöneich, Christian; Forrest, M Laird; Middaugh, C Russell; Volkin, David B; Deeds, Eric J

2017-11-01

There is growing interest in generating physicochemical and biological analytical data sets to compare complex mixture drugs, for example, products from different manufacturers. In this work, we compare various crofelemer samples prepared from a single lot by filtration with varying molecular weight cutoffs combined with incubation for different times at different temperatures. The 2 preceding articles describe experimental data sets generated from analytical characterization of fractionated and degraded crofelemer samples. In this work, we use data mining techniques such as principal component analysis and mutual information scores to help visualize the data and determine discriminatory regions within these large data sets. The mutual information score identifies chemical signatures that differentiate crofelemer samples. These signatures, in many cases, would likely be missed by traditional data analysis tools. We also found that supervised learning classifiers robustly discriminate samples with around 99% classification accuracy, indicating that mathematical models of these physicochemical data sets are capable of identifying even subtle differences in crofelemer samples. Data mining and machine learning techniques can thus identify fingerprint-type attributes of complex mixture drugs that may be used for comparative characterization of products. Copyright © 2017 American Pharmacists Association®. All rights reserved.

Apparent Mass Nonlinearity for Paired Oscillating Plates

NASA Astrophysics Data System (ADS)

Granlund, Kenneth; Ol, Michael

2014-11-01

The classical potential-flow problem of a plate oscillating sinusoidally at small amplitude, in a direction normal to its plane, has a well-known analytical solution of a fluid ``mass,'' multiplied by plate acceleration, being equal to the force on the plate. This so-called apparent-mass is analytically equal to that of a cylinder of fluid, with diameter equal to plate chord. The force is directly proportional to frequency squared. Here we consider experimentally a generalization, where two coplanar plates of equal chord are placed at some lateral distance apart. For spacing of ~0.5 chord and larger between the two plates, the analytical solution for a single plate can simply be doubled. Zero spacing means a plate of twice the chord and therefore a heuristic cylinder of fluid of twice the cross-sectional area. This limit is approached for plate spacing <0.5c. For a spacing of 0.1-0.2c, the force due to apparent mass was found to increase with frequency, when normalized by frequency squared; this is a nonlinearity and a departure from the classical theory. Flow visualization in a water-tank suggests that such departure can be imputed to vortex shedding from the plates' edges inside the inter-plate gap.

Experimental and Analytic Evaluation of the Effects of Visual and Motion Simulation in SH-3 Helicopter Training. Technical Report 85-002.

ERIC Educational Resources Information Center

Pfeiffer, Mark G.; Scott, Paul G.

A fly-only group (N=16) of Navy replacement pilots undergoing fleet readiness training in the SH-3 helicopter was compared with groups pre-trained on Device 2F64C with: (1) visual only (N=13); (2) no visual/no motion (N=14); and (3) one visual plus motion group (N=19). Groups were compared for their SH-3 helicopter performance in the transition…

Qualitative evaluation of water displacement in simulated analytical breaststroke movements.

PubMed

Martens, Jonas; Daly, Daniel

2012-05-01

One purpose of evaluating a swimmer is to establish the individualized optimal technique. A swimmer's particular body structure and the resulting movement pattern will cause the surrounding water to react in differing ways. Consequently, an assessment method based on flow visualization was developed complimentary to movement analysis and body structure quantification. A fluorescent dye was used to make the water displaced by the body visible on video. To examine the hypothesis on the propulsive mechanisms applied in breaststroke swimming, we analyzed the movements of the surrounding water during 4 analytical breaststroke movements using the flow visualization technique.

Electroencephalographic monitoring of complex mental tasks

NASA Technical Reports Server (NTRS)

Guisado, Raul; Montgomery, Richard; Montgomery, Leslie; Hickey, Chris

1992-01-01

Outlined here is the development of neurophysiological procedures to monitor operators during the performance of cognitive tasks. Our approach included the use of electroencepalographic (EEG) and rheoencephalographic (REG) techniques to determine changes in cortical function associated with cognition in the operator's state. A two channel tetrapolar REG, a single channel forearm impedance plethysmograph, a Lead I electrocardiogram (ECG) and a 21 channel EEG were used to measure subject responses to various visual-motor cognitive tasks. Testing, analytical, and display procedures for EEG and REG monitoring were developed that extend the state of the art and provide a valuable tool for the study of cerebral circulatory and neural activity during cognition.

The functional significance of EEG microstates--Associations with modalities of thinking.

PubMed

Milz, P; Faber, P L; Lehmann, D; Koenig, T; Kochi, K; Pascual-Marqui, R D

2016-01-15

The momentary, global functional state of the brain is reflected by its electric field configuration. Cluster analytical approaches consistently extracted four head-surface brain electric field configurations that optimally explain the variance of their changes across time in spontaneous EEG recordings. These four configurations are referred to as EEG microstate classes A, B, C, and D and have been associated with verbal/phonological, visual, subjective interoceptive-autonomic processing, and attention reorientation, respectively. The present study tested these associations via an intra-individual and inter-individual analysis approach. The intra-individual approach tested the effect of task-induced increased modality-specific processing on EEG microstate parameters. The inter-individual approach tested the effect of personal modality-specific parameters on EEG microstate parameters. We obtained multichannel EEG from 61 healthy, right-handed, male students during four eyes-closed conditions: object-visualization, spatial-visualization, verbalization (6 runs each), and resting (7 runs). After each run, we assessed participants' degrees of object-visual, spatial-visual, and verbal thinking using subjective reports. Before and after the recording, we assessed modality-specific cognitive abilities and styles using nine cognitive tests and two questionnaires. The EEG of all participants, conditions, and runs was clustered into four classes of EEG microstates (A, B, C, and D). RMANOVAs, ANOVAs and post-hoc paired t-tests compared microstate parameters between conditions. TANOVAs compared microstate class topographies between conditions. Differences were localized using eLORETA. Pearson correlations assessed interrelationships between personal modality-specific parameters and EEG microstate parameters during no-task resting. As hypothesized, verbal as opposed to visual conditions consistently affected the duration, occurrence, and coverage of microstate classes A and B. Contrary to associations suggested by previous reports, parameters were increased for class A during visualization, and class B during verbalization. In line with previous reports, microstate D parameters were increased during no-task resting compared to the three internal, goal-directed tasks. Topographic differences between conditions included particular sub-regions of components of the metabolic default mode network. Modality-specific personal parameters did not consistently correlate with microstate parameters except verbal cognitive style which correlated negatively with microstate class A duration and positively with class C occurrence. This is the first study that aimed to induce EEG microstate class parameter changes based on their hypothesized functional significance. Beyond the associations of microstate classes A and B with visual and verbal processing, respectively, our results suggest that a finely-tuned interplay between all four EEG microstate classes is necessary for the continuous formation of visual and verbal thoughts. Our results point to the possibility that the EEG microstate classes may represent the head-surface measured activity of intra-cortical sources primarily exhibiting inhibitory functions. However, additional studies are needed to verify and elaborate on this hypothesis. Copyright © 2015 Elsevier Inc. All rights reserved.

ClipCard: Sharable, Searchable Visual Metadata Summaries on the Cloud to Render Big Data Actionable

NASA Astrophysics Data System (ADS)

Saripalli, P.; Davis, D.; Cunningham, R.

2013-12-01

Research firm IDC estimates that approximately 90 percent of the Enterprise Big Data go un-analyzed, as 'dark data' - an enormous corpus of undiscovered, untagged information residing on data warehouses, servers and Storage Area Networks (SAN). In the geosciences, these data range from unpublished model runs to vast survey data assets to raw sensor data. Many of these are now being collected instantaneously, at a greater volume and in new data formats. Not all of these data can be analyzed, nor processed in real time, and their features may not be well described at the time of collection. These dark data are a serious data management problem for science organizations of all types, especially ones with mandated or required data reporting and compliance requirements. Additionally, data curators and scientists are encouraged to quantify the impact of their data holdings as a way to measure research success. Deriving actionable insights is the foremost goal of Big Data Analytics (BDA), which is especially true with geoscience, given its direct impact on most of the pressing global issues. Clearly, there is a pressing need for innovative approaches to making dark data discoverable, measurable, and actionable. We report on ClipCard, a Cloud-based SaaS analytic platform for instant summarization, quick search, visualization and easy sharing of metadata summaries form the Dark Data at hierarchical levels of detail, thus rendering it 'white', i.e., actionable. We present a use case of the ClipCard platform, a cloud-based application which helps generate (abstracted) visual metadata summaries and meta-analytics for environmental data at hierarchical scales within and across big data containers. These summaries and analyses provide important new tools for managing big data and simplifying collaboration through easy to deploy sharing APIs. The ClipCard application solves a growing data management bottleneck by helping enterprises and large organizations to summarize, search, discover, and share the potential in their unused data and information assets. Using Cloud as the base platform enables wider reach, quick dissemination and easy sharing of the metadata summaries, without actually storing or sharing the original data assets per se.

Visual Thinking and Gender Differences in High School Calculus

ERIC Educational Resources Information Center

Haciomeroglu, Erhan Selcuk; Chicken, Eric

2012-01-01

This study sought to examine calculus students' mathematical performances and preferences for visual or analytic thinking regarding derivative and antiderivative tasks presented graphically. It extends previous studies by investigating factors mediating calculus students' mathematical performances and their preferred modes of thinking. Data were…

Visual Basic programs for spreadsheet analysis.

PubMed

Hunt, Bruce

2005-01-01

A collection of Visual Basic programs, entitled Function.xls, has been written for ground water spreadsheet calculations. This collection includes programs for calculating mathematical functions and for evaluating analytical solutions in ground water hydraulics and contaminant transport. Several spreadsheet examples are given to illustrate their use.

The challenge of big data in public health: an opportunity for visual analytics.

PubMed

Ola, Oluwakemi; Sedig, Kamran

2014-01-01

Public health (PH) data can generally be characterized as big data. The efficient and effective use of this data determines the extent to which PH stakeholders can sufficiently address societal health concerns as they engage in a variety of work activities. As stakeholders interact with data, they engage in various cognitive activities such as analytical reasoning, decision-making, interpreting, and problem solving. Performing these activities with big data is a challenge for the unaided mind as stakeholders encounter obstacles relating to the data's volume, variety, velocity, and veracity. Such being the case, computer-based information tools are needed to support PH stakeholders. Unfortunately, while existing computational tools are beneficial in addressing certain work activities, they fall short in supporting cognitive activities that involve working with large, heterogeneous, and complex bodies of data. This paper presents visual analytics (VA) tools, a nascent category of computational tools that integrate data analytics with interactive visualizations, to facilitate the performance of cognitive activities involving big data. Historically, PH has lagged behind other sectors in embracing new computational technology. In this paper, we discuss the role that VA tools can play in addressing the challenges presented by big data. In doing so, we demonstrate the potential benefit of incorporating VA tools into PH practice, in addition to highlighting the need for further systematic and focused research.

Process monitoring and visualization solutions for hot-melt extrusion: a review.

PubMed

Saerens, Lien; Vervaet, Chris; Remon, Jean Paul; De Beer, Thomas

2014-02-01

Hot-melt extrusion (HME) is applied as a continuous pharmaceutical manufacturing process for the production of a variety of dosage forms and formulations. To ensure the continuity of this process, the quality of the extrudates must be assessed continuously during manufacturing. The objective of this review is to provide an overview and evaluation of the available process analytical techniques which can be applied in hot-melt extrusion. Pharmaceutical extruders are equipped with traditional (univariate) process monitoring tools, observing barrel and die temperatures, throughput, screw speed, torque, drive amperage, melt pressure and melt temperature. The relevance of several spectroscopic process analytical techniques for monitoring and control of pharmaceutical HME has been explored recently. Nevertheless, many other sensors visualizing HME and measuring diverse critical product and process parameters with potential use in pharmaceutical extrusion are available, and were thoroughly studied in polymer extrusion. The implementation of process analytical tools in HME serves two purposes: (1) improving process understanding by monitoring and visualizing the material behaviour and (2) monitoring and analysing critical product and process parameters for process control, allowing to maintain a desired process state and guaranteeing the quality of the end product. This review is the first to provide an evaluation of the process analytical tools applied for pharmaceutical HME monitoring and control, and discusses techniques that have been used in polymer extrusion having potential for monitoring and control of pharmaceutical HME. © 2013 Royal Pharmaceutical Society.

The Challenge of Big Data in Public Health: An Opportunity for Visual Analytics

PubMed Central

Ola, Oluwakemi; Sedig, Kamran

2014-01-01

Public health (PH) data can generally be characterized as big data. The efficient and effective use of this data determines the extent to which PH stakeholders can sufficiently address societal health concerns as they engage in a variety of work activities. As stakeholders interact with data, they engage in various cognitive activities such as analytical reasoning, decision-making, interpreting, and problem solving. Performing these activities with big data is a challenge for the unaided mind as stakeholders encounter obstacles relating to the data’s volume, variety, velocity, and veracity. Such being the case, computer-based information tools are needed to support PH stakeholders. Unfortunately, while existing computational tools are beneficial in addressing certain work activities, they fall short in supporting cognitive activities that involve working with large, heterogeneous, and complex bodies of data. This paper presents visual analytics (VA) tools, a nascent category of computational tools that integrate data analytics with interactive visualizations, to facilitate the performance of cognitive activities involving big data. Historically, PH has lagged behind other sectors in embracing new computational technology. In this paper, we discuss the role that VA tools can play in addressing the challenges presented by big data. In doing so, we demonstrate the potential benefit of incorporating VA tools into PH practice, in addition to highlighting the need for further systematic and focused research. PMID:24678376

Perspectives on making big data analytics work for oncology.

PubMed

El Naqa, Issam

2016-12-01

Oncology, with its unique combination of clinical, physical, technological, and biological data provides an ideal case study for applying big data analytics to improve cancer treatment safety and outcomes. An oncology treatment course such as chemoradiotherapy can generate a large pool of information carrying the 5Vs hallmarks of big data. This data is comprised of a heterogeneous mixture of patient demographics, radiation/chemo dosimetry, multimodality imaging features, and biological markers generated over a treatment period that can span few days to several weeks. Efforts using commercial and in-house tools are underway to facilitate data aggregation, ontology creation, sharing, visualization and varying analytics in a secure environment. However, open questions related to proper data structure representation and effective analytics tools to support oncology decision-making need to be addressed. It is recognized that oncology data constitutes a mix of structured (tabulated) and unstructured (electronic documents) that need to be processed to facilitate searching and subsequent knowledge discovery from relational or NoSQL databases. In this context, methods based on advanced analytics and image feature extraction for oncology applications will be discussed. On the other hand, the classical p (variables)≫n (samples) inference problem of statistical learning is challenged in the Big data realm and this is particularly true for oncology applications where p-omics is witnessing exponential growth while the number of cancer incidences has generally plateaued over the past 5-years leading to a quasi-linear growth in samples per patient. Within the Big data paradigm, this kind of phenomenon may yield undesirable effects such as echo chamber anomalies, Yule-Simpson reversal paradox, or misleading ghost analytics. In this work, we will present these effects as they pertain to oncology and engage small thinking methodologies to counter these effects ranging from incorporating prior knowledge, using information-theoretic techniques to modern ensemble machine learning approaches or combination of these. We will particularly discuss the pros and cons of different approaches to improve mining of big data in oncology. Copyright © 2016 Elsevier Inc. All rights reserved.

Safety and Suitability for Service Assessment Testing for Surface and Underwater Launched Munitions

DTIC Science & Technology

2014-12-05

test efficiency that tend to associate the Analytical S3 Test Approach with large, complex munition systems and the Empirical S3 Test Approach with...the smaller, less complex munition systems . 8.1 ANALYTICAL S3 TEST APPROACH. The Analytical S3 test approach, as shown in Figure 3, evaluates...assets than the Analytical S3 Test approach to establish the safety margin of the system . This approach is generally applicable to small munitions

HERA: A dynamic web application for visualizing community exposure to flood hazards based on storm and sea level rise scenarios

NASA Astrophysics Data System (ADS)

Jones, Jeanne M.; Henry, Kevin; Wood, Nathan; Ng, Peter; Jamieson, Matthew

2017-12-01

The Hazard Exposure Reporting and Analytics (HERA) dynamic web application was created to provide a platform that makes research on community exposure to coastal-flooding hazards influenced by sea level rise accessible to planners, decision makers, and the public in a manner that is both easy to use and easily accessible. HERA allows users to (a) choose flood-hazard scenarios based on sea level rise and storm assumptions, (b) appreciate the modeling uncertainty behind a chosen hazard zone, (c) select one or several communities to examine exposure, (d) select the category of population or societal asset, and (e) choose how to look at results. The application is designed to highlight comparisons between (a) varying levels of sea level rise and coastal storms, (b) communities, (c) societal asset categories, and (d) spatial scales. Through a combination of spatial and graphical visualizations, HERA aims to help individuals and organizations to craft more informed mitigation and adaptation strategies for climate-driven coastal hazards. This paper summarizes the technologies used to maximize the user experience, in terms of interface design, visualization approaches, and data processing.

HERA: A dynamic web application for visualizing community exposure to flood hazards based on storm and sea level rise scenarios

USGS Publications Warehouse

Jones, Jeanne M.; Henry, Kevin; Wood, Nathan J.; Ng, Peter; Jamieson, Matthew

2017-01-01

The Hazard Exposure Reporting and Analytics (HERA) dynamic web application was created to provide a platform that makes research on community exposure to coastal-flooding hazards influenced by sea level rise accessible to planners, decision makers, and the public in a manner that is both easy to use and easily accessible. HERA allows users to (a) choose flood-hazard scenarios based on sea level rise and storm assumptions, (b) appreciate the modeling uncertainty behind a chosen hazard zone, (c) select one or several communities to examine exposure, (d) select the category of population or societal asset, and (e) choose how to look at results. The application is designed to highlight comparisons between (a) varying levels of sea level rise and coastal storms, (b) communities, (c) societal asset categories, and (d) spatial scales. Through a combination of spatial and graphical visualizations, HERA aims to help individuals and organizations to craft more informed mitigation and adaptation strategies for climate-driven coastal hazards. This paper summarizes the technologies used to maximize the user experience, in terms of interface design, visualization approaches, and data processing.

a Web-Based Framework for Visualizing Industrial Spatiotemporal Distribution Using Standard Deviational Ellipse and Shifting Routes of Gravity Centers

NASA Astrophysics Data System (ADS)

Song, Y.; Gui, Z.; Wu, H.; Wei, Y.

2017-09-01

Analysing spatiotemporal distribution patterns and its dynamics of different industries can help us learn the macro-level developing trends of those industries, and in turn provides references for industrial spatial planning. However, the analysis process is challenging task which requires an easy-to-understand information presentation mechanism and a powerful computational technology to support the visual analytics of big data on the fly. Due to this reason, this research proposes a web-based framework to enable such a visual analytics requirement. The framework uses standard deviational ellipse (SDE) and shifting route of gravity centers to show the spatial distribution and yearly developing trends of different enterprise types according to their industry categories. The calculation of gravity centers and ellipses is paralleled using Apache Spark to accelerate the processing. In the experiments, we use the enterprise registration dataset in Mainland China from year 1960 to 2015 that contains fine-grain location information (i.e., coordinates of each individual enterprise) to demonstrate the feasibility of this framework. The experiment result shows that the developed visual analytics method is helpful to understand the multi-level patterns and developing trends of different industries in China. Moreover, the proposed framework can be used to analyse any nature and social spatiotemporal point process with large data volume, such as crime and disease.

Toward a Visualization-Supported Workflow for Cyber Alert Management using Threat Models and Human-Centered Design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Franklin, Lyndsey; Pirrung, Megan A.; Blaha, Leslie M.

Cyber network analysts follow complex processes in their investigations of potential threats to their network. Much research is dedicated to providing automated tool support in the effort to make their tasks more efficient, accurate, and timely. This tool support comes in a variety of implementations from machine learning algorithms that monitor streams of data to visual analytic environments for exploring rich and noisy data sets. Cyber analysts, however, often speak of a need for tools which help them merge the data they already have and help them establish appropriate baselines against which to compare potential anomalies. Furthermore, existing threat modelsmore » that cyber analysts regularly use to structure their investigation are not often leveraged in support tools. We report on our work with cyber analysts to understand they analytic process and how one such model, the MITRE ATT&CK Matrix [32], is used to structure their analytic thinking. We present our efforts to map specific data needed by analysts into the threat model to inform our eventual visualization designs. We examine data mapping for gaps where the threat model is under-supported by either data or tools. We discuss these gaps as potential design spaces for future research efforts. We also discuss the design of a prototype tool that combines machine-learning and visualization components to support cyber analysts working with this threat model.« less

Finding Waldo: Learning about Users from their Interactions.

PubMed

Brown, Eli T; Ottley, Alvitta; Zhao, Helen; Quan Lin; Souvenir, Richard; Endert, Alex; Chang, Remco

2014-12-01

Visual analytics is inherently a collaboration between human and computer. However, in current visual analytics systems, the computer has limited means of knowing about its users and their analysis processes. While existing research has shown that a user's interactions with a system reflect a large amount of the user's reasoning process, there has been limited advancement in developing automated, real-time techniques that mine interactions to learn about the user. In this paper, we demonstrate that we can accurately predict a user's task performance and infer some user personality traits by using machine learning techniques to analyze interaction data. Specifically, we conduct an experiment in which participants perform a visual search task, and apply well-known machine learning algorithms to three encodings of the users' interaction data. We achieve, depending on algorithm and encoding, between 62% and 83% accuracy at predicting whether each user will be fast or slow at completing the task. Beyond predicting performance, we demonstrate that using the same techniques, we can infer aspects of the user's personality factors, including locus of control, extraversion, and neuroticism. Further analyses show that strong results can be attained with limited observation time: in one case 95% of the final accuracy is gained after a quarter of the average task completion time. Overall, our findings show that interactions can provide information to the computer about its human collaborator, and establish a foundation for realizing mixed-initiative visual analytics systems.

