EquiX-A Search and Query Language for XML.

ERIC Educational Resources Information Center

Cohen, Sara; Kanza, Yaron; Kogan, Yakov; Sagiv, Yehoshua; Nutt, Werner; Serebrenik, Alexander

2002-01-01

Describes EquiX, a search language for XML that combines querying with searching to query the data and the meta-data content of Web pages. Topics include search engines; a data model for XML documents; search query syntax; search query semantics; an algorithm for evaluating a query on a document; and indexing EquiX queries. (LRW)

An EHR Prototype Using Structured ISO/EN 13606 Documents to Respond to Identified Clinical Information Needs of Diabetes Specialists: A Controlled Study on Feasibility and Impact

PubMed Central

Huebner-Bloder, Gudrun; Duftschmid, Georg; Kohler, Michael; Rinner, Christoph; Saboor, Samrend; Ammenwerth, Elske

2012-01-01

Cross-institutional longitudinal Electronic Health Records (EHR), as introduced in Austria at the moment, increase the challenge of information overload of healthcare professionals. We developed an innovative cross-institutional EHR query prototype that offers extended query options, including searching for specific information items or sets of information items. The available query options were derived from a systematic analysis of information needs of diabetes specialists during patient encounters. The prototype operates in an IHE-XDS-based environment where ISO/EN 13606-structured documents are available. We conducted a controlled study with seven diabetes specialists to assess the feasibility and impact of this EHR query prototype on efficient retrieving of patient information to answer typical clinical questions. The controlled study showed that the specialists were quicker and more successful (measured in percentage of expected information items found) in finding patient information compared to the standard full-document search options. The participants also appreciated the extended query options. PMID:23304308

Querying Event Sequences by Exact Match or Similarity Search: Design and Empirical Evaluation

PubMed Central

Wongsuphasawat, Krist; Plaisant, Catherine; Taieb-Maimon, Meirav; Shneiderman, Ben

2012-01-01

Specifying event sequence queries is challenging even for skilled computer professionals familiar with SQL. Most graphical user interfaces for database search use an exact match approach, which is often effective, but near misses may also be of interest. We describe a new similarity search interface, in which users specify a query by simply placing events on a blank timeline and retrieve a similarity-ranked list of results. Behind this user interface is a new similarity measure for event sequences which the users can customize by four decision criteria, enabling them to adjust the impact of missing, extra, or swapped events or the impact of time shifts. We describe a use case with Electronic Health Records based on our ongoing collaboration with hospital physicians. A controlled experiment with 18 participants compared exact match and similarity search interfaces. We report on the advantages and disadvantages of each interface and suggest a hybrid interface combining the best of both. PMID:22379286

Fast Multivariate Search on Large Aviation Datasets

NASA Technical Reports Server (NTRS)

Bhaduri, Kanishka; Zhu, Qiang; Oza, Nikunj C.; Srivastava, Ashok N.

2010-01-01

Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual

SPARK: Adapting Keyword Query to Semantic Search

NASA Astrophysics Data System (ADS)

Zhou, Qi; Wang, Chong; Xiong, Miao; Wang, Haofen; Yu, Yong

Semantic search promises to provide more accurate result than present-day keyword search. However, progress with semantic search has been delayed due to the complexity of its query languages. In this paper, we explore a novel approach of adapting keywords to querying the semantic web: the approach automatically translates keyword queries into formal logic queries so that end users can use familiar keywords to perform semantic search. A prototype system named 'SPARK' has been implemented in light of this approach. Given a keyword query, SPARK outputs a ranked list of SPARQL queries as the translation result. The translation in SPARK consists of three major steps: term mapping, query graph construction and query ranking. Specifically, a probabilistic query ranking model is proposed to select the most likely SPARQL query. In the experiment, SPARK achieved an encouraging translation result.