Teaching Science through Pictorial Models during Read-Alouds

ERIC Educational Resources Information Center

Oliveira, Alandeom W.; Rivera, Seema; Glass, Rory; Mastroianni, Michael; Wizner, Francine; Amodeo, Vincent

2013-01-01

This study examines how three elementary teachers refer to pictorial models (photographs, drawings, and cartoons) during science read-alouds. While one teacher used realistic photographs for the purpose of visually verifying facts about crystals, another employed analytical diagrams as heuristic tools to help students visualize complex target…

75 FR 53262 - Privacy Act of 1974; System of Records

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-31

... a new Privacy Act system of records, JUSTICE/FBI- 021, the Data Integration and Visualization System... Act system of records, the Data Integration and Visualization System (DIVS), Justice/FBI-021. The... investigative mission by enabling access, search, integration, and analytics across multiple existing databases...

Reimagining Khan Analytics for Student Coaches

ERIC Educational Resources Information Center

Cunningham, Jim

2015-01-01

In this paper, I describe preliminary work on a new research project in learning analytics at Arizona State University. In conjunction with an innovative remedial mathematics course using Khan Academy and student coaches, this study seeks to measure the effectiveness of visualized data in assisting student coaches as they help remedial math…

LATUX: An Iterative Workflow for Designing, Validating, and Deploying Learning Analytics Visualizations

ERIC Educational Resources Information Center

Martinez-Maldonado, Roberto; Pardo, Abelardo; Mirriahi, Negin; Yacef, Kalina; Kay, Judy; Clayphan, Andrew

2015-01-01

Designing, validating, and deploying learning analytics tools for instructors or students is a challenge that requires techniques and methods from different disciplines, such as software engineering, human-computer interaction, computer graphics, educational design, and psychology. Whilst each has established its own design methodologies, we now…

Instrumentation: Photodiode Array Detectors in UV-VIS Spectroscopy. Part II.

ERIC Educational Resources Information Center

Jones, Dianna G.

1985-01-01

A previous part (Analytical Chemistry; v57 n9 p1057A) discussed the theoretical aspects of diode ultraviolet-visual (UV-VIS) spectroscopy. This part describes the applications of diode arrays in analytical chemistry, also considering spectroelectrochemistry, high performance liquid chromatography (HPLC), HPLC data processing, stopped flow, and…

StreamSqueeze: a dynamic stream visualization for monitoring of event data

NASA Astrophysics Data System (ADS)

Mansmann, Florian; Krstajic, Milos; Fischer, Fabian; Bertini, Enrico

2012-01-01

While in clear-cut situations automated analytical solution for data streams are already in place, only few visual approaches have been proposed in the literature for exploratory analysis tasks on dynamic information. However, due to the competitive or security-related advantages that real-time information gives in domains such as finance, business or networking, we are convinced that there is a need for exploratory visualization tools for data streams. Under the conditions that new events have higher relevance and that smooth transitions enable traceability of items, we propose a novel dynamic stream visualization called StreamSqueeze. In this technique the degree of interest of recent items is expressed through an increase in size and thus recent events can be shown with more details. The technique has two main benefits: First, the layout algorithm arranges items in several lists of various sizes and optimizes the positions within each list so that the transition of an item from one list to the other triggers least visual changes. Second, the animation scheme ensures that for 50 percent of the time an item has a static screen position where reading is most effective and then continuously shrinks and moves to the its next static position in the subsequent list. To demonstrate the capability of our technique, we apply it to large and high-frequency news and syslog streams and show how it maintains optimal stability of the layout under the conditions given above.

Biological basis for space-variant sensor design I: parameters of monkey and human spatial vision

NASA Astrophysics Data System (ADS)

Rojer, Alan S.; Schwartz, Eric L.

1991-02-01

Biological sensor design has long provided inspiration for sensor design in machine vision. However relatively little attention has been paid to the actual design parameters provided by biological systems as opposed to the general nature of biological vision architectures. In the present paper we will provide a review of current knowledge of primate spatial vision design parameters and will present recent experimental and modeling work from our lab which demonstrates that a numerical conformal mapping which is a refinement of our previous complex logarithmic model provides the best current summary of this feature of the primate visual system. In this paper we will review recent work from our laboratory which has characterized some of the spatial architectures of the primate visual system. In particular we will review experimental and modeling studies which indicate that: . The global spatial architecture of primate visual cortex is well summarized by a numerical conformal mapping whose simplest analytic approximation is the complex logarithm function . The columnar sub-structure of primate visual cortex can be well summarized by a model based on a band-pass filtered white noise. We will also refer to ongoing work in our lab which demonstrates that: . The joint columnar/map structure of primate visual cortex can be modeled and summarized in terms of a new algorithm the ''''proto-column'''' algorithm. This work provides a reference-point for current engineering approaches to novel architectures for

Using Secure Web Services to Visualize Poison Center Data for Nationwide Biosurveillance: A Case Study

PubMed Central

Savel, Thomas G; Bronstein, Alvin; Duck, William; Rhodes, M. Barry; Lee, Brian; Stinn, John; Worthen, Katherine

2010-01-01

Objectives Real-time surveillance systems are valuable for timely response to public health emergencies. It has been challenging to leverage existing surveillance systems in state and local communities, and, using a centralized architecture, add new data sources and analytical capacity. Because this centralized model has proven to be difficult to maintain and enhance, the US Centers for Disease Control and Prevention (CDC) has been examining the ability to use a federated model based on secure web services architecture, with data stewardship remaining with the data provider. Methods As a case study for this approach, the American Association of Poison Control Centers and the CDC extended an existing data warehouse via a secure web service, and shared aggregate clinical effects and case counts data by geographic region and time period. To visualize these data, CDC developed a web browser-based interface, Quicksilver, which leveraged the Google Maps API and Flot, a javascript plotting library. Results Two iterations of the NPDS web service were completed in 12 weeks. The visualization client, Quicksilver, was developed in four months. Discussion This implementation of web services combined with a visualization client represents incremental positive progress in transitioning national data sources like BioSense and NPDS to a federated data exchange model. Conclusion Quicksilver effectively demonstrates how the use of secure web services in conjunction with a lightweight, rapidly deployed visualization client can easily integrate isolated data sources for biosurveillance. PMID:23569581

Situated cognition in clinical visualization: the role of transparency in GammaKnife neurosurgery planning.

PubMed

Dinka, David; Nyce, James M; Timpka, Toomas

2009-06-01

The aim of this study was to investigate how the clinical use of visualization technology can be advanced by the application of a situated cognition perspective. The data were collected in the GammaKnife radiosurgery setting and analyzed using qualitative methods. Observations and in-depth interviews with neurosurgeons and physicists were performed at three clinics using the Leksell GammaKnife. The users' ability to perform cognitive tasks was found to be reduced each time visualizations incongruent with the particular user's perception of clinical reality were used. The main issue here was a lack of transparency, i.e. a black box problem where machine representations "stood between" users and the cognitive tasks they wanted to perform. For neurosurgeons, transparency meant their previous experience from traditional surgery could be applied, i.e. that they were not forced to perform additional cognitive work. From the view of the physicists, on the other hand, the concept of transparency was associated with mathematical precision and avoiding creating a cognitive distance between basic patient data and what is experienced as clinical reality. The physicists approached clinical visualization technology as though it was a laboratory apparatus--one that required continual adjustment and assessment in order to "capture" a quantitative clinical reality. Designers of visualization technology need to compare the cognitive interpretations generated by the new visualization systems to conceptions generated during "traditional" clinical work. This means that the viewpoint of different clinical user groups involved in a given clinical task would have to be taken into account as well. A way forward would be to acknowledge that visualization is a socio-cognitive function that has practice-based antecedents and consequences, and to reconsider what analytical and scientific challenges this presents us with.

Enhanced peripheral visual processing in congenitally deaf humans is supported by multiple brain regions, including primary auditory cortex.

PubMed

Scott, Gregory D; Karns, Christina M; Dow, Mark W; Stevens, Courtney; Neville, Helen J

2014-01-01

Brain reorganization associated with altered sensory experience clarifies the critical role of neuroplasticity in development. An example is enhanced peripheral visual processing associated with congenital deafness, but the neural systems supporting this have not been fully characterized. A gap in our understanding of deafness-enhanced peripheral vision is the contribution of primary auditory cortex. Previous studies of auditory cortex that use anatomical normalization across participants were limited by inter-subject variability of Heschl's gyrus. In addition to reorganized auditory cortex (cross-modal plasticity), a second gap in our understanding is the contribution of altered modality-specific cortices (visual intramodal plasticity in this case), as well as supramodal and multisensory cortices, especially when target detection is required across contrasts. Here we address these gaps by comparing fMRI signal change for peripheral vs. perifoveal visual stimulation (11-15° vs. 2-7°) in congenitally deaf and hearing participants in a blocked experimental design with two analytical approaches: a Heschl's gyrus region of interest analysis and a whole brain analysis. Our results using individually-defined primary auditory cortex (Heschl's gyrus) indicate that fMRI signal change for more peripheral stimuli was greater than perifoveal in deaf but not in hearing participants. Whole-brain analyses revealed differences between deaf and hearing participants for peripheral vs. perifoveal visual processing in extrastriate visual cortex including primary auditory cortex, MT+/V5, superior-temporal auditory, and multisensory and/or supramodal regions, such as posterior parietal cortex (PPC), frontal eye fields, anterior cingulate, and supplementary eye fields. Overall, these data demonstrate the contribution of neuroplasticity in multiple systems including primary auditory cortex, supramodal, and multisensory regions, to altered visual processing in congenitally deaf adults.

Network analysis for the visualization and analysis of qualitative data.

PubMed

Pokorny, Jennifer J; Norman, Alex; Zanesco, Anthony P; Bauer-Wu, Susan; Sahdra, Baljinder K; Saron, Clifford D

2018-03-01

We present a novel manner in which to visualize the coding of qualitative data that enables representation and analysis of connections between codes using graph theory and network analysis. Network graphs are created from codes applied to a transcript or audio file using the code names and their chronological location. The resulting network is a representation of the coding data that characterizes the interrelations of codes. This approach enables quantification of qualitative codes using network analysis and facilitates examination of associations of network indices with other quantitative variables using common statistical procedures. Here, as a proof of concept, we applied this method to a set of interview transcripts that had been coded in 2 different ways and the resultant network graphs were examined. The creation of network graphs allows researchers an opportunity to view and share their qualitative data in an innovative way that may provide new insights and enhance transparency of the analytical process by which they reach their conclusions. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Analytic modeling of aerosol size distributions

NASA Technical Reports Server (NTRS)

Deepack, A.; Box, G. P.

1979-01-01

Mathematical functions commonly used for representing aerosol size distributions are studied parametrically. Methods for obtaining best fit estimates of the parameters are described. A catalog of graphical plots depicting the parametric behavior of the functions is presented along with procedures for obtaining analytical representations of size distribution data by visual matching of the data with one of the plots. Examples of fitting the same data with equal accuracy by more than one analytic model are also given.

Seeking Information with an Information Visualization System: A Study of Cognitive Styles

ERIC Educational Resources Information Center

Yuan, Xiaojun; Zhang, Xiangman; Chen, Chaomei; Avery, Joshua M.

2011-01-01

Introduction: This study investigated the effect of cognitive styles on users' information-seeking task performance using a knowledge domain information visualization system called CiteSpace. Method: Sixteen graduate students participated in a user experiment. Each completed an extended cognitive style analysis wholistic-analytic test (the…

Improving Student Performance Using Nudge Analytics

ERIC Educational Resources Information Center

Feild, Jacqueline

2015-01-01

Providing students with continuous and personalized feedback on their performance is an important part of encouraging self regulated learning. As part of our higher education platform, we built a set of data visualizations to provide feedback to students on their assignment performance. These visualizations give students information about how they…

Innovative Didactic Designs: Visual Analytics and Visual Literacy in School

ERIC Educational Resources Information Center

Stenliden, Linnéa; Nissen, Jörgen; Bodén, Ulrika

2017-01-01

In a world of massively mediated information and communication, students must learn to handle rapidly growing information volumes inside and outside school. Pedagogy attuned to processing this growing production and communication of information is needed. However, ordinary educational models often fail to support students, trialing neither…

Understanding wax screen-printing: a novel patterning process for microfluidic cloth-based analytical devices.

PubMed

Liu, Min; Zhang, Chunsun; Liu, Feifei

2015-09-03

In this work, we first introduce the fabrication of microfluidic cloth-based analytical devices (μCADs) using a wax screen-printing approach that is suitable for simple, inexpensive, rapid, low-energy-consumption and high-throughput preparation of cloth-based analytical devices. We have carried out a detailed study on the wax screen-printing of μCADs and have obtained some interesting results. Firstly, an analytical model is established for the spreading of molten wax in cloth. Secondly, a new wax screen-printing process has been proposed for fabricating μCADs, where the melting of wax into the cloth is much faster (∼5 s) and the heating temperature is much lower (75 °C). Thirdly, the experimental results show that the patterning effects of the proposed wax screen-printing method depend to a certain extent on types of screens, wax melting temperatures and melting time. Under optimized conditions, the minimum printing width of hydrophobic wax barrier and hydrophilic channel is 100 μm and 1.9 mm, respectively. Importantly, the developed analytical model is also well validated by these experiments. Fourthly, the μCADs fabricated by the presented wax screen-printing method are used to perform a proof-of-concept assay of glucose or protein in artificial urine with rapid high-throughput detection taking place on a 48-chamber cloth-based device and being performed by a visual readout. Overall, the developed cloth-based wax screen-printing and arrayed μCADs should provide a new research direction in the development of advanced sensor arrays for detection of a series of analytes relevant to many diverse applications. Copyright © 2015 Elsevier B.V. All rights reserved.

The identification of van Hiele level students on the topic of space analytic geometry

NASA Astrophysics Data System (ADS)

Yudianto, E.; Sunardi; Sugiarti, T.; Susanto; Suharto; Trapsilasiwi, D.

2018-03-01

Geometry topics are still considered difficult by most students. Therefore, this study focused on the identification of students related to van Hiele levels. The task used from result of the development of questions related to analytical geometry of space. The results of the work involving 78 students who worked on these questions covered 11.54% (nine students) classified on a visual level; 5.13% (four students) on analysis level; 1.28% (one student) on informal deduction level; 2.56% (two students) on deduction and 2.56% (two students) on rigor level, and 76.93% (sixty students) classified on the pre-visualization level.

T.Rex Visual Analytics for Transactional Exploration

ScienceCinema

None

2018-01-16

T.Rex is PNNL's visual analytics tool that specializes in tabular structured data, like you might open with Excel. It's a client-server application, allowing the server to do a lot of the heavy lifting and the client to open spreadsheets with millions of rows. With datasets of that size, especially if you're unfamiliar with the contents, it's very hard to get a good grasp of what's in it using traditional tools. With T.Rex, the multiple views allow you to see categorical, temporal, numerical, relational, and summary data. The interactivity lets you look across your data and see how things relate to each other.

T.Rex Visual Analytics for Transactional Exploration

DOE Office of Scientific and Technical Information (OSTI.GOV)

None

2014-07-01

T.Rex is PNNL's visual analytics tool that specializes in tabular structured data, like you might open with Excel. It's a client-server application, allowing the server to do a lot of the heavy lifting and the client to open spreadsheets with millions of rows. With datasets of that size, especially if you're unfamiliar with the contents, it's very hard to get a good grasp of what's in it using traditional tools. With T.Rex, the multiple views allow you to see categorical, temporal, numerical, relational, and summary data. The interactivity lets you look across your data and see how things relate tomore » each other.« less

Visual communication in the psychoanalytic situation.

PubMed

Kanzer, M

1980-01-01

The relationship between verbal and visual aspects of the analytic proceedings shows them blended integrally in the experiences of both patient and analyst and in contributing to the insights derived during the treatment. Areas in which the admixture of the verbal and visual occur are delineated. Awareness of the visual aspects gives substance to the operations of empathy, intuition, acting out, working through, etc. Some typical features of visual 'language" are noted and related to the analytic situation. As such they can be translated with the use of logic and consciousness on the analyst's part, not mere random eruptions of intuition. The original significance of dreams as a royal road to the unconscious is confirmed-but we also find in them insights to be derived with higher mental processes. Finally, dyadic aspects of the formation and aims of dreams during analysis are pointed out, with important implications for the analyst's own self-supervision of his techniques and 'real personality" and their effects upon the patient. how remarkable that Dora's dreams, all too belatedly teaching Freud about their transference implications, still have so much more to communicate that derives from his capacity to record faithfully observations he was not yet ready to explain.

Visual Analytics approach for Lightning data analysis and cell nowcasting

NASA Astrophysics Data System (ADS)

Peters, Stefan; Meng, Liqiu; Betz, Hans-Dieter

2013-04-01

Thunderstorms and their ground effects, such as flash floods, hail, lightning, strong wind and tornadoes, are responsible for most weather damages (Bonelli & Marcacci 2008). Thus to understand, identify, track and predict lightning cells is essential. An important aspect for decision makers is an appropriate visualization of weather analysis results including the representation of dynamic lightning cells. This work focuses on the visual analysis of lightning data and lightning cell nowcasting which aim to detect and understanding spatial-temporal patterns of moving thunderstorms. Lightnings are described by 3D coordinates and the exact occurrence time of lightnings. The three-dimensionally resolved total lightning data used in our experiment are provided by the European lightning detection network LINET (Betz et al. 2009). In all previous works, lightning point data, detected lightning cells and derived cell tracks are visualized in 2D. Lightning cells are either displayed as 2D convex hulls with or without the underlying lightning point data. Due to recent improvements of lightning data detection and accuracy, there is a growing demand on multidimensional and interactive visualization in particular for decision makers. In a first step lightning cells are identified and tracked. Then an interactive graphic user interface (GUI) is developed to investigate the dynamics of the lightning cells: e.g. changes of cell density, location, extension as well as merging and splitting behavior in 3D over time. In particular a space time cube approach is highlighted along with statistical analysis. Furthermore a lightning cell nowcasting is conducted and visualized. The idea thereby is to predict the following cell features for the next 10-60 minutes including location, centre, extension, density, area, volume, lifetime and cell feature probabilities. The main focus will be set to a suitable interactive visualization of the predicted featured within the GUI. The developed visual exploring tool for the purpose of supporting decision making is investigated for two determined user groups: lightning experts and interested lay public. Betz HD, Schmidt K, Oettinger WP (2009) LINET - An International VLF/LF Lightning Detection Network in Europe. In: Betz HD, Schumann U, Laroche P (eds) Lightning: Principles, Instruments and Applications. Springer Netherlands, Dordrecht, pp 115-140 Bonelli P, Marcacci P (2008) Thunderstorm nowcasting by means of lightning and radar data: algorithms and applications in northern Italy. Nat. Hazards Earth Syst. Sci 8(5):1187-1198

A big data approach for climate change indicators processing in the CLIP-C project

NASA Astrophysics Data System (ADS)

D'Anca, Alessandro; Conte, Laura; Palazzo, Cosimo; Fiore, Sandro; Aloisio, Giovanni

2016-04-01

Defining and implementing processing chains with multiple (e.g. tens or hundreds of) data analytics operators can be a real challenge in many practical scientific use cases such as climate change indicators. This is usually done via scripts (e.g. bash) on the client side and requires climate scientists to take care of, implement and replicate workflow-like control logic aspects (which may be error-prone too) in their scripts, along with the expected application-level part. Moreover, the big amount of data and the strong I/O demand pose additional challenges related to the performance. In this regard, production-level tools for climate data analysis are mostly sequential and there is a lack of big data analytics solutions implementing fine-grain data parallelism or adopting stronger parallel I/O strategies, data locality, workflow optimization, etc. High-level solutions leveraging on workflow-enabled big data analytics frameworks for eScience could help scientists in defining and implementing the workflows related to their experiments by exploiting a more declarative, efficient and powerful approach. This talk will start introducing the main needs and challenges regarding big data analytics workflow management for eScience and will then provide some insights about the implementation of some real use cases related to some climate change indicators on large datasets produced in the context of the CLIP-C project - a EU FP7 project aiming at providing access to climate information of direct relevance to a wide variety of users, from scientists to policy makers and private sector decision makers. All the proposed use cases have been implemented exploiting the Ophidia big data analytics framework. The software stack includes an internal workflow management system, which coordinates, orchestrates, and optimises the execution of multiple scientific data analytics and visualization tasks. Real-time workflow monitoring execution is also supported through a graphical user interface. In order to address the challenges of the use cases, the implemented data analytics workflows include parallel data analysis, metadata management, virtual file system tasks, maps generation, rolling of datasets, and import/export of datasets in NetCDF format. The use cases have been implemented on a HPC cluster of 8-nodes (16-cores/node) of the Athena Cluster available at the CMCC Supercomputing Centre. Benchmark results will be also presented during the talk.

PB-AM: An open-source, fully analytical linear poisson-boltzmann solver.

PubMed

Felberg, Lisa E; Brookes, David H; Yap, Eng-Hui; Jurrus, Elizabeth; Baker, Nathan A; Head-Gordon, Teresa

2017-06-05

We present the open source distributed software package Poisson-Boltzmann Analytical Method (PB-AM), a fully analytical solution to the linearized PB equation, for molecules represented as non-overlapping spherical cavities. The PB-AM software package includes the generation of outputs files appropriate for visualization using visual molecular dynamics, a Brownian dynamics scheme that uses periodic boundary conditions to simulate dynamics, the ability to specify docking criteria, and offers two different kinetics schemes to evaluate biomolecular association rate constants. Given that PB-AM defines mutual polarization completely and accurately, it can be refactored as a many-body expansion to explore 2- and 3-body polarization. Additionally, the software has been integrated into the Adaptive Poisson-Boltzmann Solver (APBS) software package to make it more accessible to a larger group of scientists, educators, and students that are more familiar with the APBS framework. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Capture and exploration of sample quality data to inform and improve the management of a screening collection.

PubMed

Charles, Isabel; Sinclair, Ian; Addison, Daniel H

2014-04-01

A new approach to the storage, processing, and interrogation of the quality data for screening samples has improved analytical throughput and confidence and enhanced the opportunities for learning from the accumulating records. The approach has entailed the design, development, and implementation of a database-oriented system, capturing information from the liquid chromatography-mass spectrometry capabilities used for assessing the integrity of samples in AstraZeneca's screening collection. A Web application has been developed to enable the visualization and interactive annotation of the analytical data, monitor the current sample queue, and report the throughput rate. Sample purity and identity are certified automatically on the chromatographic peaks of interest if predetermined thresholds are reached on key parameters. Using information extracted in parallel from the compound registration and container inventory databases, the chromatographic and spectroscopic profiles for each vessel are linked to the sample structures and storage histories. A search engine facilitates the direct comparison of results for multiple vessels of the same or similar compounds, for single vessels analyzed at different time points, or for vessels related by their origin or process flow. Access to this network of information has provided a deeper understanding of the multiple factors contributing to sample quality assurance.

Open chemistry: RESTful web APIs, JSON, NWChem and the modern web application.

PubMed

Hanwell, Marcus D; de Jong, Wibe A; Harris, Christopher J

2017-10-30

An end-to-end platform for chemical science research has been developed that integrates data from computational and experimental approaches through a modern web-based interface. The platform offers an interactive visualization and analytics environment that functions well on mobile, laptop and desktop devices. It offers pragmatic solutions to ensure that large and complex data sets are more accessible. Existing desktop applications/frameworks were extended to integrate with high-performance computing resources, and offer command-line tools to automate interaction-connecting distributed teams to this software platform on their own terms. The platform was developed openly, and all source code hosted on the GitHub platform with automated deployment possible using Ansible coupled with standard Ubuntu-based machine images deployed to cloud machines. The platform is designed to enable teams to reap the benefits of the connected web-going beyond what conventional search and analytics platforms offer in this area. It also has the goal of offering federated instances, that can be customized to the sites/research performed. Data gets stored using JSON, extending upon previous approaches using XML, building structures that support computational chemistry calculations. These structures were developed to make it easy to process data across different languages, and send data to a JavaScript-based web client.

Protein Multiplexed Immunoassay Analysis with R.

PubMed

Breen, Edmond J

2017-01-01

Plasma samples from 177 control and type 2 diabetes patients collected at three Australian hospitals are screened for 14 analytes using six custom-made multiplex kits across 60 96-well plates. In total 354 samples were collected from the patients, representing one baseline and one end point sample from each patient. R methods and source code for analyzing the analyte fluorescence response obtained from these samples by Luminex Bio-Plex ® xMap multiplexed immunoassay technology are disclosed. Techniques and R procedures for reading Bio-Plex ® result files for statistical analysis and data visualization are also presented. The need for technical replicates and the number of technical replicates are addressed as well as plate layout design strategies. Multinomial regression is used to determine plate to sample covariate balance. Methods for matching clinical covariate information to Bio-Plex ® results and vice versa are given. As well as methods for measuring and inspecting the quality of the fluorescence responses are presented. Both fixed and mixed-effect approaches for immunoassay statistical differential analysis are presented and discussed. A random effect approach to outlier analysis and detection is also shown. The bioinformatics R methodology present here provides a foundation for rigorous and reproducible analysis of the fluorescence response obtained from multiplexed immunoassays.

Open chemistry: RESTful web APIs, JSON, NWChem and the modern web application

DOE PAGES

Hanwell, Marcus D.; de Jong, Wibe A.; Harris, Christopher J.

2017-10-30

An end-to-end platform for chemical science research has been developed that integrates data from computational and experimental approaches through a modern web-based interface. The platform offers an interactive visualization and analytics environment that functions well on mobile, laptop and desktop devices. It offers pragmatic solutions to ensure that large and complex data sets are more accessible. Existing desktop applications/frameworks were extended to integrate with high-performance computing resources, and offer command-line tools to automate interaction - connecting distributed teams to this software platform on their own terms. The platform was developed openly, and all source code hosted on the GitHub platformmore » with automated deployment possible using Ansible coupled with standard Ubuntu-based machine images deployed to cloud machines. The platform is designed to enable teams to reap the benefits of the connected web - going beyond what conventional search and analytics platforms offer in this area. It also has the goal of offering federated instances, that can be customized to the sites/research performed. Data gets stored using JSON, extending upon previous approaches using XML, building structures that support computational chemistry calculations. These structures were developed to make it easy to process data across different languages, and send data to a JavaScript-based web client.« less

Open chemistry: RESTful web APIs, JSON, NWChem and the modern web application

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hanwell, Marcus D.; de Jong, Wibe A.; Harris, Christopher J.

An end-to-end platform for chemical science research has been developed that integrates data from computational and experimental approaches through a modern web-based interface. The platform offers an interactive visualization and analytics environment that functions well on mobile, laptop and desktop devices. It offers pragmatic solutions to ensure that large and complex data sets are more accessible. Existing desktop applications/frameworks were extended to integrate with high-performance computing resources, and offer command-line tools to automate interaction - connecting distributed teams to this software platform on their own terms. The platform was developed openly, and all source code hosted on the GitHub platformmore » with automated deployment possible using Ansible coupled with standard Ubuntu-based machine images deployed to cloud machines. The platform is designed to enable teams to reap the benefits of the connected web - going beyond what conventional search and analytics platforms offer in this area. It also has the goal of offering federated instances, that can be customized to the sites/research performed. Data gets stored using JSON, extending upon previous approaches using XML, building structures that support computational chemistry calculations. These structures were developed to make it easy to process data across different languages, and send data to a JavaScript-based web client.« less

Effect-Based Screening Methods for Water Quality Characterization Will Augment Conventional Analyte-by-Analyte Chemical Methods in Research As Well As Regulatory Monitoring

EPA Science Inventory

Conventional approaches to water quality characterization can provide data on individual chemical components of each water sample. This analyte-by-analyte approach currently serves many useful research and compliance monitoring needs. However these approaches, which require a ...

A mass spectrometry imaging approach for investigating how drug-drug interactions influence drug blood-brain barrier permeability.

PubMed

Vallianatou, Theodosia; Strittmatter, Nicole; Nilsson, Anna; Shariatgorji, Mohammadreza; Hamm, Gregory; Pereira, Marcela; Källback, Patrik; Svenningsson, Per; Karlgren, Maria; Goodwin, Richard J A; Andrén, Per E

2018-05-15

There is a high need to develop quantitative imaging methods capable of providing detailed brain localization information of several molecular species simultaneously. In addition, extensive information on the effect of the blood-brain barrier on the penetration, distribution and efficacy of neuroactive compounds is required. Thus, we have developed a mass spectrometry imaging method to visualize and quantify the brain distribution of drugs with varying blood-brain barrier permeability. With this approach, we were able to determine blood-brain barrier transport of different drugs and define the drug distribution in very small brain structures (e.g., choroid plexus) due to the high spatial resolution provided. Simultaneously, we investigated the effect of drug-drug interactions by inhibiting the membrane transporter multidrug resistance 1 protein. We propose that the described approach can serve as a valuable analytical tool during the development of neuroactive drugs, as it can provide physiologically relevant information often neglected by traditional imaging technologies. Copyright © 2018. Published by Elsevier Inc.

Visualization and Analytics Tools for Infectious Disease Epidemiology: A Systematic Review

PubMed Central

Carroll, Lauren N.; Au, Alan P.; Detwiler, Landon Todd; Fu, Tsung-chieh; Painter, Ian S.; Abernethy, Neil F.

2014-01-01

Background A myriad of new tools and algorithms have been developed to help public health professionals analyze and visualize the complex data used in infectious disease control. To better understand approaches to meet these users' information needs, we conducted a systematic literature review focused on the landscape of infectious disease visualization tools for public health professionals, with a special emphasis on geographic information systems (GIS), molecular epidemiology, and social network analysis. The objectives of this review are to: (1) Identify public health user needs and preferences for infectious disease information visualization tools; (2) Identify existing infectious disease information visualization tools and characterize their architecture and features; (3) Identify commonalities among approaches applied to different data types; and (4) Describe tool usability evaluation efforts and barriers to the adoption of such tools. Methods We identified articles published in English from January 1, 1980 to June 30, 2013 from five bibliographic databases. Articles with a primary focus on infectious disease visualization tools, needs of public health users, or usability of information visualizations were included in the review. Results A total of 88 articles met our inclusion criteria. Users were found to have diverse needs, preferences and uses for infectious disease visualization tools, and the existing tools are correspondingly diverse. The architecture of the tools was inconsistently described, and few tools in the review discussed the incorporation of usability studies or plans for dissemination. Many studies identified concerns regarding data sharing, confidentiality and quality. Existing tools offer a range of features and functions that allow users to explore, analyze, and visualize their data, but the tools are often for siloed applications. Commonly cited barriers to widespread adoption included lack of organizational support, access issues, and misconceptions about tool use. Discussion and Conclusion As the volume and complexity of infectious disease data increases, public health professionals must synthesize highly disparate data to facilitate communication with the public and inform decisions regarding measures to protect the public's health. Our review identified several themes: consideration of users' needs, preferences, and computer literacy; integration of tools into routine workflow; complications associated with understanding and use of visualizations; and the role of user trust and organizational support in the adoption of these tools. Interoperability also emerged as a prominent theme, highlighting challenges associated with the increasingly collaborative and interdisciplinary nature of infectious disease control and prevention. Future work should address methods for representing uncertainty and missing data to avoid misleading users as well as strategies to minimize cognitive overload. PMID:24747356

Visualization and analytics tools for infectious disease epidemiology: a systematic review.

PubMed

Carroll, Lauren N; Au, Alan P; Detwiler, Landon Todd; Fu, Tsung-Chieh; Painter, Ian S; Abernethy, Neil F

2014-10-01

A myriad of new tools and algorithms have been developed to help public health professionals analyze and visualize the complex data used in infectious disease control. To better understand approaches to meet these users' information needs, we conducted a systematic literature review focused on the landscape of infectious disease visualization tools for public health professionals, with a special emphasis on geographic information systems (GIS), molecular epidemiology, and social network analysis. The objectives of this review are to: (1) identify public health user needs and preferences for infectious disease information visualization tools; (2) identify existing infectious disease information visualization tools and characterize their architecture and features; (3) identify commonalities among approaches applied to different data types; and (4) describe tool usability evaluation efforts and barriers to the adoption of such tools. We identified articles published in English from January 1, 1980 to June 30, 2013 from five bibliographic databases. Articles with a primary focus on infectious disease visualization tools, needs of public health users, or usability of information visualizations were included in the review. A total of 88 articles met our inclusion criteria. Users were found to have diverse needs, preferences and uses for infectious disease visualization tools, and the existing tools are correspondingly diverse. The architecture of the tools was inconsistently described, and few tools in the review discussed the incorporation of usability studies or plans for dissemination. Many studies identified concerns regarding data sharing, confidentiality and quality. Existing tools offer a range of features and functions that allow users to explore, analyze, and visualize their data, but the tools are often for siloed applications. Commonly cited barriers to widespread adoption included lack of organizational support, access issues, and misconceptions about tool use. As the volume and complexity of infectious disease data increases, public health professionals must synthesize highly disparate data to facilitate communication with the public and inform decisions regarding measures to protect the public's health. Our review identified several themes: consideration of users' needs, preferences, and computer literacy; integration of tools into routine workflow; complications associated with understanding and use of visualizations; and the role of user trust and organizational support in the adoption of these tools. Interoperability also emerged as a prominent theme, highlighting challenges associated with the increasingly collaborative and interdisciplinary nature of infectious disease control and prevention. Future work should address methods for representing uncertainty and missing data to avoid misleading users as well as strategies to minimize cognitive overload. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Predicting the Development of Analytical and Creative Abilities in Upper Elementary Grades

ERIC Educational Resources Information Center

Gubbels, Joyce; Segers, Eliane; Verhoeven, Ludo

2017-01-01

In some models, intelligence has been described as a multidimensional construct comprising both analytical and creative abilities. In addition, intelligence is considered to be dynamic rather than static. A structural equation model was used to examine the predictive role of cognitive (visual short-term memory, verbal short-term memory, selective…

Evidence for Holistic Representations of Ignored Images and Analytic Representations of Attended Images

ERIC Educational Resources Information Center

Thoma, Volker; Hummel, John E.; Davidoff, Jules

2004-01-01

According to the hybrid theory of object recognition (J. E. Hummel, 2001), ignored object images are represented holistically, and attended images are represented both holistically and analytically. This account correctly predicts patterns of visual priming as a function of translation, scale (B. J. Stankiewicz & J. E. Hummel, 2002), and…

Large High Resolution Displays for Co-Located Collaborative Sensemaking: Display Usage and Territoriality

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bradel, Lauren; Endert, Alexander; Koch, Kristen

2013-08-01

Large, high-resolution vertical displays carry the potential to increase the accuracy of collaborative sensemaking, given correctly designed visual analytics tools. From an exploratory user study using a fictional textual intelligence analysis task, we investigated how users interact with the display to construct spatial schemas and externalize information, as well as how they establish shared and private territories. We investigated the space management strategies of users partitioned by type of tool philosophy followed (visualization- or text-centric). We classified the types of territorial behavior exhibited in terms of how the users interacted with information on the display (integrated or independent workspaces). Next,more » we examined how territorial behavior impacted the common ground between the pairs of users. Finally, we offer design suggestions for building future co-located collaborative visual analytics tools specifically for use on large, high-resolution vertical displays.« less

Exploratory Visual Analytics of a Dynamically Built Network of Nodes in a WebGL-Enabled Browser

DTIC Science & Technology

2014-01-01

dimensionality reduction, feature extraction, high-dimensional data, t-distributed stochastic neighbor embedding, neighbor retrieval visualizer, visual...WebGL-enabled rendering is supported natively by browsers such as the latest Mozilla Firefox , Google Chrome, and Microsoft Internet Explorer 11. At the...appropriate names. The resultant 26-node network is displayed in a Mozilla Firefox browser in figure 2 (also see appendix B). 3 Figure 1. The

Advanced Video Activity Analytics (AVAA): Human Factors Evaluation

DTIC Science & Technology

2015-05-01

video, and 3) creating and saving annotations (Fig. 11). (The logging program was updated after the pilot to also capture search clicks.) Playing and... visual search task and the auditory task together and thus automatically focused on the visual task. Alternatively, the operator may have intentionally...affect performance on the primary task; however, in the current test there was no apparent effect on the operator’s performance in the visual search task

HyFinBall: A Two-Handed, Hybrid 2D/3D Desktop VR Interface for Visualization

DTIC Science & Technology

2013-01-01

user study . This is done in the context of a rich, visual analytics interface containing coordinated views with 2D and 3D visualizations and...the user interface (hardware and software), the design space, as well as preliminary results of a formal user study . This is done in the context of a ... virtual reality , user interface , two-handed interface , hybrid user interface , multi-touch, gesture,

Geovisualization applications to examine and explore high-density and hierarchical critical infrastructure data

NASA Astrophysics Data System (ADS)

Edsall, Robert; Hembree, Harvey

2018-05-01

The geospatial research and development team in the National and Homeland Security Division at Idaho National Laboratory was tasked with providing tools to derive insight from the substantial amount of data currently available - and continuously being produced - associated with the critical infrastructure of the US. This effort is in support of the Department of Homeland Security, whose mission includes the protection of this infrastructure and the enhancement of its resilience to hazards, both natural and human. We present geovisual-analytics-based approaches for analysis of vulnerabilities and resilience of critical infrastructure, designed so that decision makers, analysts, and infrastructure owners and managers can manage risk, prepare for hazards, and direct resources before and after an incident that might result in an interruption in service. Our designs are based on iterative discussions with DHS leadership and analysts, who in turn will use these tools to explore and communicate data in partnership with utility providers, law enforcement, and emergency response and recovery organizations, among others. In most cases these partners desire summaries of large amounts of data, but increasingly, our users seek the additional capability of focusing on, for example, a specific infrastructure sector, a particular geographic region, or time period, or of examining data in a variety of generalization or aggregation levels. These needs align well with tenets of in-formation-visualization design; in this paper, selected applications among those that we have designed are described and positioned within geovisualization, geovisual analytical, and information visualization frameworks.

Whole-brain functional connectivity during acquisition of novel grammar: Distinct functional networks depend on language learning abilities.

PubMed

Kepinska, Olga; de Rover, Mischa; Caspers, Johanneke; Schiller, Niels O

2017-03-01

In an effort to advance the understanding of brain function and organisation accompanying second language learning, we investigate the neural substrates of novel grammar learning in a group of healthy adults, consisting of participants with high and average language analytical abilities (LAA). By means of an Independent Components Analysis, a data-driven approach to functional connectivity of the brain, the fMRI data collected during a grammar-learning task were decomposed into maps representing separate cognitive processes. These included the default mode, task-positive, working memory, visual, cerebellar and emotional networks. We further tested for differences within the components, representing individual differences between the High and Average LAA learners. We found high analytical abilities to be coupled with stronger contributions to the task-positive network from areas adjacent to bilateral Broca's region, stronger connectivity within the working memory network and within the emotional network. Average LAA participants displayed stronger engagement within the task-positive network from areas adjacent to the right-hemisphere homologue of Broca's region and typical to lower level processing (visual word recognition), and increased connectivity within the default mode network. The significance of each of the identified networks for the grammar learning process is presented next to a discussion on the established markers of inter-individual learners' differences. We conclude that in terms of functional connectivity, the engagement of brain's networks during grammar acquisition is coupled with one's language learning abilities. Copyright © 2016 Elsevier B.V. All rights reserved.

Identification of microplastics by FTIR and Raman microscopy: a novel silicon filter substrate opens the important spectral range below 1300 cm(-1) for FTIR transmission measurements.

PubMed

Käppler, Andrea; Windrich, Frank; Löder, Martin G J; Malanin, Mikhail; Fischer, Dieter; Labrenz, Matthias; Eichhorn, Klaus-Jochen; Voit, Brigitte

2015-09-01

The presence of microplastics in aquatic ecosystems is a topical problem and leads to the need of appropriate and reliable analytical methods to distinctly identify and to quantify these particles in environmental samples. As an example transmission, Fourier transform infrared (FTIR) imaging can be used to analyze samples directly on filters without any visual presorting, when the environmental sample was afore extracted, purified, and filtered. However, this analytical approach is strongly restricted by the limited IR transparency of conventional filter materials. Within this study, we describe a novel silicon (Si) filter substrate produced by photolithographic microstructuring, which guarantees sufficient transparency for the broad mid-infrared region of 4000-600 cm(-1). This filter type features holes with a diameter of 10 μm and exhibits adequate mechanical stability. Furthermore, it will be shown that our Si filter substrate allows a distinct identification of the most common microplastics, polyethylene (PE), and polypropylene (PP), in the characteristic fingerprint region (1400-600 cm(-1)). Moreover, using the Si filter substrate, a differentiation of microparticles of polyesters having quite similar chemical structure, like polyethylene terephthalate (PET) and polybutylene terephthalate (PBT), is now possible, which facilitates a visualization of their distribution within a microplastic sample by FTIR imaging. Finally, this Si filter can also be used as substrate for Raman microscopy-a second complementary spectroscopic technique-to identify microplastic samples.

Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons.

PubMed

Zerlaut, Yann; Chemla, Sandrine; Chavane, Frederic; Destexhe, Alain

2018-02-01

Voltage-sensitive dye imaging (VSDi) has revealed fundamental properties of neocortical processing at macroscopic scales. Since for each pixel VSDi signals report the average membrane potential over hundreds of neurons, it seems natural to use a mean-field formalism to model such signals. Here, we present a mean-field model of networks of Adaptive Exponential (AdEx) integrate-and-fire neurons, with conductance-based synaptic interactions. We study a network of regular-spiking (RS) excitatory neurons and fast-spiking (FS) inhibitory neurons. We use a Master Equation formalism, together with a semi-analytic approach to the transfer function of AdEx neurons to describe the average dynamics of the coupled populations. We compare the predictions of this mean-field model to simulated networks of RS-FS cells, first at the level of the spontaneous activity of the network, which is well predicted by the analytical description. Second, we investigate the response of the network to time-varying external input, and show that the mean-field model predicts the response time course of the population. Finally, to model VSDi signals, we consider a one-dimensional ring model made of interconnected RS-FS mean-field units. We found that this model can reproduce the spatio-temporal patterns seen in VSDi of awake monkey visual cortex as a response to local and transient visual stimuli. Conversely, we show that the model allows one to infer physiological parameters from the experimentally-recorded spatio-temporal patterns.