Correlation between National Influenza Surveillance Data and Search Queries from Mobile Devices and Desktops in South Korea

PubMed Central

Seo, Dong-Woo; Sohn, Chang Hwan; Kim, Sung-Hoon; Ryoo, Seung Mok; Lee, Yoon-Seon; Lee, Jae Ho; Kim, Won Young; Lim, Kyoung Soo

2016-01-01

Background Digital surveillance using internet search queries can improve both the sensitivity and timeliness of the detection of a health event, such as an influenza outbreak. While it has recently been estimated that the mobile search volume surpasses the desktop search volume and mobile search patterns differ from desktop search patterns, the previous digital surveillance systems did not distinguish mobile and desktop search queries. The purpose of this study was to compare the performance of mobile and desktop search queries in terms of digital influenza surveillance. Methods and Results The study period was from September 6, 2010 through August 30, 2014, which consisted of four epidemiological years. Influenza-like illness (ILI) and virologic surveillance data from the Korea Centers for Disease Control and Prevention were used. A total of 210 combined queries from our previous survey work were used for this study. Mobile and desktop weekly search data were extracted from Naver, which is the largest search engine in Korea. Spearman’s correlation analysis was used to examine the correlation of the mobile and desktop data with ILI and virologic data in Korea. We also performed lag correlation analysis. We observed that the influenza surveillance performance of mobile search queries matched or exceeded that of desktop search queries over time. The mean correlation coefficients of mobile search queries and the number of queries with an r-value of ≥ 0.7 equaled or became greater than those of desktop searches over the four epidemiological years. A lag correlation analysis of up to two weeks showed similar trends. Conclusion Our study shows that mobile search queries for influenza surveillance have equaled or even become greater than desktop search queries over time. In the future development of influenza surveillance using search queries, the recognition of changing trend of mobile search data could be necessary. PMID:27391028

Correlation between National Influenza Surveillance Data and Search Queries from Mobile Devices and Desktops in South Korea.

PubMed

Shin, Soo-Yong; Kim, Taerim; Seo, Dong-Woo; Sohn, Chang Hwan; Kim, Sung-Hoon; Ryoo, Seung Mok; Lee, Yoon-Seon; Lee, Jae Ho; Kim, Won Young; Lim, Kyoung Soo

2016-01-01

Digital surveillance using internet search queries can improve both the sensitivity and timeliness of the detection of a health event, such as an influenza outbreak. While it has recently been estimated that the mobile search volume surpasses the desktop search volume and mobile search patterns differ from desktop search patterns, the previous digital surveillance systems did not distinguish mobile and desktop search queries. The purpose of this study was to compare the performance of mobile and desktop search queries in terms of digital influenza surveillance. The study period was from September 6, 2010 through August 30, 2014, which consisted of four epidemiological years. Influenza-like illness (ILI) and virologic surveillance data from the Korea Centers for Disease Control and Prevention were used. A total of 210 combined queries from our previous survey work were used for this study. Mobile and desktop weekly search data were extracted from Naver, which is the largest search engine in Korea. Spearman's correlation analysis was used to examine the correlation of the mobile and desktop data with ILI and virologic data in Korea. We also performed lag correlation analysis. We observed that the influenza surveillance performance of mobile search queries matched or exceeded that of desktop search queries over time. The mean correlation coefficients of mobile search queries and the number of queries with an r-value of ≥ 0.7 equaled or became greater than those of desktop searches over the four epidemiological years. A lag correlation analysis of up to two weeks showed similar trends. Our study shows that mobile search queries for influenza surveillance have equaled or even become greater than desktop search queries over time. In the future development of influenza surveillance using search queries, the recognition of changing trend of mobile search data could be necessary.

Comparative Analysis of Online Health Queries Originating From Personal Computers and Smart Devices on a Consumer Health Information Portal

PubMed Central

Jadhav, Ashutosh; Andrews, Donna; Fiksdal, Alexander; Kumbamu, Ashok; McCormick, Jennifer B; Misitano, Andrew; Nelsen, Laurie; Ryu, Euijung; Sheth, Amit; Wu, Stephen