Visual Analytics Tools for Sustainable Lifecycle Design: Current Status, Challenges, and Future Opportunities.

PubMed

Ramanujan, Devarajan; Bernstein, William Z; Chandrasegaran, Senthil K; Ramani, Karthik

2017-01-01

The rapid rise in technologies for data collection has created an unmatched opportunity to advance the use of data-rich tools for lifecycle decision-making. However, the usefulness of these technologies is limited by the ability to translate lifecycle data into actionable insights for human decision-makers. This is especially true in the case of sustainable lifecycle design (SLD), as the assessment of environmental impacts, and the feasibility of making corresponding design changes, often relies on human expertise and intuition. Supporting human sense-making in SLD requires the use of both data-driven and user-driven methods while exploring lifecycle data. A promising approach for combining the two is through the use of visual analytics (VA) tools. Such tools can leverage the ability of computer-based tools to gather, process, and summarize data along with the ability of human-experts to guide analyses through domain knowledge or data-driven insight. In this paper, we review previous research that has created VA tools in SLD. We also highlight existing challenges and future opportunities for such tools in different lifecycle stages-design, manufacturing, distribution & supply chain, use-phase, end-of-life, as well as life cycle assessment. Our review shows that while the number of VA tools in SLD is relatively small, researchers are increasingly focusing on the subject matter. Our review also suggests that VA tools can address existing challenges in SLD and that significant future opportunities exist.

Business intelligence from social media: a study from the VAST Box Office Challenge.

PubMed

Lu, Yafeng; Wang, Feng; Maciejewski, Ross

2014-01-01

With over 16 million tweets per hour, 600 new blog posts per minute, and 400 million active users on Facebook, businesses have begun searching for ways to turn real-time consumer-based posts into actionable intelligence. The goal is to extract information from this noisy, unstructured data and use it for trend analysis and prediction. Current practices support the idea that visual analytics (VA) can help enable the effective analysis of such data. However, empirical evidence demonstrating the effectiveness of a VA solution is still lacking. A proposed VA toolkit extracts data from Bitly and Twitter to predict movie revenue and ratings. Results from the 2013 VAST Box Office Challenge demonstrate the benefit of an interactive environment for predictive analysis, compared to a purely statistical modeling approach. The VA approach used by the toolkit is generalizable to other domains involving social media data, such as sales forecasting and advertisement analysis.

Concept mapping and network analysis: an analytic approach to measure ties among constructs.

PubMed

Goldman, Alyssa W; Kane, Mary

2014-12-01

Group concept mapping is a mixed-methods approach that helps a group visually represent its ideas on a topic of interest through a series of related maps. The maps and additional graphics are useful for planning, evaluation and theory development. Group concept maps are typically described, interpreted and utilized through points, clusters and distances, and the implications of these features in understanding how constructs relate to one another. This paper focuses on the application of network analysis to group concept mapping to quantify the strength and directionality of relationships among clusters. The authors outline the steps of this analysis, and illustrate its practical use through an organizational strategic planning example. Additional benefits of this analysis to evaluation projects are also discussed, supporting the overall utility of this supplemental technique to the standard concept mapping methodology. Copyright © 2014 Elsevier Ltd. All rights reserved.

What's Going on in This Picture? Visual Thinking Strategies and Adult Learning

ERIC Educational Resources Information Center

Landorf, Hilary

2006-01-01

The Visual Thinking Strategies (VTS) curriculum and teaching method uses art to help students think critically, listen attentively, communicate, and collaborate. VTS has been proven to enhance reading, writing, comprehension, and creative and analytical skills among students of all ages. The origins and procedures of the VTS curriculum are…

Interpreting Visual Texts: Use of an Analytic Rubric to Evaluate Student Responses to Writing Prompts

ERIC Educational Resources Information Center

Sundeen, Todd H.; O'Neil, Kathleen; Fanselow, Stephanie A.

2017-01-01

Younger students' visual texts are statements and stories conveyed through drawings or other artwork and often convey meaning beyond the child's capability to communicate with written expression. Although opportunities for expression through drawing are routinely offered to children in the initial and middle stages of early childhood literacy…

Communicating Science Concepts through Art: 21st-Century Skills in Practice

ERIC Educational Resources Information Center

Buczynski, Sandy; Ireland, Kathleen; Reed, Sherri; Lacanienta, Evelyn

2012-01-01

There is a dynamic synergy between the visual arts and the natural sciences. For example, science relies heavily on individuals with visual-art skills to render detailed illustrations, depicting everything from atoms to zebras. Likewise, artists apply analytic, linear, and logical thinking to compose and scale their work of art. These parallel…

Domain Coloring and the Argument Principle

ERIC Educational Resources Information Center

Farris, Frank A.

2017-01-01

The "domain-coloring algorithm" allows us to visualize complex-valued functions on the plane in a single image--an alternative to before-and-after mapping diagrams. It helps us see when a function is analytic and aids in understanding contour integrals. The culmination of this article is a visual discovery and subsequent proof of the…

Characterizing Provenance in Visualization and Data Analysis: An Organizational Framework of Provenance Types and Purposes.

PubMed

Ragan, Eric D; Endert, Alex; Sanyal, Jibonananda; Chen, Jian

2016-01-01

While the primary goal of visual analytics research is to improve the quality of insights and findings, a substantial amount of research in provenance has focused on the history of changes and advances throughout the analysis process. The term, provenance, has been used in a variety of ways to describe different types of records and histories related to visualization. The existing body of provenance research has grown to a point where the consolidation of design knowledge requires cross-referencing a variety of projects and studies spanning multiple domain areas. We present an organizational framework of the different types of provenance information and purposes for why they are desired in the field of visual analytics. Our organization is intended to serve as a framework to help researchers specify types of provenance and coordinate design knowledge across projects. We also discuss the relationships between these factors and the methods used to capture provenance information. In addition, our organization can be used to guide the selection of evaluation methodology and the comparison of study outcomes in provenance research.

Characterizing Provenance in Visualization and Data Analysis: An Organizational Framework of Provenance Types and Purposes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ragan, Eric; Alex, Endert; Sanyal, Jibonananda

While the primary goal of visual analytics research is to improve the quality of insights and findings, a substantial amount of research in provenance has focused on the history of changes and advances throughout the analysis process. The term, provenance, has been used in a variety of ways to describe different types of records and histories related to visualization. The existing body of provenance research has grown to a point where the consolidation of design knowledge requires cross-referencing a variety of projects and studies spanning multiple domain areas. We present an organizational framework of the different types of provenance informationmore » and purposes for why they are desired in the field of visual analytics. Our organization is intended to serve as a framework to help researchers specify types of provenance and coordinate design knowledge across projects. We also discuss the relationships between these factors and the methods used to capture provenance information. In addition, our organization can be used to guide the selection of evaluation methodology and the comparison of study outcomes in provenance research« less

Characterizing Provenance in Visualization and Data Analysis: An Organizational Framework of Provenance Types and Purposes

DOE PAGES

Ragan, Eric; Alex, Endert; Sanyal, Jibonananda; ...

2016-01-01

While the primary goal of visual analytics research is to improve the quality of insights and findings, a substantial amount of research in provenance has focused on the history of changes and advances throughout the analysis process. The term, provenance, has been used in a variety of ways to describe different types of records and histories related to visualization. The existing body of provenance research has grown to a point where the consolidation of design knowledge requires cross-referencing a variety of projects and studies spanning multiple domain areas. We present an organizational framework of the different types of provenance informationmore » and purposes for why they are desired in the field of visual analytics. Our organization is intended to serve as a framework to help researchers specify types of provenance and coordinate design knowledge across projects. We also discuss the relationships between these factors and the methods used to capture provenance information. In addition, our organization can be used to guide the selection of evaluation methodology and the comparison of study outcomes in provenance research« less

Visual analytics of inherently noisy crowdsourced data on ultra high resolution displays

NASA Astrophysics Data System (ADS)

Huynh, Andrew; Ponto, Kevin; Lin, Albert Yu-Min; Kuester, Falko

The increasing prevalence of distributed human microtasking, crowdsourcing, has followed the exponential increase in data collection capabilities. The large scale and distributed nature of these microtasks produce overwhelming amounts of information that is inherently noisy due to the nature of human input. Furthermore, these inputs create a constantly changing dataset with additional information added on a daily basis. Methods to quickly visualize, filter, and understand this information over temporal and geospatial constraints is key to the success of crowdsourcing. This paper present novel methods to visually analyze geospatial data collected through crowdsourcing on top of remote sensing satellite imagery. An ultra high resolution tiled display system is used to explore the relationship between human and satellite remote sensing data at scale. A case study is provided that evaluates the presented technique in the context of an archaeological field expedition. A team in the field communicated in real-time with and was guided by researchers in the remote visual analytics laboratory, swiftly sifting through incoming crowdsourced data to identify target locations that were identified as viable archaeological sites.

World Spatiotemporal Analytics and Mapping Project (WSTAMP): Discovering, Exploring, and Mapping Spatiotemporal Patterns across the World s Largest Open Source Geographic Data Sets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stewart, Robert N; Piburn, Jesse O; Sorokine, Alexandre

The application of spatiotemporal (ST) analytics to integrated data from major sources such as the World Bank, United Nations, and dozens of others holds tremendous potential for shedding new light on the evolution of cultural, health, economic, and geopolitical landscapes on a global level. Realizing this potential first requires an ST data model that addresses challenges in properly merging data from multiple authors, with evolving ontological perspectives, semantical differences, and changing attributes, as well as content that is textual, numeric, categorical, and hierarchical. Equally challenging is the development of analytical and visualization approaches that provide a serious exploration of thismore » integrated data while remaining accessible to practitioners with varied backgrounds. The WSTAMP project at Oak Ridge National Laboratory has yielded two major results in addressing these challenges: 1) development of the WSTAMP database, a significant advance in ST data modeling that integrates 10,000+ attributes covering over 200 nation states spanning over 50 years from over 30 major sources and 2) a novel online ST exploratory and analysis tool providing an array of modern statistical and visualization techniques for analyzing these data temporally, spatially, and spatiotemporally under a standard analytic workflow. We discuss the status of this work and report on major findings. Acknowledgment Prepared by Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, Tennessee 37831-6285, managed by UT-Battelle, LLC for the U. S. Department of Energy under contract no. DEAC05-00OR22725. Copyright This manuscript has been authored by employees of UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy. Accordingly, the United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.« less

Finding Waldo: Learning about Users from their Interactions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, Eli T.; Ottley, Alvitta; Zhao, Helen

Visual analytics is inherently a collaboration between human and computer. However, in current visual analytics systems, the computer has limited means of knowing about its users and their analysis processes. While existing research has shown that a user’s interactions with a system reflect a large amount of the user’s reasoning process, there has been limited advancement in developing automated, real-time techniques that mine interactions to learn about the user. In this paper, we demonstrate that we can accurately predict a user’s task performance and infer some user personality traits by using machine learning techniques to analyze interaction data. Specifically, wemore » conduct an experiment in which participants perform a visual search task and we apply well-known machine learning algorithms to three encodings of the users interaction data. We achieve, depending on algorithm and encoding, between 62% and 96% accuracy at predicting whether each user will be fast or slow at completing the task. Beyond predicting performance, we demonstrate that using the same techniques, we can infer aspects of the user’s personality factors, including locus of control, extraversion, and neuroticism. Further analyses show that strong results can be attained with limited observation time, in some cases, 82% of the final accuracy is gained after a quarter of the average task completion time. Overall, our findings show that interactions can provide information to the computer about its human collaborator, and establish a foundation for realizing mixed- initiative visual analytics systems.« less

VisOHC: Designing Visual Analytics for Online Health Communities

PubMed Central

Kwon, Bum Chul; Kim, Sung-Hee; Lee, Sukwon; Choo, Jaegul; Huh, Jina; Yi, Ji Soo

2015-01-01

Through online health communities (OHCs), patients and caregivers exchange their illness experiences and strategies for overcoming the illness, and provide emotional support. To facilitate healthy and lively conversations in these communities, their members should be continuously monitored and nurtured by OHC administrators. The main challenge of OHC administrators' tasks lies in understanding the diverse dimensions of conversation threads that lead to productive discussions in their communities. In this paper, we present a design study in which three domain expert groups participated, an OHC researcher and two OHC administrators of online health communities, which was conducted to find with a visual analytic solution. Through our design study, we characterized the domain goals of OHC administrators and derived tasks to achieve these goals. As a result of this study, we propose a system called VisOHC, which visualizes individual OHC conversation threads as collapsed boxes–a visual metaphor of conversation threads. In addition, we augmented the posters' reply authorship network with marks and/or beams to show conversation dynamics within threads. We also developed unique measures tailored to the characteristics of OHCs, which can be encoded for thread visualizations at the users' requests. Our observation of the two administrators while using VisOHC showed that it supports their tasks and reveals interesting insights into online health communities. Finally, we share our methodological lessons on probing visual designs together with domain experts by allowing them to freely encode measurements into visual variables. PMID:26529688

VisOHC: Designing Visual Analytics for Online Health Communities.

PubMed

Kwon, Bum Chul; Kim, Sung-Hee; Lee, Sukwon; Choo, Jaegul; Huh, Jina; Yi, Ji Soo

2016-01-01

Through online health communities (OHCs), patients and caregivers exchange their illness experiences and strategies for overcoming the illness, and provide emotional support. To facilitate healthy and lively conversations in these communities, their members should be continuously monitored and nurtured by OHC administrators. The main challenge of OHC administrators' tasks lies in understanding the diverse dimensions of conversation threads that lead to productive discussions in their communities. In this paper, we present a design study in which three domain expert groups participated, an OHC researcher and two OHC administrators of online health communities, which was conducted to find with a visual analytic solution. Through our design study, we characterized the domain goals of OHC administrators and derived tasks to achieve these goals. As a result of this study, we propose a system called VisOHC, which visualizes individual OHC conversation threads as collapsed boxes-a visual metaphor of conversation threads. In addition, we augmented the posters' reply authorship network with marks and/or beams to show conversation dynamics within threads. We also developed unique measures tailored to the characteristics of OHCs, which can be encoded for thread visualizations at the users' requests. Our observation of the two administrators while using VisOHC showed that it supports their tasks and reveals interesting insights into online health communities. Finally, we share our methodological lessons on probing visual designs together with domain experts by allowing them to freely encode measurements into visual variables.

Estimating Vehicle Fuel Consumption and Emissions Using GPS Big Data

PubMed Central

Kan, Zihan; Zhang, Xia

2018-01-01

The energy consumption and emissions from vehicles adversely affect human health and urban sustainability. Analysis of GPS big data collected from vehicles can provide useful insights about the quantity and distribution of such energy consumption and emissions. Previous studies, which estimated fuel consumption/emissions from traffic based on GPS sampled data, have not sufficiently considered vehicle activities and may have led to erroneous estimations. By adopting the analytical construct of the space-time path in time geography, this study proposes methods that more accurately estimate and visualize vehicle energy consumption/emissions based on analysis of vehicles’ mobile activities (MA) and stationary activities (SA). First, we build space-time paths of individual vehicles, extract moving parameters, and identify MA and SA from each space-time path segment (STPS). Then we present an N-Dimensional framework for estimating and visualizing fuel consumption/emissions. For each STPS, fuel consumption, hot emissions, and cold start emissions are estimated based on activity type, i.e., MA, SA with engine-on and SA with engine-off. In the case study, fuel consumption and emissions of a single vehicle and a road network are estimated and visualized with GPS data. The estimation accuracy of the proposed approach is 88.6%. We also analyze the types of activities that produced fuel consumption on each road segment to explore the patterns and mechanisms of fuel consumption in the study area. The results not only show the effectiveness of the proposed approaches in estimating fuel consumption/emissions but also indicate their advantages for uncovering the relationships between fuel consumption and vehicles’ activities in road networks. PMID:29561813

Estimating Vehicle Fuel Consumption and Emissions Using GPS Big Data.

PubMed

Kan, Zihan; Tang, Luliang; Kwan, Mei-Po; Zhang, Xia

2018-03-21

The energy consumption and emissions from vehicles adversely affect human health and urban sustainability. Analysis of GPS big data collected from vehicles can provide useful insights about the quantity and distribution of such energy consumption and emissions. Previous studies, which estimated fuel consumption/emissions from traffic based on GPS sampled data, have not sufficiently considered vehicle activities and may have led to erroneous estimations. By adopting the analytical construct of the space-time path in time geography, this study proposes methods that more accurately estimate and visualize vehicle energy consumption/emissions based on analysis of vehicles' mobile activities ( MA ) and stationary activities ( SA ). First, we build space-time paths of individual vehicles, extract moving parameters, and identify MA and SA from each space-time path segment (STPS). Then we present an N-Dimensional framework for estimating and visualizing fuel consumption/emissions. For each STPS, fuel consumption, hot emissions, and cold start emissions are estimated based on activity type, i.e., MA , SA with engine-on and SA with engine-off. In the case study, fuel consumption and emissions of a single vehicle and a road network are estimated and visualized with GPS data. The estimation accuracy of the proposed approach is 88.6%. We also analyze the types of activities that produced fuel consumption on each road segment to explore the patterns and mechanisms of fuel consumption in the study area. The results not only show the effectiveness of the proposed approaches in estimating fuel consumption/emissions but also indicate their advantages for uncovering the relationships between fuel consumption and vehicles' activities in road networks.

HierarchicalTopics: visually exploring large text collections using topic hierarchies.

PubMed

Dou, Wenwen; Yu, Li; Wang, Xiaoyu; Ma, Zhiqiang; Ribarsky, William

2013-12-01

Analyzing large textual collections has become increasingly challenging given the size of the data available and the rate that more data is being generated. Topic-based text summarization methods coupled with interactive visualizations have presented promising approaches to address the challenge of analyzing large text corpora. As the text corpora and vocabulary grow larger, more topics need to be generated in order to capture the meaningful latent themes and nuances in the corpora. However, it is difficult for most of current topic-based visualizations to represent large number of topics without being cluttered or illegible. To facilitate the representation and navigation of a large number of topics, we propose a visual analytics system--HierarchicalTopic (HT). HT integrates a computational algorithm, Topic Rose Tree, with an interactive visual interface. The Topic Rose Tree constructs a topic hierarchy based on a list of topics. The interactive visual interface is designed to present the topic content as well as temporal evolution of topics in a hierarchical fashion. User interactions are provided for users to make changes to the topic hierarchy based on their mental model of the topic space. To qualitatively evaluate HT, we present a case study that showcases how HierarchicalTopics aid expert users in making sense of a large number of topics and discovering interesting patterns of topic groups. We have also conducted a user study to quantitatively evaluate the effect of hierarchical topic structure. The study results reveal that the HT leads to faster identification of large number of relevant topics. We have also solicited user feedback during the experiments and incorporated some suggestions into the current version of HierarchicalTopics.

Web-Based Geospatial Visualization of GPM Data with CesiumJS

NASA Technical Reports Server (NTRS)

Lammers, Matt

2018-01-01

Advancements in the capabilities of JavaScript frameworks and web browsing technology have made online visualization of large geospatial datasets such as those coming from precipitation satellites viable. These data benefit from being visualized on and above a three-dimensional surface. The open-source JavaScript framework CesiumJS (http://cesiumjs.org), developed by Analytical Graphics, Inc., leverages the WebGL protocol to do just that. This presentation will describe how CesiumJS has been used in three-dimensional visualization products developed as part of the NASA Precipitation Processing System (PPS) STORM data-order website. Existing methods of interacting with Global Precipitation Measurement (GPM) Mission data primarily focus on two-dimensional static images, whether displaying vertical slices or horizontal surface/height-level maps. These methods limit interactivity with the robust three-dimensional data coming from the GPM core satellite. Integrating the data with CesiumJS in a web-based user interface has allowed us to create the following products. We have linked with the data-order interface an on-the-fly visualization tool for any GPM/partner satellite orbit. A version of this tool also focuses on high-impact weather events. It enables viewing of combined radar and microwave-derived precipitation data on mobile devices and in a way that can be embedded into other websites. We also have used CesiumJS to visualize a method of integrating gridded precipitation data with modeled wind speeds that animates over time. Emphasis in the presentation will be placed on how a variety of technical methods were used to create these tools, and how the flexibility of the CesiumJS framework facilitates creative approaches to interact with the data.

Two Geo-Arithmetic Representations of n[superscript 3]: Sum of Hex Numbers

ERIC Educational Resources Information Center

Unal, Husan

2009-01-01

Studies have shown that students' understanding is typically analytic and not visual. Two possible reasons for this are when the analytic mode, instead of the graphic mode, is most frequently used in instruction or, when students or teachers hold the belief that mathematics consists simply of skillful manipulation of symbols and numbers. The…

Understanding Fluorescence Measurements through a Guided-Inquiry and Discovery Experiment in Advanced Analytical Laboratory

ERIC Educational Resources Information Center

Wilczek-Vera, Grazyna; Salin, Eric Dunbar

2011-01-01

An experiment on fluorescence spectroscopy suitable for an advanced analytical laboratory is presented. Its conceptual development used a combination of the expository and discovery styles. The "learn-as-you-go" and direct "hands-on" methodology applied ensures an active role for a student in the process of visualization and discovery of concepts.…

Effects of Learning Analytics Dashboard: Analyzing the Relations among Dashboard Utilization, Satisfaction, and Learning Achievement

ERIC Educational Resources Information Center

Kim, Jeonghyun; Jo, Il-Hyun; Park, Yeonjeong

2016-01-01

The learning analytics dashboard (LAD) is a newly developed learning support tool for virtual classrooms that is believed to allow students to review their online learning behavior patterns intuitively through the provision of visual information. The purpose of this study was to empirically validate the effects of LAD. An experimental study was…

Transport of a decay chain in homogenous porous media: analytical solutions.

PubMed

Bauer, P; Attinger, S; Kinzelbach, W

2001-06-01

With the aid of integral transforms, analytical solutions for the transport of a decay chain in homogenous porous media are derived. Unidirectional steady-state flow and radial steady-state flow in single and multiple porosity media are considered. At least in Laplace domain, all solutions can be written in closed analytical formulae. Partly, the solutions can also be inverted analytically. If not, analytical calculation of the steady-state concentration distributions, evaluation of temporal moments and numerical inversion are still possible. Formulae for several simple boundary conditions are given and visualized in this paper. The derived novel solutions are widely applicable and are very useful for the validation of numerical transport codes.

Damage states in laminated composite three-point bend specimens: An experimental-analytical correlation study

NASA Technical Reports Server (NTRS)

Starbuck, J. Michael; Guerdal, Zafer; Pindera, Marek-Jerzy; Poe, Clarence C.

1990-01-01

Damage states in laminated composites were studied by considering the model problem of a laminated beam subjected to three-point bending. A combination of experimental and theoretical research techniques was used to correlate the experimental results with the analytical stress distributions. The analytical solution procedure was based on the stress formulation approach of the mathematical theory of elasticity. The solution procedure is capable of calculating the ply-level stresses and beam displacements for any laminated beam of finite length using the generalized plane deformation or plane stress state assumption. Prior to conducting the experimental phase, the results from preliminary analyses were examined. Significant effects in the ply-level stress distributions were seen depending on the fiber orientation, aspect ratio, and whether or not a grouped or interspersed stacking sequence was used. The experimental investigation was conducted to determine the different damage modes in laminated three-point bend specimens. The test matrix consisted of three-point bend specimens of 0 deg unidirectional, cross-ply, and quasi-isotropic stacking sequences. The dependence of the damage initiation loads and ultimate failure loads were studied, and their relation to damage susceptibility and damage tolerance of the mean configuration was discussed. Damage modes were identified by visual inspection of the damaged specimens using an optical microscope. The four fundamental damage mechanisms identified were delaminations, matrix cracking, fiber breakage, and crushing. The correlation study between the experimental results and the analytical results were performed for the midspan deflection, indentation, damage modes, and damage susceptibility.

An automated baseline correction protocol for infrared spectra of atmospheric aerosols collected on polytetrafluoroethylene (Teflon) filters

NASA Astrophysics Data System (ADS)

Kuzmiakova, Adele; Dillner, Ann M.; Takahama, Satoshi

2016-06-01

A growing body of research on statistical applications for characterization of atmospheric aerosol Fourier transform infrared (FT-IR) samples collected on polytetrafluoroethylene (PTFE) filters (e.g., Russell et al., 2011; Ruthenburg et al., 2014) and a rising interest in analyzing FT-IR samples collected by air quality monitoring networks call for an automated PTFE baseline correction solution. The existing polynomial technique (Takahama et al., 2013) is not scalable to a project with a large number of aerosol samples because it contains many parameters and requires expert intervention. Therefore, the question of how to develop an automated method for baseline correcting hundreds to thousands of ambient aerosol spectra given the variability in both environmental mixture composition and PTFE baselines remains. This study approaches the question by detailing the statistical protocol, which allows for the precise definition of analyte and background subregions, applies nonparametric smoothing splines to reproduce sample-specific PTFE variations, and integrates performance metrics from atmospheric aerosol and blank samples alike in the smoothing parameter selection. Referencing 794 atmospheric aerosol samples from seven Interagency Monitoring of PROtected Visual Environment (IMPROVE) sites collected during 2011, we start by identifying key FT-IR signal characteristics, such as non-negative absorbance or analyte segment transformation, to capture sample-specific transitions between background and analyte. While referring to qualitative properties of PTFE background, the goal of smoothing splines interpolation is to learn the baseline structure in the background region to predict the baseline structure in the analyte region. We then validate the model by comparing smoothing splines baseline-corrected spectra with uncorrected and polynomial baseline (PB)-corrected equivalents via three statistical applications: (1) clustering analysis, (2) functional group quantification, and (3) thermal optical reflectance (TOR) organic carbon (OC) and elemental carbon (EC) predictions. The discrepancy rate for a four-cluster solution is 10 %. For all functional groups but carboxylic COH the discrepancy is ≤ 10 %. Performance metrics obtained from TOR OC and EC predictions (R2 ≥ 0.94 %, bias ≤ 0.01 µg m-3, and error ≤ 0.04 µg m-3) are on a par with those obtained from uncorrected and PB-corrected spectra. The proposed protocol leads to visually and analytically similar estimates as those generated by the polynomial method. More importantly, the automated solution allows us and future users to evaluate its analytical reproducibility while minimizing reducible user bias. We anticipate the protocol will enable FT-IR researchers and data analysts to quickly and reliably analyze a large amount of data and connect them to a variety of available statistical learning methods to be applied to analyte absorbances isolated in atmospheric aerosol samples.

Validity of Particle-Counting Method Using Laser-Light Scattering for Detecting Platelet Aggregation in Diabetic Patients

NASA Astrophysics Data System (ADS)

Nakadate, Hiromichi; Sekizuka, Eiichi; Minamitani, Haruyuki

We aimed to study the validity of a new analytical approach that reflected the phase from platelet activation to the formation of small platelet aggregates. We hoped that this new approach would enable us to use the particle-counting method with laser-light scattering to measure platelet aggregation in healthy controls and in diabetic patients without complications. We measured agonist-induced platelet aggregation for 10 min. Agonist was added to the platelet-rich plasma 1 min after measurement started. We compared the total scattered light intensity from small aggregates over a 10-min period (established analytical approach) and that over a 2-min period from 1 to 3 min after measurement started (new analytical approach). Consequently platelet aggregation in diabetics with HbA1c ≥ 6.5% was significantly greater than in healthy controls by both analytical approaches. However, platelet aggregation in diabetics with HbA1c < 6.5%, i.e. patients in the early stages of diabetes, was significantly greater than in healthy controls only by the new analytical approach, not by the established analytical approach. These results suggest that platelet aggregation as detected by the particle-counting method using laser-light scattering could be applied in clinical examinations by our new analytical approach.

Semi-quantitative visual detection of loop mediated isothermal amplification (LAMP)-generated DNA by distance-based measurement on a paper device.

PubMed

Hongwarittorrn, Irin; Chaichanawongsaroj, Nuntaree; Laiwattanapaisal, Wanida

2017-12-01

A distance-based paper analytical device (dPAD) for loop mediated isothermal amplification (LAMP) detection based on distance measurement was proposed. This approach relied on visual detection by the length of colour developed on the dPAD with reference to semi-quantitative determination of the initial amount of genomic DNA. In this communication, E. coli DNA was chosen as a template DNA for LAMP reaction. In accordance with the principle, the dPAD was immobilized by polyethylenimine (PEI), which is a strong cationic polymer, in the hydrophilic channel of the paper device. Hydroxynaphthol blue (HNB), a colourimetric indicator for monitoring the change of magnesium ion concentration in the LAMP reaction, was used to react with the immobilized PEI. The positive charges of PEI react with the negative charges of free HNB in the LAMP reaction, producing a blue colour deposit on the paper device. Consequently, the apparently visual distance appeared within 5min and length of distance correlated to the amount of DNA in the sample. The distance-based PAD for the visual detection of the LAMP reaction could quantify the initial concentration of genomic DNA as low as 4.14 × 10 3 copiesµL -1 . This distance-based visual semi-quantitative platform is suitable for choice of LAMP detection method, particular in resource-limited settings because of the advantages of low cost, simple fabrication and operation, disposability and portable detection of the dPAD device. Copyright © 2017 Elsevier B.V. All rights reserved.

MO-C-BRCD-03: The Role of Informatics in Medical Physics and Vice Versa.

PubMed

Andriole, K

2012-06-01

Like Medical Physics, Imaging Informatics encompasses concepts touching every aspect of the imaging chain from image creation, acquisition, management and archival, to image processing, analysis, display and interpretation. The two disciplines are in fact quite complementary, with similar goals to improve the quality of care provided to patients using an evidence-based approach, to assure safety in the clinical and research environments, to facilitate efficiency in the workplace, and to accelerate knowledge discovery. Use-cases describing several areas of informatics activity will be given to illustrate current limitations that would benefit from medical physicist participation, and conversely areas in which informaticists may contribute to the solution. Topics to be discussed include radiation dose monitoring, process management and quality control, display technologies, business analytics techniques, and quantitative imaging. Quantitative imaging is increasingly becoming an essential part of biomedicalresearch as well as being incorporated into clinical diagnostic activities. Referring clinicians are asking for more objective information to be gleaned from the imaging tests that they order so that they may make the best clinical management decisions for their patients. Medical Physicists may be called upon to identify existing issues as well as develop, validate and implement new approaches and technologies to help move the field further toward quantitative imaging methods for the future. Biomedical imaging informatics tools and techniques such as standards, integration, data mining, cloud computing and new systems architectures, ontologies and lexicons, data visualization and navigation tools, and business analytics applications can be used to overcome some of the existing limitations. 1. Describe what is meant by Medical Imaging Informatics and understand why the medical physicist should care. 2. Identify existing limitations in information technologies with respect to Medical Physics, and conversely see how Informatics may assist the medical physicist in filling some of the current gaps in their activities. 3. Understand general informatics concepts and areas of investigation including imaging and workflow standards, systems integration, computing architectures, ontologies, data mining and business analytics, data visualization and human-computer interface tools, and the importance of quantitative imaging for the future of Medical Physics and Imaging Informatics. 4. Become familiar with on-going efforts to address current challenges facing future research into and clinical implementation of quantitative imaging applications. © 2012 American Association of Physicists in Medicine.

Interactive entity resolution in relational data: a visual analytic tool and its evaluation.

PubMed

Kang, Hyunmo; Getoor, Lise; Shneiderman, Ben; Bilgic, Mustafa; Licamele, Louis

2008-01-01

Databases often contain uncertain and imprecise references to real-world entities. Entity resolution, the process of reconciling multiple references to underlying real-world entities, is an important data cleaning process required before accurate visualization or analysis of the data is possible. In many cases, in addition to noisy data describing entities, there is data describing the relationships among the entities. This relational data is important during the entity resolution process; it is useful both for the algorithms which determine likely database references to be resolved and for visual analytic tools which support the entity resolution process. In this paper, we introduce a novel user interface, D-Dupe, for interactive entity resolution in relational data. D-Dupe effectively combines relational entity resolution algorithms with a novel network visualization that enables users to make use of an entity's relational context for making resolution decisions. Since resolution decisions often are interdependent, D-Dupe facilitates understanding this complex process through animations which highlight combined inferences and a history mechanism which allows users to inspect chains of resolution decisions. An empirical study with 12 users confirmed the benefits of the relational context visualization on the performance of entity resolution tasks in relational data in terms of time as well as users' confidence and satisfaction.

IN13B-1660: Analytics and Visualization Pipelines for Big Data on the NASA Earth Exchange (NEX) and OpenNEX

NASA Technical Reports Server (NTRS)

Chaudhary, Aashish; Votava, Petr; Nemani, Ramakrishna R.; Michaelis, Andrew; Kotfila, Chris

2016-01-01

We are developing capabilities for an integrated petabyte-scale Earth science collaborative analysis and visualization environment. The ultimate goal is to deploy this environment within the NASA Earth Exchange (NEX) and OpenNEX in order to enhance existing science data production pipelines in both high-performance computing (HPC) and cloud environments. Bridging of HPC and cloud is a fairly new concept under active research and this system significantly enhances the ability of the scientific community to accelerate analysis and visualization of Earth science data from NASA missions, model outputs and other sources. We have developed a web-based system that seamlessly interfaces with both high-performance computing (HPC) and cloud environments, providing tools that enable science teams to develop and deploy large-scale analysis, visualization and QA pipelines of both the production process and the data products, and enable sharing results with the community. Our project is developed in several stages each addressing separate challenge - workflow integration, parallel execution in either cloud or HPC environments and big-data analytics or visualization. This work benefits a number of existing and upcoming projects supported by NEX, such as the Web Enabled Landsat Data (WELD), where we are developing a new QA pipeline for the 25PB system.

Analytics and Visualization Pipelines for Big ­Data on the NASA Earth Exchange (NEX) and OpenNEX

NASA Astrophysics Data System (ADS)

Chaudhary, A.; Votava, P.; Nemani, R. R.; Michaelis, A.; Kotfila, C.

2016-12-01

We are developing capabilities for an integrated petabyte-scale Earth science collaborative analysis and visualization environment. The ultimate goal is to deploy this environment within the NASA Earth Exchange (NEX) and OpenNEX in order to enhance existing science data production pipelines in both high-performance computing (HPC) and cloud environments. Bridging of HPC and cloud is a fairly new concept under active research and this system significantly enhances the ability of the scientific community to accelerate analysis and visualization of Earth science data from NASA missions, model outputs and other sources. We have developed a web-based system that seamlessly interfaces with both high-performance computing (HPC) and cloud environments, providing tools that enable science teams to develop and deploy large-scale analysis, visualization and QA pipelines of both the production process and the data products, and enable sharing results with the community. Our project is developed in several stages each addressing separate challenge - workflow integration, parallel execution in either cloud or HPC environments and big-data analytics or visualization. This work benefits a number of existing and upcoming projects supported by NEX, such as the Web Enabled Landsat Data (WELD), where we are developing a new QA pipeline for the 25PB system.

Differences in Visual Analysis and Sequence Memory of Skilled and Poor Readers.

ERIC Educational Resources Information Center

Gildemeister, Joan E.; Friedman, Philip

Reading achievement tests have been used to identify deficiencies in inner city, poor readers; however, they often do not provide information about encoding strategies which lead some children to academic success. Immediate memory and visual analytic differences which contribute to the success of skilled readers are isolated in this study using 20…

Data Visualization: An Exploratory Study into the Software Tools Used by Businesses

ERIC Educational Resources Information Center

Diamond, Michael; Mattia, Angela

2017-01-01

Data visualization is a key component to business and data analytics, allowing analysts in businesses to create tools such as dashboards for business executives. Various software packages allow businesses to create these tools in order to manipulate data for making informed business decisions. The focus is to examine what skills employers are…

Interactive Visualization of a Thin Disc around a Schwarzschild Black Hole

ERIC Educational Resources Information Center

Muller, Thomas; Frauendiener, Jorg

2012-01-01

In a first course in general relativity, the Schwarzschild spacetime is the most discussed analytic solution to Einstein's field equations. Unfortunately, there is rarely enough time to study the optical consequences of the bending of light for some advanced examples. In this paper, we present how the visual appearance of a thin disc around a…

Visual Images and Imagination in Pursuit of Mimesis and French Society.

ERIC Educational Resources Information Center

Santorini, George

1990-01-01

Focuses on activities that took place in courses on contemporary French society. In these courses, students and instructor attempted to develop a series of analytical tools from a systematic body of visual, oral, and textual materials in order to increase cultural understanding and speech styles of French-speaking communities or social groups.…

Examining the Relationship between Comparative and Self-Focused Academic Data Visualizations in At-Risk College Students' Academic Motivation

ERIC Educational Resources Information Center

Aguilar, Stephen J.

2018-01-01

This qualitative study focuses on capturing students' understanding two visualizations often utilized by learning analytics-based educational technologies: bar graphs, and line graphs. It is framed by Achievement Goal Theory--a prominent theory of students' academic motivation--and utilizes interviews (n = 60) to investigate how students at risk…

The Intuitive Style: Relationships with Local/Global and Verbal/Visual Styles, Gender, and Superstitious Reasoning

ERIC Educational Resources Information Center

Sadler-Smith, Eugene

2011-01-01

The study explored various facets of the intuitive style and its relevance to learning and education from a dual-processing perspective, namely how it relates to other style constructs (analytical; visual and verbal; local and global), gender, and superstitious reasoning and how these are likely to impact upon learning in educational and…

Data Visualization: An Exploratory Study into the Software Tools Used by Businesses

ERIC Educational Resources Information Center

Diamond, Michael; Mattia, Angela

2015-01-01

Data visualization is a key component to business and data analytics, allowing analysts in businesses to create tools such as dashboards for business executives. Various software packages allow businesses to create these tools in order to manipulate data for making informed business decisions. The focus is to examine what skills employers are…

An Intelligent Cooperative Visual Sensor Network for Urban Mobility

PubMed Central

Leone, Giuseppe Riccardo; Petracca, Matteo; Salvetti, Ovidio; Azzarà, Andrea

2017-01-01

Smart cities are demanding solutions for improved traffic efficiency, in order to guarantee optimal access to mobility resources available in urban areas. Intelligent video analytics deployed directly on board embedded sensors offers great opportunities to gather highly informative data about traffic and transport, allowing reconstruction of a real-time neat picture of urban mobility patterns. In this paper, we present a visual sensor network in which each node embeds computer vision logics for analyzing in real time urban traffic. The nodes in the network share their perceptions and build a global and comprehensive interpretation of the analyzed scenes in a cooperative and adaptive fashion. This is possible thanks to an especially designed Internet of Things (IoT) compliant middleware which encompasses in-network event composition as well as full support of Machine-2-Machine (M2M) communication mechanism. The potential of the proposed cooperative visual sensor network is shown with two sample applications in urban mobility connected to the estimation of vehicular flows and parking management. Besides providing detailed results of each key component of the proposed solution, the validity of the approach is demonstrated by extensive field tests that proved the suitability of the system in providing a scalable, adaptable and extensible data collection layer for managing and understanding mobility in smart cities. PMID:29125535

MinOmics, an Integrative and Immersive Tool for Multi-Omics Analysis.

PubMed

Maes, Alexandre; Martinez, Xavier; Druart, Karen; Laurent, Benoist; Guégan, Sean; Marchand, Christophe H; Lemaire, Stéphane D; Baaden, Marc

2018-06-21

Proteomic and transcriptomic technologies resulted in massive biological datasets, their interpretation requiring sophisticated computational strategies. Efficient and intuitive real-time analysis remains challenging. We use proteomic data on 1417 proteins of the green microalga Chlamydomonas reinhardtii to investigate physicochemical parameters governing selectivity of three cysteine-based redox post translational modifications (PTM): glutathionylation (SSG), nitrosylation (SNO) and disulphide bonds (SS) reduced by thioredoxins. We aim to understand underlying molecular mechanisms and structural determinants through integration of redox proteome data from gene- to structural level. Our interactive visual analytics approach on an 8.3 m2 display wall of 25 MPixel resolution features stereoscopic three dimensions (3D) representation performed by UnityMol WebGL. Virtual reality headsets complement the range of usage configurations for fully immersive tasks. Our experiments confirm that fast access to a rich cross-linked database is necessary for immersive analysis of structural data. We emphasize the possibility to display complex data structures and relationships in 3D, intrinsic to molecular structure visualization, but less common for omics-network analysis. Our setup is powered by MinOmics, an integrated analysis pipeline and visualization framework dedicated to multi-omics analysis. MinOmics integrates data from various sources into a materialized physical repository. We evaluate its performance, a design criterion for the framework.

An Intelligent Cooperative Visual Sensor Network for Urban Mobility.

PubMed

Leone, Giuseppe Riccardo; Moroni, Davide; Pieri, Gabriele; Petracca, Matteo; Salvetti, Ovidio; Azzarà, Andrea; Marino, Francesco

2017-11-10

Smart cities are demanding solutions for improved traffic efficiency, in order to guarantee optimal access to mobility resources available in urban areas. Intelligent video analytics deployed directly on board embedded sensors offers great opportunities to gather highly informative data about traffic and transport, allowing reconstruction of a real-time neat picture of urban mobility patterns. In this paper, we present a visual sensor network in which each node embeds computer vision logics for analyzing in real time urban traffic. The nodes in the network share their perceptions and build a global and comprehensive interpretation of the analyzed scenes in a cooperative and adaptive fashion. This is possible thanks to an especially designed Internet of Things (IoT) compliant middleware which encompasses in-network event composition as well as full support of Machine-2-Machine (M2M) communication mechanism. The potential of the proposed cooperative visual sensor network is shown with two sample applications in urban mobility connected to the estimation of vehicular flows and parking management. Besides providing detailed results of each key component of the proposed solution, the validity of the approach is demonstrated by extensive field tests that proved the suitability of the system in providing a scalable, adaptable and extensible data collection layer for managing and understanding mobility in smart cities.

Analyzing a 35-Year Hourly Data Record: Why So Difficult?