2014-01-01

Background The number of people using the Internet and mobile/smart devices for health information seeking is increasing rapidly. Although the user experience for online health information seeking varies with the device used, for example, smart devices (SDs) like smartphones/tablets versus personal computers (PCs) like desktops/laptops, very few studies have investigated how online health information seeking behavior (OHISB) may differ by device. Objective The objective of this study is to examine differences in OHISB between PCs and SDs through a comparative analysis of large-scale health search queries submitted through Web search engines from both types of devices. Methods Using the Web analytics tool, IBM NetInsight OnDemand, and based on the type of devices used (PCs or SDs), we obtained the most frequent health search queries between June 2011 and May 2013 that were submitted on Web search engines and directed users to the Mayo Clinic’s consumer health information website. We performed analyses on “Queries with considering repetition counts (QwR)” and “Queries without considering repetition counts (QwoR)”. The dataset contains (1) 2.74 million and 3.94 million QwoR, respectively for PCs and SDs, and (2) more than 100 million QwR for both PCs and SDs. We analyzed structural properties of the queries (length of the search queries, usage of query operators and special characters in health queries), types of search queries (keyword-based, wh-questions, yes/no questions), categorization of the queries based on health categories and information mentioned in the queries (gender, age-groups, temporal references), misspellings in the health queries, and the linguistic structure of the health queries. Results Query strings used for health information searching via PCs and SDs differ by almost 50%. The most searched health categories are “Symptoms” (1 in 3 search queries), “Causes”, and “Treatments & Drugs”. The distribution of search queries for different health categories differs with the device used for the search. Health queries tend to be longer and more specific than general search queries. Health queries from SDs are longer and have slightly fewer spelling mistakes than those from PCs. Users specify words related to women and children more often than that of men and any other age group. Most of the health queries are formulated using keywords; the second-most common are wh- and yes/no questions. Users ask more health questions using SDs than PCs. Almost all health queries have at least one noun and health queries from SDs are more descriptive than those from PCs. Conclusions This study is a large-scale comparative analysis of health search queries to understand the effects of device type (PCs vs SDs) used on OHISB. The study indicates that the device used for online health information search plays an important role in shaping how health information searches by consumers and patients are executed. PMID:25000537

Comparative analysis of online health queries originating from personal computers and smart devices on a consumer health information portal.

PubMed

Jadhav, Ashutosh; Andrews, Donna; Fiksdal, Alexander; Kumbamu, Ashok; McCormick, Jennifer B; Misitano, Andrew; Nelsen, Laurie; Ryu, Euijung; Sheth, Amit; Wu, Stephen; Pathak, Jyotishman

2014-07-04

The number of people using the Internet and mobile/smart devices for health information seeking is increasing rapidly. Although the user experience for online health information seeking varies with the device used, for example, smart devices (SDs) like smartphones/tablets versus personal computers (PCs) like desktops/laptops, very few studies have investigated how online health information seeking behavior (OHISB) may differ by device. The objective of this study is to examine differences in OHISB between PCs and SDs through a comparative analysis of large-scale health search queries submitted through Web search engines from both types of devices. Using the Web analytics tool, IBM NetInsight OnDemand, and based on the type of devices used (PCs or SDs), we obtained the most frequent health search queries between June 2011 and May 2013 that were submitted on Web search engines and directed users to the Mayo Clinic's consumer health information website. We performed analyses on "Queries with considering repetition counts (QwR)" and "Queries without considering repetition counts (QwoR)". The dataset contains (1) 2.74 million and 3.94 million QwoR, respectively for PCs and SDs, and (2) more than 100 million QwR for both PCs and SDs. We analyzed structural properties of the queries (length of the search queries, usage of query operators and special characters in health queries), types of search queries (keyword-based, wh-questions, yes/no questions), categorization of the queries based on health categories and information mentioned in the queries (gender, age-groups, temporal references), misspellings in the health queries, and the linguistic structure of the health queries. Query strings used for health information searching via PCs and SDs differ by almost 50%. The most searched health categories are "Symptoms" (1 in 3 search queries), "Causes", and "Treatments & Drugs". The distribution of search queries for different health categories differs with the device used for the search. Health queries tend to be longer and more specific than general search queries. Health queries from SDs are longer and have slightly fewer spelling mistakes than those from PCs. Users specify words related to women and children more often than that of men and any other age group. Most of the health queries are formulated using keywords; the second-most common are wh- and yes/no questions. Users ask more health questions using SDs than PCs. Almost all health queries have at least one noun and health queries from SDs are more descriptive than those from PCs. This study is a large-scale comparative analysis of health search queries to understand the effects of device type (PCs vs. SDs) used on OHISB. The study indicates that the device used for online health information search plays an important role in shaping how health information searches by consumers and patients are executed.