NASA Technical Reports Server (NTRS)

Lynnes, Chris

2014-01-01

At the Goddard Distributed Active Archive Center, we have recently added a 35-Year record of output data from the North American Land Assimilation System (NLDAS) to the Giovanni web-based analysis and visualization tool. Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure) offers a variety of data summarization and visualization to users that operate at the data center, obviating the need for users to download and read the data themselves for exploratory data analysis. However, the NLDAS data has proven surprisingly resistant to application of the summarization algorithms. Algorithms that were perfectly happy analyzing 15 years of daily satellite data encountered limitations both at the algorithm and system level for 35 years of hourly data. Failures arose, sometimes unexpectedly, from command line overflows, memory overflows, internal buffer overflows, and time-outs, among others. These serve as an early warning sign for the problems likely to be encountered by the general user community as they try to scale up to Big Data analytics. Indeed, it is likely that more users will seek to perform remote web-based analysis precisely to avoid the issues, or the need to reprogram around them. We will discuss approaches to mitigating the limitations and the implications for data systems serving the user communities that try to scale up their current techniques to analyze Big Data.

Special Issue of Selected Papers from Visualization and Data Analysis 2011

NASA Technical Reports Server (NTRS)

Kao, David L.; Wong, Pak Chung

2012-01-01

This special issue features the best papers that were selected from the 18th SPIE Conference on Visualization and Data Analysis (VDA 2011). This annual conference is a major international forum for researchers and practitioners interested in data visualization and analytics research, development, and applications. VDA 2011 received 42 high-quality submissions from around the world. Twenty-four papers were selected for full conference papers. The top five papers have been expanded and reviewed for this special issue.

moocRP: Enabling Open Learning Analytics with an Open Source Platform for Data Distribution, Analysis, and Visualization

ERIC Educational Resources Information Center

Pardos, Zachary A.; Whyte, Anthony; Kao, Kevin

2016-01-01

In this paper, we address issues of transparency, modularity, and privacy with the introduction of an open source, web-based data repository and analysis tool tailored to the Massive Open Online Course community. The tool integrates data request/authorization and distribution workflow features as well as provides a simple analytics module upload…

Augmented reality enabling intelligence exploitation at the edge

NASA Astrophysics Data System (ADS)

Kase, Sue E.; Roy, Heather; Bowman, Elizabeth K.; Patton, Debra

2015-05-01

Today's Warfighters need to make quick decisions while interacting in densely populated environments comprised of friendly, hostile, and neutral host nation locals. However, there is a gap in the real-time processing of big data streams for edge intelligence. We introduce a big data processing pipeline called ARTEA that ingests, monitors, and performs a variety of analytics including noise reduction, pattern identification, and trend and event detection in the context of an area of operations (AOR). Results of the analytics are presented to the Soldier via an augmented reality (AR) device Google Glass (Glass). Non-intrusive AR devices such as Glass can visually communicate contextually relevant alerts to the Soldier based on the current mission objectives, time, location, and observed or sensed activities. This real-time processing and AR presentation approach to knowledge discovery flattens the intelligence hierarchy enabling the edge Soldier to act as a vital and active participant in the analysis process. We report preliminary observations testing ARTEA and Glass in a document exploitation and person of interest scenario simulating edge Soldier participation in the intelligence process in disconnected deployment conditions.

Gut Feelings as a Third Track in General Practitioners’ Diagnostic Reasoning

PubMed Central

Van de Wiel, Margje; Van Royen, Paul; Van Bokhoven, Marloes; Van der Weijden, Trudy; Dinant, Geert Jan

2010-01-01

Background General practitioners (GPs) are often faced with complicated, vague problems in situations of uncertainty that they have to solve at short notice. In such situations, gut feelings seem to play a substantial role in their diagnostic process. Qualitative research distinguished a sense of alarm and a sense of reassurance. However, not every GP trusted their gut feelings, since a scientific explanation is lacking. Objective This paper explains how gut feelings arise and function in GPs’ diagnostic reasoning. Approach The paper reviews literature from medical, psychological and neuroscientific perspectives. Conclusions Gut feelings in general practice are based on the interaction between patient information and a GP’s knowledge and experience. This is visualized in a knowledge-based model of GPs’ diagnostic reasoning emphasizing that this complex task combines analytical and non-analytical cognitive processes. The model integrates the two well-known diagnostic reasoning tracks of medical decision-making and medical problem-solving, and adds gut feelings as a third track. Analytical and non-analytical diagnostic reasoning interacts continuously, and GPs use elements of all three tracks, depending on the task and the situation. In this dual process theory, gut feelings emerge as a consequence of non-analytical processing of the available information and knowledge, either reassuring GPs or alerting them that something is wrong and action is required. The role of affect as a heuristic within the physician’s knowledge network explains how gut feelings may help GPs to navigate in a mostly efficient way in the often complex and uncertain diagnostic situations of general practice. Emotion research and neuroscientific data support the unmistakable role of affect in the process of making decisions and explain the bodily sensation of gut feelings.The implications for health care practice and medical education are discussed. PMID:20967509

Scalable Predictive Analysis in Critically Ill Patients Using a Visual Open Data Analysis Platform

PubMed Central

Poucke, Sven Van; Zhang, Zhongheng; Schmitz, Martin; Vukicevic, Milan; Laenen, Margot Vander; Celi, Leo Anthony; Deyne, Cathy De

2016-01-01

With the accumulation of large amounts of health related data, predictive analytics could stimulate the transformation of reactive medicine towards Predictive, Preventive and Personalized (PPPM) Medicine, ultimately affecting both cost and quality of care. However, high-dimensionality and high-complexity of the data involved, prevents data-driven methods from easy translation into clinically relevant models. Additionally, the application of cutting edge predictive methods and data manipulation require substantial programming skills, limiting its direct exploitation by medical domain experts. This leaves a gap between potential and actual data usage. In this study, the authors address this problem by focusing on open, visual environments, suited to be applied by the medical community. Moreover, we review code free applications of big data technologies. As a showcase, a framework was developed for the meaningful use of data from critical care patients by integrating the MIMIC-II database in a data mining environment (RapidMiner) supporting scalable predictive analytics using visual tools (RapidMiner’s Radoop extension). Guided by the CRoss-Industry Standard Process for Data Mining (CRISP-DM), the ETL process (Extract, Transform, Load) was initiated by retrieving data from the MIMIC-II tables of interest. As use case, correlation of platelet count and ICU survival was quantitatively assessed. Using visual tools for ETL on Hadoop and predictive modeling in RapidMiner, we developed robust processes for automatic building, parameter optimization and evaluation of various predictive models, under different feature selection schemes. Because these processes can be easily adopted in other projects, this environment is attractive for scalable predictive analytics in health research. PMID:26731286

Scalable Predictive Analysis in Critically Ill Patients Using a Visual Open Data Analysis Platform.

PubMed

Van Poucke, Sven; Zhang, Zhongheng; Schmitz, Martin; Vukicevic, Milan; Laenen, Margot Vander; Celi, Leo Anthony; De Deyne, Cathy

2016-01-01

With the accumulation of large amounts of health related data, predictive analytics could stimulate the transformation of reactive medicine towards Predictive, Preventive and Personalized (PPPM) Medicine, ultimately affecting both cost and quality of care. However, high-dimensionality and high-complexity of the data involved, prevents data-driven methods from easy translation into clinically relevant models. Additionally, the application of cutting edge predictive methods and data manipulation require substantial programming skills, limiting its direct exploitation by medical domain experts. This leaves a gap between potential and actual data usage. In this study, the authors address this problem by focusing on open, visual environments, suited to be applied by the medical community. Moreover, we review code free applications of big data technologies. As a showcase, a framework was developed for the meaningful use of data from critical care patients by integrating the MIMIC-II database in a data mining environment (RapidMiner) supporting scalable predictive analytics using visual tools (RapidMiner's Radoop extension). Guided by the CRoss-Industry Standard Process for Data Mining (CRISP-DM), the ETL process (Extract, Transform, Load) was initiated by retrieving data from the MIMIC-II tables of interest. As use case, correlation of platelet count and ICU survival was quantitatively assessed. Using visual tools for ETL on Hadoop and predictive modeling in RapidMiner, we developed robust processes for automatic building, parameter optimization and evaluation of various predictive models, under different feature selection schemes. Because these processes can be easily adopted in other projects, this environment is attractive for scalable predictive analytics in health research.

A framework for inference about carnivore density from unstructured spatial sampling of scat using detector dogs

USGS Publications Warehouse

Thompson, Craig M.; Royle, J. Andrew; Garner, James D.

2012-01-01

Wildlife management often hinges upon an accurate assessment of population density. Although undeniably useful, many of the traditional approaches to density estimation such as visual counts, livetrapping, or mark–recapture suffer from a suite of methodological and analytical weaknesses. Rare, secretive, or highly mobile species exacerbate these problems through the reality of small sample sizes and movement on and off study sites. In response to these difficulties, there is growing interest in the use of non-invasive survey techniques, which provide the opportunity to collect larger samples with minimal increases in effort, as well as the application of analytical frameworks that are not reliant on large sample size arguments. One promising survey technique, the use of scat detecting dogs, offers a greatly enhanced probability of detection while at the same time generating new difficulties with respect to non-standard survey routes, variable search intensity, and the lack of a fixed survey point for characterizing non-detection. In order to account for these issues, we modified an existing spatially explicit, capture–recapture model for camera trap data to account for variable search intensity and the lack of fixed, georeferenced trap locations. We applied this modified model to a fisher (Martes pennanti) dataset from the Sierra National Forest, California, and compared the results (12.3 fishers/100 km2) to more traditional density estimates. We then evaluated model performance using simulations at 3 levels of population density. Simulation results indicated that estimates based on the posterior mode were relatively unbiased. We believe that this approach provides a flexible analytical framework for reconciling the inconsistencies between detector dog survey data and density estimation procedures.

Multimodal Preception and Multicriterion Control of Nested Systems. 3; A Functional Visual Assessment Test for Human Health Maintenance and Countermeasure Evaluation

NASA Technical Reports Server (NTRS)

Riccio, Gary E.; McDonald, P. Vernon; Bloomberg, Jacob

1999-01-01

Our theoretical and empirical research on the whole-body coordination during locomotion led to a Phase 1 SBIR grant from NASA JSC. The purpose of the SBIR grant was to design an innovative system for evaluating eye-head-trunk coordination during whole-body perturbations that are characteristic of locomotion. The approach we used to satisfy the Phase 1 objectives was based on a structured methodology for the development of human-systems technology. Accordingly the project was broken down into a number of tasks and subtasks. In sequence, the major tasks were: (1) identify needs for functional assessment of visual acuity under conditions involving whole-body perturbation within the NASA Space Medical Monitoring and Countermeasures (SMMaC) program and in other related markets; (2) analyze the needs into the causes and symptoms of impaired visual acuity under conditions involving whole-body perturbation; (3) translate the analyzed needs into technology requirements for the Functional Visual Assessment Test (FVAT); (4) identify candidate technology solutions and implementations of FVAT; and (5) prioritize and select technology solutions. The work conducted in these tasks is described in this final volume of the series on Multimodal Perception and Multicriterion Control of Nested Systems. While prior volumes (1 and 2) in the series focus on theoretical foundations and novel data-analytic techniques, this volume addresses technology that is necessary for minimally intrusive data collection and near-real-time data analysis and display.

Innovating Big Data Computing Geoprocessing for Analysis of Engineered-Natural Systems

NASA Astrophysics Data System (ADS)

Rose, K.; Baker, V.; Bauer, J. R.; Vasylkivska, V.

2016-12-01

Big data computing and analytical techniques offer opportunities to improve predictions about subsurface systems while quantifying and characterizing associated uncertainties from these analyses. Spatial analysis, big data and otherwise, of subsurface natural and engineered systems are based on variable resolution, discontinuous, and often point-driven data to represent continuous phenomena. We will present examples from two spatio-temporal methods that have been adapted for use with big datasets and big data geo-processing capabilities. The first approach uses regional earthquake data to evaluate spatio-temporal trends associated with natural and induced seismicity. The second algorithm, the Variable Grid Method (VGM), is a flexible approach that presents spatial trends and patterns, such as those resulting from interpolation methods, while simultaneously visualizing and quantifying uncertainty in the underlying spatial datasets. In this presentation we will show how we are utilizing Hadoop to store and perform spatial analyses to efficiently consume and utilize large geospatial data in these custom analytical algorithms through the development of custom Spark and MapReduce applications that incorporate ESRI Hadoop libraries. The team will present custom `Big Data' geospatial applications that run on the Hadoop cluster and integrate with ESRI ArcMap with the team's probabilistic VGM approach. The VGM-Hadoop tool has been specially built as a multi-step MapReduce application running on the Hadoop cluster for the purpose of data reduction. This reduction is accomplished by generating multi-resolution, non-overlapping, attributed topology that is then further processed using ESRI's geostatistical analyst to convey a probabilistic model of a chosen study region. Finally, we will share our approach for implementation of data reduction and topology generation via custom multi-step Hadoop applications, performance benchmarking comparisons, and Hadoop-centric opportunities for greater parallelization of geospatial operations.

Cultural differences in attention: Eye movement evidence from a comparative visual search task.

PubMed

Alotaibi, Albandri; Underwood, Geoffrey; Smith, Alastair D

2017-10-01

Individual differences in visual attention have been linked to thinking style: analytic thinking (common in individualistic cultures) is thought to promote attention to detail and focus on the most important part of a scene, whereas holistic thinking (common in collectivist cultures) promotes attention to the global structure of a scene and the relationship between its parts. However, this theory is primarily based on relatively simple judgement tasks. We compared groups from Great Britain (an individualist culture) and Saudi Arabia (a collectivist culture) on a more complex comparative visual search task, using simple natural scenes. A higher overall number of fixations for Saudi participants, along with longer search times, indicated less efficient search behaviour than British participants. Furthermore, intra-group comparisons of scan-path for Saudi participants revealed less similarity than within the British group. Together, these findings suggest that there is a positive relationship between an analytic cognitive style and controlled attention. Copyright © 2017 Elsevier Inc. All rights reserved.

Hawkeye and AMOS: visualizing and assessing the quality of genome assemblies

PubMed Central

Schatz, Michael C.; Phillippy, Adam M.; Sommer, Daniel D.; Delcher, Arthur L.; Puiu, Daniela; Narzisi, Giuseppe; Salzberg, Steven L.; Pop, Mihai

2013-01-01

Since its launch in 2004, the open-source AMOS project has released several innovative DNA sequence analysis applications including: Hawkeye, a visual analytics tool for inspecting the structure of genome assemblies; the Assembly Forensics and FRCurve pipelines for systematically evaluating the quality of a genome assembly; and AMOScmp, the first comparative genome assembler. These applications have been used to assemble and analyze dozens of genomes ranging in complexity from simple microbial species through mammalian genomes. Recent efforts have been focused on enhancing support for new data characteristics brought on by second- and now third-generation sequencing. This review describes the major components of AMOS in light of these challenges, with an emphasis on methods for assessing assembly quality and the visual analytics capabilities of Hawkeye. These interactive graphical aspects are essential for navigating and understanding the complexities of a genome assembly, from the overall genome structure down to individual bases. Hawkeye and AMOS are available open source at http://amos.sourceforge.net. PMID:22199379

VisualUrText: A Text Analytics Tool for Unstructured Textual Data

NASA Astrophysics Data System (ADS)

Zainol, Zuraini; Jaymes, Mohd T. H.; Nohuddin, Puteri N. E.

2018-05-01

The growing amount of unstructured text over Internet is tremendous. Text repositories come from Web 2.0, business intelligence and social networking applications. It is also believed that 80-90% of future growth data is available in the form of unstructured text databases that may potentially contain interesting patterns and trends. Text Mining is well known technique for discovering interesting patterns and trends which are non-trivial knowledge from massive unstructured text data. Text Mining covers multidisciplinary fields involving information retrieval (IR), text analysis, natural language processing (NLP), data mining, machine learning statistics and computational linguistics. This paper discusses the development of text analytics tool that is proficient in extracting, processing, analyzing the unstructured text data and visualizing cleaned text data into multiple forms such as Document Term Matrix (DTM), Frequency Graph, Network Analysis Graph, Word Cloud and Dendogram. This tool, VisualUrText, is developed to assist students and researchers for extracting interesting patterns and trends in document analyses.

Social Media Visual Analytics for Events

NASA Astrophysics Data System (ADS)

Diakopoulos, Nicholas; Naaman, Mor; Yazdani, Tayebeh; Kivran-Swaine, Funda

For large-scale multimedia events such as televised debates and speeches, the amount of content on social media channels such as Facebook or Twitter can easily become overwhelming, yet still contain information that may aid and augment understanding of the multimedia content via individual social media items, or aggregate information from the crowd's response. In this work we discuss this opportunity in the context of a social media visual analytic tool, Vox Civitas, designed to help journalists, media professionals, or other researchers make sense of large-scale aggregations of social media content around multimedia broadcast events. We discuss the design of the tool, present and evaluate the text analysis techniques used to enable the presentation, and detail the visual and interaction design. We provide an exploratory evaluation based on a user study in which journalists interacted with the system to analyze and report on a dataset of over one 100 000 Twitter messages collected during the broadcast of the U.S. State of the Union presidential address in 2010.

LipidQC: Method Validation Tool for Visual Comparison to SRM 1950 Using NIST Interlaboratory Comparison Exercise Lipid Consensus Mean Estimate Values.

PubMed

Ulmer, Candice Z; Ragland, Jared M; Koelmel, Jeremy P; Heckert, Alan; Jones, Christina M; Garrett, Timothy J; Yost, Richard A; Bowden, John A

2017-12-19

As advances in analytical separation techniques, mass spectrometry instrumentation, and data processing platforms continue to spur growth in the lipidomics field, more structurally unique lipid species are detected and annotated. The lipidomics community is in need of benchmark reference values to assess the validity of various lipidomics workflows in providing accurate quantitative measurements across the diverse lipidome. LipidQC addresses the harmonization challenge in lipid quantitation by providing a semiautomated process, independent of analytical platform, for visual comparison of experimental results of National Institute of Standards and Technology Standard Reference Material (SRM) 1950, "Metabolites in Frozen Human Plasma", against benchmark consensus mean concentrations derived from the NIST Lipidomics Interlaboratory Comparison Exercise.

KOLAM: a cross-platform architecture for scalable visualization and tracking in wide-area imagery

NASA Astrophysics Data System (ADS)

Fraser, Joshua; Haridas, Anoop; Seetharaman, Guna; Rao, Raghuveer M.; Palaniappan, Kannappan

2013-05-01

KOLAM is an open, cross-platform, interoperable, scalable and extensible framework supporting a novel multi- scale spatiotemporal dual-cache data structure for big data visualization and visual analytics. This paper focuses on the use of KOLAM for target tracking in high-resolution, high throughput wide format video also known as wide-area motion imagery (WAMI). It was originally developed for the interactive visualization of extremely large geospatial imagery of high spatial and spectral resolution. KOLAM is platform, operating system and (graphics) hardware independent, and supports embedded datasets scalable from hundreds of gigabytes to feasibly petabytes in size on clusters, workstations, desktops and mobile computers. In addition to rapid roam, zoom and hyper- jump spatial operations, a large number of simultaneously viewable embedded pyramid layers (also referred to as multiscale or sparse imagery), interactive colormap and histogram enhancement, spherical projection and terrain maps are supported. The KOLAM software architecture was extended to support airborne wide-area motion imagery by organizing spatiotemporal tiles in very large format video frames using a temporal cache of tiled pyramid cached data structures. The current version supports WAMI animation, fast intelligent inspection, trajectory visualization and target tracking (digital tagging); the latter by interfacing with external automatic tracking software. One of the critical needs for working with WAMI is a supervised tracking and visualization tool that allows analysts to digitally tag multiple targets, quickly review and correct tracking results and apply geospatial visual analytic tools on the generated trajectories. One-click manual tracking combined with multiple automated tracking algorithms are available to assist the analyst and increase human effectiveness.

In with the new, out with the old? Auto-extraction for remote sensing archaeology

NASA Astrophysics Data System (ADS)

Cowley, David C.

2012-09-01

This paper explores aspects of the inter-relationships between traditional archaeological interpretation of remote sensed data (principally visual examination of aerial photographs/satellite) and those drawing on automated feature extraction and processing. Established approaches to archaeological interpretation of aerial photographs are heavily reliant on individual observation (eye/brain) in an experience and knowledge-based process. Increasingly, however, much more complex and extensive datasets are becoming available to archaeology and these require critical reflection on analytical and interpretative processes. Archaeological applications of Airborne Laser Scanning (ALS) are becoming increasingly routine, and as the spatial resolution of hyper-spectral data improves, its potentially massive implications for archaeological site detection may prove to be a sea-change. These complex datasets demand new approaches, as traditional methods based on direct observation by an archaeological interpreter will never do more than scratch the surface, and will fail to fully extend the boundaries of knowledge. Inevitably, changing analytical and interpretative processes can create tensions, especially, as has been the case in archaeology, when the innovations in data and analysis come from outside the discipline. These tensions often centre on the character of the information produced, and a lack of clarity on the place of archaeological interpretation in the workflow. This is especially true for ALS data and autoextraction techniques, and carries implications for all forms of remote sensed archaeological datasets, including hyperspectral data and aerial photographs.