NLM at TREC 2012 Medical Records Track

DTIC Science & Technology

2012-11-01

automatic runs are not significantly above the medians. As in 2011, we conclude that the existing search engines are mature enough to support cohort selection tasks, and the quality of the queries could be

Searching PubMed for studies on bacteremia, bloodstream infection, septicemia, or whatever the best term is: a note of caution.

PubMed

Søgaard, Mette; Andersen, Jens P; Schønheyder, Henrik C

2012-04-01

There is inconsistency in the terminology used to describe bacteremia. To demonstrate the impact on information retrieval, we compared the yield of articles from PubMed MEDLINE using the terms "bacteremia," "bloodstream infection," and "septicemia." We searched for articles published between 1966 and 2009, and depicted the relationships among queries graphically. To examine the content of the retrieved articles, we extracted all Medical Subject Headings (MeSH) terms and compared topic similarity using a cosine measure. The recovered articles differed greatly by term, and only 53 articles were captured by all terms. Of the articles retrieved by the "bacteremia" query, 21,438 (84.1%) were not captured when searching for "bloodstream infection" or "septicemia." Likewise, only 2,243 of the 11,796 articles recovered by free-text query for "bloodstream infection" were retrieved by the "bacteremia" query (19%). Entering "bloodstream infection" as a phrase, 46.1% of the records overlapped with the "bacteremia" query. Similarity measures ranged from 0.52 to 0.78 and were lowest for "bloodstream infection" as a phrase compared with "septicemia." Inconsistent terminology has a major impact on the yield of queries. Agreement on terminology should be sought and promoted by scientific journals. An immediate solution is to add "bloodstream infection" as entry term for bacteremia in the MeSH vocabulary. Copyright © 2012 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Mosby, Inc. All rights reserved.

Architecture for knowledge-based and federated search of online clinical evidence.

PubMed

Coiera, Enrico; Walther, Martin; Nguyen, Ken; Lovell, Nigel H

2005-10-24

It is increasingly difficult for clinicians to keep up-to-date with the rapidly growing biomedical literature. Online evidence retrieval methods are now seen as a core tool to support evidence-based health practice. However, standard search engine technology is not designed to manage the many different types of evidence sources that are available or to handle the very different information needs of various clinical groups, who often work in widely different settings. The objectives of this paper are (1) to describe the design considerations and system architecture of a wrapper-mediator approach to federate search system design, including the use of knowledge-based, meta-search filters, and (2) to analyze the implications of system design choices on performance measurements. A trial was performed to evaluate the technical performance of a federated evidence retrieval system, which provided access to eight distinct online resources, including e-journals, PubMed, and electronic guidelines. The Quick Clinical system architecture utilized a universal query language to reformulate queries internally and utilized meta-search filters to optimize search strategies across resources. We recruited 227 family physicians from across Australia who used the system to retrieve evidence in a routine clinical setting over a 4-week period. The total search time for a query was recorded, along with the duration of individual queries sent to different online resources. Clinicians performed 1662 searches over the trial. The average search duration was 4.9 +/- 3.2 s (N = 1662 searches). Mean search duration to the individual sources was between 0.05 s and 4.55 s. Average system time (ie, system overhead) was 0.12 s. The relatively small system overhead compared to the average time it takes to perform a search for an individual source shows that the system achieves a good trade-off between performance and reliability. Furthermore, despite the additional effort required to incorporate the capabilities of each individual source (to improve the quality of search results), system maintenance requires only a small additional overhead.

28 CFR 25.7 - Querying records in the system.

Code of Federal Regulations, 2011 CFR

2011-07-01

...) Name; (2) Sex; (3) Race; (4) Complete date of birth; and (5) State of residence. (b) A unique numeric identifier may also be provided to search for additional records based on exact matches by the numeric identifier. Examples of unique numeric identifiers for purposes of this system are: Social Security number...

28 CFR 25.7 - Querying records in the system.

Code of Federal Regulations, 2014 CFR

2014-07-01

...) Name; (2) Sex; (3) Race; (4) Complete date of birth; and (5) State of residence. (b) A unique numeric identifier may also be provided to search for additional records based on exact matches by the numeric identifier. Examples of unique numeric identifiers for purposes of this system are: Social Security number...

28 CFR 25.7 - Querying records in the system.

Code of Federal Regulations, 2013 CFR

2013-07-01

...) Name; (2) Sex; (3) Race; (4) Complete date of birth; and (5) State of residence. (b) A unique numeric identifier may also be provided to search for additional records based on exact matches by the numeric identifier. Examples of unique numeric identifiers for purposes of this system are: Social Security number...

28 CFR 25.7 - Querying records in the system.

Code of Federal Regulations, 2012 CFR

2012-07-01

...) Name; (2) Sex; (3) Race; (4) Complete date of birth; and (5) State of residence. (b) A unique numeric identifier may also be provided to search for additional records based on exact matches by the numeric identifier. Examples of unique numeric identifiers for purposes of this system are: Social Security number...

Privacy-preserving search for chemical compound databases.

PubMed

Shimizu, Kana; Nuida, Koji; Arai, Hiromi; Mitsunari, Shigeo; Attrapadung, Nuttapong; Hamada, Michiaki; Tsuda, Koji; Hirokawa, Takatsugu; Sakuma, Jun; Hanaoka, Goichiro; Asai, Kiyoshi

2015-01-01

Searching for similar compounds in a database is the most important process for in-silico drug screening. Since a query compound is an important starting point for the new drug, a query holder, who is afraid of the query being monitored by the database server, usually downloads all the records in the database and uses them in a closed network. However, a serious dilemma arises when the database holder also wants to output no information except for the search results, and such a dilemma prevents the use of many important data resources. In order to overcome this dilemma, we developed a novel cryptographic protocol that enables database searching while keeping both the query holder's privacy and database holder's privacy. Generally, the application of cryptographic techniques to practical problems is difficult because versatile techniques are computationally expensive while computationally inexpensive techniques can perform only trivial computation tasks. In this study, our protocol is successfully built only from an additive-homomorphic cryptosystem, which allows only addition performed on encrypted values but is computationally efficient compared with versatile techniques such as general purpose multi-party computation. In an experiment searching ChEMBL, which consists of more than 1,200,000 compounds, the proposed method was 36,900 times faster in CPU time and 12,000 times as efficient in communication size compared with general purpose multi-party computation. We proposed a novel privacy-preserving protocol for searching chemical compound databases. The proposed method, easily scaling for large-scale databases, may help to accelerate drug discovery research by making full use of unused but valuable data that includes sensitive information.

Privacy-preserving search for chemical compound databases

PubMed Central

2015-01-01

Background Searching for similar compounds in a database is the most important process for in-silico drug screening. Since a query compound is an important starting point for the new drug, a query holder, who is afraid of the query being monitored by the database server, usually downloads all the records in the database and uses them in a closed network. However, a serious dilemma arises when the database holder also wants to output no information except for the search results, and such a dilemma prevents the use of many important data resources. Results In order to overcome this dilemma, we developed a novel cryptographic protocol that enables database searching while keeping both the query holder's privacy and database holder's privacy. Generally, the application of cryptographic techniques to practical problems is difficult because versatile techniques are computationally expensive while computationally inexpensive techniques can perform only trivial computation tasks. In this study, our protocol is successfully built only from an additive-homomorphic cryptosystem, which allows only addition performed on encrypted values but is computationally efficient compared with versatile techniques such as general purpose multi-party computation. In an experiment searching ChEMBL, which consists of more than 1,200,000 compounds, the proposed method was 36,900 times faster in CPU time and 12,000 times as efficient in communication size compared with general purpose multi-party computation. Conclusion We proposed a novel privacy-preserving protocol for searching chemical compound databases. The proposed method, easily scaling for large-scale databases, may help to accelerate drug discovery research by making full use of unused but valuable data that includes sensitive information. PMID:26678650