The Relationship between Visual Analysis and Five Statistical Analyses in a Simple AB Single-Case Research Design

ERIC Educational Resources Information Center

Brossart, Daniel F.; Parker, Richard I.; Olson, Elizabeth A.; Mahadevan, Lakshmi

2006-01-01

This study explored some practical issues for single-case researchers who rely on visual analysis of graphed data, but who also may consider supplemental use of promising statistical analysis techniques. The study sought to answer three major questions: (a) What is a typical range of effect sizes from these analytic techniques for data from…

Steady-State Visual Evoked Potentials and Phase Synchronization in Migraine Patients

NASA Astrophysics Data System (ADS)

Angelini, L.; Tommaso, M. De; Guido, M.; Hu, K.; Ivanov, P. Ch.; Marinazzo, D.; Nardulli, G.; Nitti, L.; Pellicoro, M.; Pierro, C.; Stramaglia, S.

2004-07-01

We investigate phase synchronization in EEG recordings from migraine patients. We use the analytic signal technique, based on the Hilbert transform, and find that migraine brains are characterized by enhanced alpha band phase synchronization in the presence of visual stimuli. Our findings show that migraine patients have an overactive regulatory mechanism that renders them more sensitive to external stimuli.

ToxPi Graphical User Interface 2.0: Dynamic exploration, visualization, and sharing of integrated data models.

PubMed

Marvel, Skylar W; To, Kimberly; Grimm, Fabian A; Wright, Fred A; Rusyn, Ivan; Reif, David M

2018-03-05

Drawing integrated conclusions from diverse source data requires synthesis across multiple types of information. The ToxPi (Toxicological Prioritization Index) is an analytical framework that was developed to enable integration of multiple sources of evidence by transforming data into integrated, visual profiles. Methodological improvements have advanced ToxPi and expanded its applicability, necessitating a new, consolidated software platform to provide functionality, while preserving flexibility for future updates. We detail the implementation of a new graphical user interface for ToxPi (Toxicological Prioritization Index) that provides interactive visualization, analysis, reporting, and portability. The interface is deployed as a stand-alone, platform-independent Java application, with a modular design to accommodate inclusion of future analytics. The new ToxPi interface introduces several features, from flexible data import formats (including legacy formats that permit backward compatibility) to similarity-based clustering to options for high-resolution graphical output. We present the new ToxPi interface for dynamic exploration, visualization, and sharing of integrated data models. The ToxPi interface is freely-available as a single compressed download that includes the main Java executable, all libraries, example data files, and a complete user manual from http://toxpi.org .

Integrated Approach to Reconstruction of Microbial Regulatory Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rodionov, Dmitry A; Novichkov, Pavel S

2013-11-04

This project had the goal(s) of development of integrated bioinformatics platform for genome-scale inference and visualization of transcriptional regulatory networks (TRNs) in bacterial genomes. The work was done in Sanford-Burnham Medical Research Institute (SBMRI, P.I. D.A. Rodionov) and Lawrence Berkeley National Laboratory (LBNL, co-P.I. P.S. Novichkov). The developed computational resources include: (1) RegPredict web-platform for TRN inference and regulon reconstruction in microbial genomes, and (2) RegPrecise database for collection, visualization and comparative analysis of transcriptional regulons reconstructed by comparative genomics. These analytical resources were selected as key components in the DOE Systems Biology KnowledgeBase (SBKB). The high-quality data accumulated inmore » RegPrecise will provide essential datasets of reference regulons in diverse microbes to enable automatic reconstruction of draft TRNs in newly sequenced genomes. We outline our progress toward the three aims of this grant proposal, which were: Develop integrated platform for genome-scale regulon reconstruction; Infer regulatory annotations in several groups of bacteria and building of reference collections of microbial regulons; and Develop KnowledgeBase on microbial transcriptional regulation.« less

Kinematic/Dynamic Characteristics for Visual and Kinesthetic Virtual Environments

NASA Technical Reports Server (NTRS)

Bortolussi, Michael R. (Compiler); Adelstein, B. D.; Gold, Miriam

1996-01-01

Work was carried out on two topics of principal importance to current progress in virtual environment research at NASA Ames and elsewhere. The first topic was directed at maximizing the temporal dynamic response of visually presented Virtual Environments (VEs) through reorganization and optimization of system hardware and software. The final results of this portion of the work was a VE system in the Advanced Display and Spatial Perception Laboratory at NASA Ames capable of updating at 60 Hz (the maximum hardware refresh rate) with latencies approaching 30 msec. In the course of achieving this system performance, specialized hardware and software tools for measurement of VE latency and analytic models correlating update rate and latency for different system configurations were developed. The second area of activity was the preliminary development and analysis of a novel kinematic architecture for three Degree Of Freedom (DOF) haptic interfaces--devices that provide force feedback for manipulative interaction with virtual and remote environments. An invention disclosure was filed on this work and a patent application is being pursued by NASA Ames. Activities in these two areas are expanded upon below.

Towards sustainable infrastructure management: knowledge-based service-oriented computing framework for visual analytics

NASA Astrophysics Data System (ADS)

Vatcha, Rashna; Lee, Seok-Won; Murty, Ajeet; Tolone, William; Wang, Xiaoyu; Dou, Wenwen; Chang, Remco; Ribarsky, William; Liu, Wanqiu; Chen, Shen-en; Hauser, Edd

2009-05-01

Infrastructure management (and its associated processes) is complex to understand, perform and thus, hard to make efficient and effective informed decisions. The management involves a multi-faceted operation that requires the most robust data fusion, visualization and decision making. In order to protect and build sustainable critical assets, we present our on-going multi-disciplinary large-scale project that establishes the Integrated Remote Sensing and Visualization (IRSV) system with a focus on supporting bridge structure inspection and management. This project involves specific expertise from civil engineers, computer scientists, geographers, and real-world practitioners from industry, local and federal government agencies. IRSV is being designed to accommodate the essential needs from the following aspects: 1) Better understanding and enforcement of complex inspection process that can bridge the gap between evidence gathering and decision making through the implementation of ontological knowledge engineering system; 2) Aggregation, representation and fusion of complex multi-layered heterogeneous data (i.e. infrared imaging, aerial photos and ground-mounted LIDAR etc.) with domain application knowledge to support machine understandable recommendation system; 3) Robust visualization techniques with large-scale analytical and interactive visualizations that support users' decision making; and 4) Integration of these needs through the flexible Service-oriented Architecture (SOA) framework to compose and provide services on-demand. IRSV is expected to serve as a management and data visualization tool for construction deliverable assurance and infrastructure monitoring both periodically (annually, monthly, even daily if needed) as well as after extreme events.

Shape anomaly detection under strong measurement noise: An analytical approach to adaptive thresholding

NASA Astrophysics Data System (ADS)

Krasichkov, Alexander S.; Grigoriev, Eugene B.; Bogachev, Mikhail I.; Nifontov, Eugene M.

2015-10-01

We suggest an analytical approach to the adaptive thresholding in a shape anomaly detection problem. We find an analytical expression for the distribution of the cosine similarity score between a reference shape and an observational shape hindered by strong measurement noise that depends solely on the noise level and is independent of the particular shape analyzed. The analytical treatment is also confirmed by computer simulations and shows nearly perfect agreement. Using this analytical solution, we suggest an improved shape anomaly detection approach based on adaptive thresholding. We validate the noise robustness of our approach using typical shapes of normal and pathological electrocardiogram cycles hindered by additive white noise. We show explicitly that under high noise levels our approach considerably outperforms the conventional tactic that does not take into account variations in the noise level.

Describing the performance of U.S. hospitals by applying big data analytics.

PubMed

Downing, Nicholas S; Cloninger, Alexander; Venkatesh, Arjun K; Hsieh, Angela; Drye, Elizabeth E; Coifman, Ronald R; Krumholz, Harlan M

2017-01-01

Public reporting of measures of hospital performance is an important component of quality improvement efforts in many countries. However, it can be challenging to provide an overall characterization of hospital performance because there are many measures of quality. In the United States, the Centers for Medicare and Medicaid Services reports over 100 measures that describe various domains of hospital quality, such as outcomes, the patient experience and whether established processes of care are followed. Although individual quality measures provide important insight, it is challenging to understand hospital performance as characterized by multiple quality measures. Accordingly, we developed a novel approach for characterizing hospital performance that highlights the similarities and differences between hospitals and identifies common patterns of hospital performance. Specifically, we built a semi-supervised machine learning algorithm and applied it to the publicly-available quality measures for 1,614 U.S. hospitals to graphically and quantitatively characterize hospital performance. In the resulting visualization, the varying density of hospitals demonstrates that there are key clusters of hospitals that share specific performance profiles, while there are other performance profiles that are rare. Several popular hospital rating systems aggregate some of the quality measures included in our study to produce a composite score; however, hospitals that were top-ranked by such systems were scattered across our visualization, indicating that these top-ranked hospitals actually excel in many different ways. Our application of a novel graph analytics method to data describing U.S. hospitals revealed nuanced differences in performance that are obscured in existing hospital rating systems.

Prospective, observational study comparing automated and visual point-of-care urinalysis in general practice

PubMed Central

van Delft, Sanne; Goedhart, Annelijn; Spigt, Mark; van Pinxteren, Bart; de Wit, Niek; Hopstaken, Rogier

2016-01-01

Objective Point-of-care testing (POCT) urinalysis might reduce errors in (subjective) reading, registration and communication of test results, and might also improve diagnostic outcome and optimise patient management. Evidence is lacking. In the present study, we have studied the analytical performance of automated urinalysis and visual urinalysis compared with a reference standard in routine general practice. Setting The study was performed in six general practitioner (GP) group practices in the Netherlands. Automated urinalysis was compared with visual urinalysis in these practices. Reference testing was performed in a primary care laboratory (Saltro, Utrecht, The Netherlands). Primary and secondary outcome measures Analytical performance of automated and visual urinalysis compared with the reference laboratory method was the primary outcome measure, analysed by calculating sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) and Cohen's κ coefficient for agreement. Secondary outcome measure was the user-friendliness of the POCT analyser. Results Automated urinalysis by experienced and routinely trained practice assistants in general practice performs as good as visual urinalysis for nitrite, leucocytes and erythrocytes. Agreement for nitrite is high for automated and visual urinalysis. κ's are 0.824 and 0.803 (ranked as very good and good, respectively). Agreement with the central laboratory reference standard for automated and visual urinalysis for leucocytes is rather poor (0.256 for POCT and 0.197 for visual, respectively, ranked as fair and poor). κ's for erythrocytes are higher: 0.517 (automated) and 0.416 (visual), both ranked as moderate. The Urisys 1100 analyser was easy to use and considered to be not prone to flaws. Conclusions Automated urinalysis performed as good as traditional visual urinalysis on reading of nitrite, leucocytes and erythrocytes in routine general practice. Implementation of automated urinalysis in general practice is justified as automation is expected to reduce human errors in patient identification and transcribing of results. PMID:27503860

Prospective, observational study comparing automated and visual point-of-care urinalysis in general practice.

PubMed

van Delft, Sanne; Goedhart, Annelijn; Spigt, Mark; van Pinxteren, Bart; de Wit, Niek; Hopstaken, Rogier

2016-08-08

Point-of-care testing (POCT) urinalysis might reduce errors in (subjective) reading, registration and communication of test results, and might also improve diagnostic outcome and optimise patient management. Evidence is lacking. In the present study, we have studied the analytical performance of automated urinalysis and visual urinalysis compared with a reference standard in routine general practice. The study was performed in six general practitioner (GP) group practices in the Netherlands. Automated urinalysis was compared with visual urinalysis in these practices. Reference testing was performed in a primary care laboratory (Saltro, Utrecht, The Netherlands). Analytical performance of automated and visual urinalysis compared with the reference laboratory method was the primary outcome measure, analysed by calculating sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) and Cohen's κ coefficient for agreement. Secondary outcome measure was the user-friendliness of the POCT analyser. Automated urinalysis by experienced and routinely trained practice assistants in general practice performs as good as visual urinalysis for nitrite, leucocytes and erythrocytes. Agreement for nitrite is high for automated and visual urinalysis. κ's are 0.824 and 0.803 (ranked as very good and good, respectively). Agreement with the central laboratory reference standard for automated and visual urinalysis for leucocytes is rather poor (0.256 for POCT and 0.197 for visual, respectively, ranked as fair and poor). κ's for erythrocytes are higher: 0.517 (automated) and 0.416 (visual), both ranked as moderate. The Urisys 1100 analyser was easy to use and considered to be not prone to flaws. Automated urinalysis performed as good as traditional visual urinalysis on reading of nitrite, leucocytes and erythrocytes in routine general practice. Implementation of automated urinalysis in general practice is justified as automation is expected to reduce human errors in patient identification and transcribing of results. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Fluorescence and visual detection of fluoride ions using a photoluminescent graphene oxide paper sensor

NASA Astrophysics Data System (ADS)

Chen, Xiaochun; Yu, Shaoming; Yang, Liang; Wang, Jianping; Jiang, Changlong

2016-07-01

The instant and on-site detection of trace aqueous fluoride ions is still a challenge for environmental monitoring and protection. This work demonstrates a new analytical method and its utility of a paper sensor for visual detection of F- on the basis of the fluorescence resonance energy transfer (FRET) between photoluminescent graphene oxide (GO) and silver nanoparticles (AgNPs) through the formation of cyclic esters between phenylborinic acid and diol. The fluorescence of GO was quenched by the AgNPs, and trace F- can recover the fluorescence of the quenched photoluminescent GO. The increase in fluorescence intensity is proportional to the concentration of F- in the range of 0.05-0.55 nM, along with a limit of detection (LOD) as low as 9.07 pM. Following the sensing mechanism, a paper-based sensor for the visual detection of aqueous F- has been successfully developed. The paper sensor showed high sensitivity for aqueous F-, and the LOD could reach as low as 0.1 μM as observed by the naked eye. The very simple and effective strategy reported here could be extended to the visual detection of a wide range of analytes in the environment by the construction of highly efficient FRET nanoprobes.The instant and on-site detection of trace aqueous fluoride ions is still a challenge for environmental monitoring and protection. This work demonstrates a new analytical method and its utility of a paper sensor for visual detection of F- on the basis of the fluorescence resonance energy transfer (FRET) between photoluminescent graphene oxide (GO) and silver nanoparticles (AgNPs) through the formation of cyclic esters between phenylborinic acid and diol. The fluorescence of GO was quenched by the AgNPs, and trace F- can recover the fluorescence of the quenched photoluminescent GO. The increase in fluorescence intensity is proportional to the concentration of F- in the range of 0.05-0.55 nM, along with a limit of detection (LOD) as low as 9.07 pM. Following the sensing mechanism, a paper-based sensor for the visual detection of aqueous F- has been successfully developed. The paper sensor showed high sensitivity for aqueous F-, and the LOD could reach as low as 0.1 μM as observed by the naked eye. The very simple and effective strategy reported here could be extended to the visual detection of a wide range of analytes in the environment by the construction of highly efficient FRET nanoprobes. Electronic supplementary information (ESI) available. See DOI: 10.1039/c6nr02878k

Exploring information transmission in gene networks using stochastic simulation and machine learning

NASA Astrophysics Data System (ADS)

Park, Kyemyung; Prüstel, Thorsten; Lu, Yong; Narayanan, Manikandan; Martins, Andrew; Tsang, John

How gene regulatory networks operate robustly despite environmental fluctuations and biochemical noise is a fundamental question in biology. Mathematically the stochastic dynamics of a gene regulatory network can be modeled using chemical master equation (CME), but nonlinearity and other challenges render analytical solutions of CMEs difficult to attain. While approaches of approximation and stochastic simulation have been devised for simple models, obtaining a more global picture of a system's behaviors in high-dimensional parameter space without simplifying the system substantially remains a major challenge. Here we present a new framework for understanding and predicting the behaviors of gene regulatory networks in the context of information transmission among genes. Our approach uses stochastic simulation of the network followed by machine learning of the mapping between model parameters and network phenotypes such as information transmission behavior. We also devised ways to visualize high-dimensional phase spaces in intuitive and informative manners. We applied our approach to several gene regulatory circuit motifs, including both feedback and feedforward loops, to reveal underexplored aspects of their operational behaviors. This work is supported by the Intramural Program of NIAID/NIH.

Visual representation of scientific information.

PubMed

Wong, Bang

2011-02-15

Great technological advances have enabled researchers to generate an enormous amount of data. Data analysis is replacing data generation as the rate-limiting step in scientific research. With this wealth of information, we have an opportunity to understand the molecular causes of human diseases. However, the unprecedented scale, resolution, and variety of data pose new analytical challenges. Visual representation of data offers insights that can lead to new understanding, whether the purpose is analysis or communication. This presentation shows how art, design, and traditional illustration can enable scientific discovery. Examples will be drawn from the Broad Institute's Data Visualization Initiative, aimed at establishing processes for creating informative visualization models.

Protein comparability assessments and potential applicability of high throughput biophysical methods and data visualization tools to compare physical stability profiles

PubMed Central

Alsenaidy, Mohammad A.; Jain, Nishant K.; Kim, Jae H.; Middaugh, C. Russell; Volkin, David B.

2014-01-01

In this review, some of the challenges and opportunities encountered during protein comparability assessments are summarized with an emphasis on developing new analytical approaches to better monitor higher-order protein structures. Several case studies are presented using high throughput biophysical methods to collect protein physical stability data as function of temperature, agitation, ionic strength and/or solution pH. These large data sets were then used to construct empirical phase diagrams (EPDs), radar charts, and comparative signature diagrams (CSDs) for data visualization and structural comparisons between the different proteins. Protein samples with different sizes, post-translational modifications, and inherent stability are presented: acidic fibroblast growth factor (FGF-1) mutants, different glycoforms of an IgG1 mAb prepared by deglycosylation, as well as comparisons of different formulations of an IgG1 mAb and granulocyte colony stimulating factor (GCSF). Using this approach, differences in structural integrity and conformational stability profiles were detected under stress conditions that could not be resolved by using the same techniques under ambient conditions (i.e., no stress). Thus, an evaluation of conformational stability differences may serve as an effective surrogate to monitor differences in higher-order structure between protein samples. These case studies are discussed in the context of potential utility in protein comparability studies. PMID:24659968

Protein comparability assessments and potential applicability of high throughput biophysical methods and data visualization tools to compare physical stability profiles.

PubMed

Alsenaidy, Mohammad A; Jain, Nishant K; Kim, Jae H; Middaugh, C Russell; Volkin, David B

2014-01-01

In this review, some of the challenges and opportunities encountered during protein comparability assessments are summarized with an emphasis on developing new analytical approaches to better monitor higher-order protein structures. Several case studies are presented using high throughput biophysical methods to collect protein physical stability data as function of temperature, agitation, ionic strength and/or solution pH. These large data sets were then used to construct empirical phase diagrams (EPDs), radar charts, and comparative signature diagrams (CSDs) for data visualization and structural comparisons between the different proteins. Protein samples with different sizes, post-translational modifications, and inherent stability are presented: acidic fibroblast growth factor (FGF-1) mutants, different glycoforms of an IgG1 mAb prepared by deglycosylation, as well as comparisons of different formulations of an IgG1 mAb and granulocyte colony stimulating factor (GCSF). Using this approach, differences in structural integrity and conformational stability profiles were detected under stress conditions that could not be resolved by using the same techniques under ambient conditions (i.e., no stress). Thus, an evaluation of conformational stability differences may serve as an effective surrogate to monitor differences in higher-order structure between protein samples. These case studies are discussed in the context of potential utility in protein comparability studies.

Counterfeit drugs: analytical techniques for their identification.

PubMed

Martino, R; Malet-Martino, M; Gilard, V; Balayssac, S

2010-09-01

In recent years, the number of counterfeit drugs has increased dramatically, including not only "lifestyle" products but also vital medicines. Besides the threat to public health, the financial and reputational damage to pharmaceutical companies is substantial. The lack of robust information on the prevalence of fake drugs is an obstacle in the fight against drug counterfeiting. It is generally accepted that approximately 10% of drugs worldwide could be counterfeit, but it is also well known that this number covers very different situations depending on the country, the places where the drugs are purchased, and the definition of what constitutes a counterfeit drug. The chemical analysis of drugs suspected to be fake is a crucial step as counterfeiters are becoming increasingly sophisticated, rendering visual inspection insufficient to distinguish the genuine products from the counterfeit ones. This article critically reviews the recent analytical methods employed to control the quality of drug formulations, using as an example artemisinin derivatives, medicines particularly targeted by counterfeiters. Indeed, a broad panel of techniques have been reported for their analysis, ranging from simple and cheap in-field ones (colorimetry and thin-layer chromatography) to more advanced laboratory methods (mass spectrometry, nuclear magnetic resonance, and vibrational spectroscopies) through chromatographic methods, which remain the most widely used. The conclusion section of the article highlights the questions to be posed before selecting the most appropriate analytical approach.

Visual Analytics for Power Grid Contingency Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wong, Pak C.; Huang, Zhenyu; Chen, Yousu

2014-01-20

Contingency analysis is the process of employing different measures to model scenarios, analyze them, and then derive the best response to remove the threats. This application paper focuses on a class of contingency analysis problems found in the power grid management system. A power grid is a geographically distributed interconnected transmission network that transmits and delivers electricity from generators to end users. The power grid contingency analysis problem is increasingly important because of both the growing size of the underlying raw data that need to be analyzed and the urgency to deliver working solutions in an aggressive timeframe. Failure tomore » do so may bring significant financial, economic, and security impacts to all parties involved and the society at large. The paper presents a scalable visual analytics pipeline that transforms about 100 million contingency scenarios to a manageable size and form for grid operators to examine different scenarios and come up with preventive or mitigation strategies to address the problems in a predictive and timely manner. Great attention is given to the computational scalability, information scalability, visual scalability, and display scalability issues surrounding the data analytics pipeline. Most of the large-scale computation requirements of our work are conducted on a Cray XMT multi-threaded parallel computer. The paper demonstrates a number of examples using western North American power grid models and data.« less

How the viewing of familiar landscapes prior to release allows pigeons to home faster: evidence from GPS tracking.

PubMed

Biro, Dora; Guilford, Tim; Dell'Omo, Giacomo; Lipp, Hans-Peter

2002-12-01

Providing homing pigeons with a 5 min preview of the landscape at familiar sites prior to release reliably improves the birds' subsequent homing speeds. This phenomenon has been taken to suggest that the visual panorama is involved in familiar-site recognition, yet the exact nature of the improvement has never been elucidated. We employed newly developed miniature Global Positioning System (GPS) tracking technology to investigate how access to visual cues prior to release affects pigeons' flight along the length of the homing route. By applying a variety of novel analytical techniques enabled by the high-resolution GPS data (track efficiency, virtual vanishing bearings, orientation threshold), we localised the preview effect to the first 1000 m of the journey. Birds denied preview of a familiar landscape for 5 min before take-off flew an initially more tortuous path, including a high incidence of circling, possibly as part of an information-gathering strategy to determine their position. Beyond the first 1000 m, no differences were found in the performance of birds with or without preview. That the effect of the visual treatment was evident only in the early part of the journey suggests that lack of access to visual cues prior to release does not result in a non-specific effect on behaviour that is maintained throughout the flight. Instead, it seems that at least some decisions regarding the direction of home can be made prior to release and that such decisions are delayed if visual access to the landscape is denied. Overall, the variety of approaches applied here clearly highlight the potential for future applications of GPS tracking technology in navigation studies.

A participatory approach to designing and enhancing integrated health information technology systems for veterans: protocol.

PubMed

Haun, Jolie N; Nazi, Kim M; Chavez, Margeaux; Lind, Jason D; Antinori, Nicole; Gosline, Robert M; Martin, Tracey L

2015-02-27

The Department of Veterans Affairs (VA) has developed health information technologies (HIT) and resources to improve veteran access to health care programs and services, and to support a patient-centered approach to health care delivery. To improve VA HIT access and meaningful use by veterans, it is necessary to understand their preferences for interacting with various HIT resources to accomplish health management related tasks and to exchange information. The objective of this paper was to describe a novel protocol for: (1) developing a HIT Digital Health Matrix Model; (2) conducting an Analytic Hierarchy Process called pairwise comparison to understand how and why veterans want to use electronic health resources to complete tasks related to health management; and (3) developing visual modeling simulations that depict veterans' preferences for using VA HIT to manage their health conditions and exchange health information. The study uses participatory research methods to understand how veterans prefer to use VA HIT to accomplish health management tasks within a given context, and how they would like to interact with HIT interfaces (eg, look, feel, and function) in the future. This study includes two rounds of veteran focus groups with self-administered surveys and visual modeling simulation techniques. This study will also convene an expert panel to assist in the development of a VA HIT Digital Health Matrix Model, so that both expert panel members and veteran participants can complete an Analytic Hierarchy Process, pairwise comparisons to evaluate and rank the applicability of electronic health resources for a series of health management tasks. This protocol describes the iterative, participatory, and patient-centered process for: (1) developing a VA HIT Digital Health Matrix Model that outlines current VA patient-facing platforms available to veterans, describing their features and relevant contexts for use; and (2) developing visual model simulations based on direct veteran feedback that depict patient preferences for enhancing the synchronization, integration, and standardization of VA patient-facing platforms. Focus group topics include current uses, preferences, facilitators, and barriers to using electronic health resources; recommendations for synchronizing, integrating, and standardizing VA HIT; and preferences on data sharing and delegation within the VA system. This work highlights the practical, technological, and personal factors that facilitate and inhibit use of current VA HIT, and informs an integrated system redesign. The Digital Health Matrix Model and visual modeling simulations use knowledge of veteran preferences and experiences to directly inform enhancements to VA HIT and provide a more holistic and integrated user experience. These efforts are designed to support the adoption and sustained use of VA HIT to support patient self-management and clinical care coordination in ways that are directly aligned with veteran preferences.

A Participatory Approach to Designing and Enhancing Integrated Health Information Technology Systems for Veterans: Protocol

PubMed Central

Nazi, Kim M; Chavez, Margeaux; Lind, Jason D; Antinori, Nicole; Gosline, Robert M; Martin, Tracey L

2015-01-01

Background The Department of Veterans Affairs (VA) has developed health information technologies (HIT) and resources to improve veteran access to health care programs and services, and to support a patient-centered approach to health care delivery. To improve VA HIT access and meaningful use by veterans, it is necessary to understand their preferences for interacting with various HIT resources to accomplish health management related tasks and to exchange information. Objective The objective of this paper was to describe a novel protocol for: (1) developing a HIT Digital Health Matrix Model; (2) conducting an Analytic Hierarchy Process called pairwise comparison to understand how and why veterans want to use electronic health resources to complete tasks related to health management; and (3) developing visual modeling simulations that depict veterans’ preferences for using VA HIT to manage their health conditions and exchange health information. Methods The study uses participatory research methods to understand how veterans prefer to use VA HIT to accomplish health management tasks within a given context, and how they would like to interact with HIT interfaces (eg, look, feel, and function) in the future. This study includes two rounds of veteran focus groups with self-administered surveys and visual modeling simulation techniques. This study will also convene an expert panel to assist in the development of a VA HIT Digital Health Matrix Model, so that both expert panel members and veteran participants can complete an Analytic Hierarchy Process, pairwise comparisons to evaluate and rank the applicability of electronic health resources for a series of health management tasks. Results This protocol describes the iterative, participatory, and patient-centered process for: (1) developing a VA HIT Digital Health Matrix Model that outlines current VA patient-facing platforms available to veterans, describing their features and relevant contexts for use; and (2) developing visual model simulations based on direct veteran feedback that depict patient preferences for enhancing the synchronization, integration, and standardization of VA patient-facing platforms. Focus group topics include current uses, preferences, facilitators, and barriers to using electronic health resources; recommendations for synchronizing, integrating, and standardizing VA HIT; and preferences on data sharing and delegation within the VA system. Conclusions This work highlights the practical, technological, and personal factors that facilitate and inhibit use of current VA HIT, and informs an integrated system redesign. The Digital Health Matrix Model and visual modeling simulations use knowledge of veteran preferences and experiences to directly inform enhancements to VA HIT and provide a more holistic and integrated user experience. These efforts are designed to support the adoption and sustained use of VA HIT to support patient self-management and clinical care coordination in ways that are directly aligned with veteran preferences. PMID:25803324

A Visual Analytic for Improving Human Terrain Understanding

DTIC Science & Technology

2013-06-01

Kim, S., Minotra, D., Strater, L ., Cuevas, and Colombo, D. “Knowledge Visualization to Enhance Human-Agent Situation Awareness within a Computational...1971). A General Coefficient of Similarity and Some of Its Properties Biometrics, Vol. 27, No. 4, pp. 857-871. [14] Coppock, S. & Mazlack, L ...and allow human interpretation. HDPT Component Overview PostgreSQL DBS Apache Tomcat Web Server [’...... _./ Globa l Graph Web ~ Application

Evaluation of a Technology for Teaching Complex Social Skills to Young Adults with Visual and Cognitive Impairments.

ERIC Educational Resources Information Center

Gumpel, Thomas P.; Nativ-Ari-Am, Hagit

2001-01-01

Two multiple baseline designs were used to evaluate a two-stage model for training four young adults with visual and cognitive impairments to grocery shop. A task-analytical flow chart of the behavioral skills involved in grocery shopping was used to increase completed skill steps and the number of correct items purchased. (Contains references.)…

Beyond Compliance Checking: A Situated Approach to Visual Research Ethics.

PubMed

Lenette, Caroline; Botfield, Jessica R; Boydell, Katherine; Haire, Bridget; Newman, Christy E; Zwi, Anthony B

2018-03-19

Visual research methods like photography and digital storytelling are increasingly used in health and social sciences research as participatory approaches that benefit participants, researchers, and audiences. Visual methods involve a number of additional ethical considerations such as using identifiable content and ownership of creative outputs. As such, ethics committees should use different assessment frameworks to consider research protocols with visual methods. Here, we outline the limitations of ethics committees in assessing projects with a visual focus and highlight the sparse knowledge on how researchers respond when they encounter ethical challenges in the practice of visual research. We propose a situated approach in relation to visual methodologies that encompasses a negotiated, flexible approach, given that ethical issues usually emerge in relation to the specific contexts of individual research projects. Drawing on available literature and two case studies, we identify and reflect on nuanced ethical implications in visual research, like tensions between aesthetics and research validity. The case studies highlight strategies developed in-situ to address the challenges two researchers encountered when using visual research methods, illustrating that some practice implications are not necessarily addressed using established ethical clearance procedures. A situated approach can ensure that visual research remains ethical, engaging, and rigorous.

An Interactive Approach to Learning and Teaching in Visual Arts Education

ERIC Educational Resources Information Center

Tomljenovic, Zlata

2015-01-01

The present research focuses on modernising the approach to learning and teaching the visual arts in teaching practice, as well as examining the performance of an interactive approach to learning and teaching in visual arts classes with the use of a combination of general and specific (visual arts) teaching methods. The study uses quantitative…

Multifield-graphs: an approach to visualizing correlations in multifield scalar data.

PubMed

Sauber, Natascha; Theisel, Holger; Seidel, Hans-Peter

2006-01-01

We present an approach to visualizing correlations in 3D multifield scalar data. The core of our approach is the computation of correlation fields, which are scalar fields containing the local correlations of subsets of the multiple fields. While the visualization of the correlation fields can be done using standard 3D volume visualization techniques, their huge number makes selection and handling a challenge. We introduce the Multifield-Graph to give an overview of which multiple fields correlate and to show the strength of their correlation. This information guides the selection of informative correlation fields for visualization. We use our approach to visually analyze a number of real and synthetic multifield datasets.

A far-field-viewing sensor for making analytical measurements in remote locations.

PubMed

Michael, K L; Taylor, L C; Walt, D R

1999-07-15

We demonstrate a far-field-viewing GRINscope sensor for making analytical measurements in remote locations. The GRINscope was fabricated by permanently affixing a micro-Gradient index (GRIN) lens on the distal face of a 350-micron-diameter optical imaging fiber. The GRINscope can obtain both chemical and visual information. In one application, a thin, pH-sensitive polymer layer was immobilized on the distal end of the GRINscope. The ability of the GRINscope to visually image its far-field surroundings and concurrently detect pH changes in a flowing stream was demonstrated. In a different application, the GRINscope was used to image pH- and O2-sensitive particles on a remote substrate and simultaneously measure their fluorescence intensity in response to pH or pO2 changes.

Metal-enhanced fluorescence/visual bimodal platform for multiplexed ultrasensitive detection of microRNA with reusable paper analytical devices.

PubMed

Liang, Linlin; Lan, Feifei; Yin, Xuemei; Ge, Shenguang; Yu, Jinghua; Yan, Mei

2017-09-15

Convenient biosensor for simultaneous multi-analyte detection was increasingly required in biological analysis. A novel flower-like silver (FLS)-enhanced fluorescence/visual bimodal platform for the ultrasensitive detection of multiple miRNAs was successfully constructed for the first time based on the principle of multi-channel microfluidic paper-based analytical devices (µPADs). Fluorophore-functionalized DNA 1 (DNA 1 -N-CDs) was combined with FLS, which was hybridized with quencher-carrying strand (DNA 2 -CeO 2 ) to form FLS-enhanced fluorescence biosensor. Upon the addition of the target miRNA, the fluorescent intensity of DNA 1 -N-CDs within the proximity of the FLS was strengthened. The disengaged DNA/CeO 2 complex could result in color change after joining H 2 O 2 , leading to real-time visual detection of miRNA firstly. If necessary, then the fluorescence method was applied for a accurate determination. In this strategy, the growth of FLS in µPADs not only reduced the background fluorescence but also provided an enrichment of "hot spots" for surface enhanced fluorescence detection of miRNAs. Results also showed versatility of the FLS in the enhancement of sensitivity and selectivity of the miRNA biosensor. Remarkably, this biosensor could detect as low as 0.03fM miRNA210 and 0.06fM miRNA21. Interestingly, the proposed biosensor also possessed good capability of recycling in three cycles upon change of the supplementation of DNA 2 -CeO 2 and visual substitutive device. This method opened new opportunities for further studies of miRNA related bioprocesses and will provide a new instrument for simultaneous detection of multiple low-level biomarkers. Copyright © 2017 Elsevier B.V. All rights reserved.

Coastal On-line Assessment and Synthesis Tool 2.0

NASA Technical Reports Server (NTRS)

Brown, Richard; Navard, Andrew; Nguyen, Beth

2011-01-01

COAST (Coastal On-line Assessment and Synthesis Tool) is a 3D, open-source Earth data browser developed by leveraging and enhancing previous NASA open-source tools. These tools use satellite imagery and elevation data in a way that allows any user to zoom from orbit view down into any place on Earth, and enables the user to experience Earth terrain in a visually rich 3D view. The benefits associated with taking advantage of an open-source geo-browser are that it is free, extensible, and offers a worldwide developer community that is available to provide additional development and improvement potential. What makes COAST unique is that it simplifies the process of locating and accessing data sources, and allows a user to combine them into a multi-layered and/or multi-temporal visual analytical look into possible data interrelationships and coeffectors for coastal environment phenomenology. COAST provides users with new data visual analytic capabilities. COAST has been upgraded to maximize use of open-source data access, viewing, and data manipulation software tools. The COAST 2.0 toolset has been developed to increase access to a larger realm of the most commonly implemented data formats used by the coastal science community. New and enhanced functionalities that upgrade COAST to COAST 2.0 include the development of the Temporal Visualization Tool (TVT) plug-in, the Recursive Online Remote Data-Data Mapper (RECORD-DM) utility, the Import Data Tool (IDT), and the Add Points Tool (APT). With these improvements, users can integrate their own data with other data sources, and visualize the resulting layers of different data types (such as spatial and spectral, for simultaneous visual analysis), and visualize temporal changes in areas of interest.

WarpIV: In situ visualization and analysis of ion accelerator simulations

DOE PAGES

Rubel, Oliver; Loring, Burlen; Vay, Jean -Luc; ...

2016-05-09

The generation of short pulses of ion beams through the interaction of an intense laser with a plasma sheath offers the possibility of compact and cheaper ion sources for many applications--from fast ignition and radiography of dense targets to hadron therapy and injection into conventional accelerators. To enable the efficient analysis of large-scale, high-fidelity particle accelerator simulations using the Warp simulation suite, the authors introduce the Warp In situ Visualization Toolkit (WarpIV). WarpIV integrates state-of-the-art in situ visualization and analysis using VisIt with Warp, supports management and control of complex in situ visualization and analysis workflows, and implements integrated analyticsmore » to facilitate query- and feature-based data analytics and efficient large-scale data analysis. WarpIV enables for the first time distributed parallel, in situ visualization of the full simulation data using high-performance compute resources as the data is being generated by Warp. The authors describe the application of WarpIV to study and compare large 2D and 3D ion accelerator simulations, demonstrating significant differences in the acceleration process in 2D and 3D simulations. WarpIV is available to the public via https://bitbucket.org/berkeleylab/warpiv. The Warp In situ Visualization Toolkit (WarpIV) supports large-scale, parallel, in situ visualization and analysis and facilitates query- and feature-based analytics, enabling for the first time high-performance analysis of large-scale, high-fidelity particle accelerator simulations while the data is being generated by the Warp simulation suite. Furthermore, this supplemental material https://extras.computer.org/extra/mcg2016030022s1.pdf provides more details regarding the memory profiling and optimization and the Yee grid recentering optimization results discussed in the main article.« less

Surrogate matrix and surrogate analyte approaches for definitive quantitation of endogenous biomolecules.

PubMed

Jones, Barry R; Schultz, Gary A; Eckstein, James A; Ackermann, Bradley L

2012-10-01

Quantitation of biomarkers by LC-MS/MS is complicated by the presence of endogenous analytes. This challenge is most commonly overcome by calibration using an authentic standard spiked into a surrogate matrix devoid of the target analyte. A second approach involves use of a stable-isotope-labeled standard as a surrogate analyte to allow calibration in the actual biological matrix. For both methods, parallelism between calibration standards and the target analyte in biological matrix must be demonstrated in order to ensure accurate quantitation. In this communication, the surrogate matrix and surrogate analyte approaches are compared for the analysis of five amino acids in human plasma: alanine, valine, methionine, leucine and isoleucine. In addition, methodology based on standard addition is introduced, which enables a robust examination of parallelism in both surrogate analyte and surrogate matrix methods prior to formal validation. Results from additional assays are presented to introduce the standard-addition methodology and to highlight the strengths and weaknesses of each approach. For the analysis of amino acids in human plasma, comparable precision and accuracy were obtained by the surrogate matrix and surrogate analyte methods. Both assays were well within tolerances prescribed by regulatory guidance for validation of xenobiotic assays. When stable-isotope-labeled standards are readily available, the surrogate analyte approach allows for facile method development. By comparison, the surrogate matrix method requires greater up-front method development; however, this deficit is offset by the long-term advantage of simplified sample analysis.

NASA aviation safety reporting system

NASA Technical Reports Server (NTRS)

1978-01-01

A sample of reports relating to operations during winter weather is presented. Several reports involving problems of judgment and decisionmaking have been selected from the numerous reports representative of this area. Problems related to aeronautical charts are discussed in a number of reports. An analytic study of reports involving potential conflicts in the immediate vicinity of uncontrolled airports was performed; the results are discussed in this report. It was found that in three-fourths of 127 such conflicts, neither pilot, or only one of the pilots, was communicating position and intentions on the appropriate frequency. The importance of providing aural transfer of information, as a backup to the visual see and avoid mode of information transfer is discussed. It was also found that a large fraction of pilots involved in potential conflicts on final approach had executed straight-in approaches, rather than the recommended traffic pattern entries, prior to the conflicts. A selection of alert bulletins and responses to them by various segments of the aviation community is presented.

Comprehensive Analysis of LC/MS Data Using Pseudocolor Plots

NASA Astrophysics Data System (ADS)

Crutchfield, Christopher A.; Olson, Matthew T.; Gourgari, Evgenia; Nesterova, Maria; Stratakis, Constantine A.; Yergey, Alfred L.

2013-02-01

We have developed new applications of the pseudocolor plot for the analysis of LC/MS data. These applications include spectral averaging, analysis of variance, differential comparison of spectra, and qualitative filtering by compound class. These applications have been motivated by the need to better understand LC/MS data generated from analysis of human biofluids. The examples presented use data generated to profile steroid hormones in urine extracts from a Cushing's disease patient relative to a healthy control, but are general to any discovery-based scanning mass spectrometry technique. In addition to new visualization techniques, we introduce a new metric of variance: the relative maximum difference from the mean. We also introduce the concept of substructure-dependent analysis of steroid hormones using precursor ion scans. These new analytical techniques provide an alternative approach to traditional untargeted metabolomics workflow. We present an approach to discovery using MS that essentially eliminates alignment or preprocessing of spectra. Moreover, we demonstrate the concept that untargeted metabolomics can be achieved using low mass resolution instrumentation.

The NASA Reanalysis Ensemble Service - Advanced Capabilities for Integrated Reanalysis Access and Intercomparison

NASA Astrophysics Data System (ADS)

Tamkin, G.; Schnase, J. L.; Duffy, D.; Li, J.; Strong, S.; Thompson, J. H.

2017-12-01

NASA's efforts to advance climate analytics-as-a-service are making new capabilities available to the research community: (1) A full-featured Reanalysis Ensemble Service (RES) comprising monthly means data from multiple reanalysis data sets, accessible through an enhanced set of extraction, analytic, arithmetic, and intercomparison operations. The operations are made accessible through NASA's climate data analytics Web services and our client-side Climate Data Services Python library, CDSlib; (2) A cloud-based, high-performance Virtual Real-Time Analytics Testbed supporting a select set of climate variables. This near real-time capability enables advanced technologies like Spark and Hadoop-based MapReduce analytics over native NetCDF files; and (3) A WPS-compliant Web service interface to our climate data analytics service that will enable greater interoperability with next-generation systems such as ESGF. The Reanalysis Ensemble Service includes the following: - New API that supports full temporal, spatial, and grid-based resolution services with sample queries - A Docker-ready RES application to deploy across platforms - Extended capabilities that enable single- and multiple reanalysis area average, vertical average, re-gridding, standard deviation, and ensemble averages - Convenient, one-stop shopping for commonly used data products from multiple reanalyses including basic sub-setting and arithmetic operations (e.g., avg, sum, max, min, var, count, anomaly) - Full support for the MERRA-2 reanalysis dataset in addition to, ECMWF ERA-Interim, NCEP CFSR, JMA JRA-55 and NOAA/ESRL 20CR… - A Jupyter notebook-based distribution mechanism designed for client use cases that combines CDSlib documentation with interactive scenarios and personalized project management - Supporting analytic services for NASA GMAO Forward Processing datasets - Basic uncertainty quantification services that combine heterogeneous ensemble products with comparative observational products (e.g., reanalysis, observational, visualization) - The ability to compute and visualize multiple reanalysis for ease of inter-comparisons - Automated tools to retrieve and prepare data collections for analytic processing