GEOmetadb: powerful alternative search engine for the Gene Expression Omnibus

PubMed Central

Zhu, Yuelin; Davis, Sean; Stephens, Robert; Meltzer, Paul S.; Chen, Yidong

2008-01-01

The NCBI Gene Expression Omnibus (GEO) represents the largest public repository of microarray data. However, finding data in GEO can be challenging. We have developed GEOmetadb in an attempt to make querying the GEO metadata both easier and more powerful. All GEO metadata records as well as the relationships between them are parsed and stored in a local MySQL database. A powerful, flexible web search interface with several convenient utilities provides query capabilities not available via NCBI tools. In addition, a Bioconductor package, GEOmetadb that utilizes a SQLite export of the entire GEOmetadb database is also available, rendering the entire GEO database accessible with full power of SQL-based queries from within R. Availability: The web interface and SQLite databases available at http://gbnci.abcc.ncifcrf.gov/geo/. The Bioconductor package is available via the Bioconductor project. The corresponding MATLAB implementation is also available at the same website. Contact: yidong@mail.nih.gov PMID:18842599

Environmental Dataset Gateway (EDG) Search Widget

EPA Pesticide Factsheets

Use the Environmental Dataset Gateway (EDG) to find and access EPA's environmental resources. Many options are available for easily reusing EDG content in other other applications. This allows individuals to provide direct access to EPA's metadata outside the EDG interface. The EDG Search Widget makes it possible to search the EDG from another web page or application. The search widget can be included on your website by simply inserting one or two lines of code. Users can type a search term or lucene search query in the search field and retrieve a pop-up list of records that match that search.

A study of medical and health queries to web search engines.

PubMed

Spink, Amanda; Yang, Yin; Jansen, Jim; Nykanen, Pirrko; Lorence, Daniel P; Ozmutlu, Seda; Ozmutlu, H Cenk

2004-03-01

This paper reports findings from an analysis of medical or health queries to different web search engines. We report results: (i). comparing samples of 10000 web queries taken randomly from 1.2 million query logs from the AlltheWeb.com and Excite.com commercial web search engines in 2001 for medical or health queries, (ii). comparing the 2001 findings from Excite and AlltheWeb.com users with results from a previous analysis of medical and health related queries from the Excite Web search engine for 1997 and 1999, and (iii). medical or health advice-seeking queries beginning with the word 'should'. Findings suggest: (i). a small percentage of web queries are medical or health related, (ii). the top five categories of medical or health queries were: general health, weight issues, reproductive health and puberty, pregnancy/obstetrics, and human relationships, and (iii). over time, the medical and health queries may have declined as a proportion of all web queries, as the use of specialized medical/health websites and e-commerce-related queries has increased. Findings provide insights into medical and health-related web querying and suggests some implications for the use of the general web search engines when seeking medical/health information.

A Directory of Sources of Information and Data Bases on Education and Training.

DTIC Science & Technology

1980-09-01

ACADO07 National Opinion Research Center (NORC) ... ............. ... ACADOO8 U of California Union Catalog Supp. (1963-1967...Records (RSR) ...... .................. ... ARMYO30 Union Central Registry System (UCRSYS) .... .............. ... ARMY032 Training Control Card Report...research. Your query directs a computer search of the Compre- hensive Dissertation Database. The search produces a list of all titles matching your

Folksonomical P2P File Sharing Networks Using Vectorized KANSEI Information as Search Tags

NASA Astrophysics Data System (ADS)

Ohnishi, Kei; Yoshida, Kaori; Oie, Yuji

We present the concept of folksonomical peer-to-peer (P2P) file sharing networks that allow participants (peers) to freely assign structured search tags to files. These networks are similar to folksonomies in the present Web from the point of view that users assign search tags to information distributed over a network. As a concrete example, we consider an unstructured P2P network using vectorized Kansei (human sensitivity) information as structured search tags for file search. Vectorized Kansei information as search tags indicates what participants feel about their files and is assigned by the participant to each of their files. A search query also has the same form of search tags and indicates what participants want to feel about files that they will eventually obtain. A method that enables file search using vectorized Kansei information is the Kansei query-forwarding method, which probabilistically propagates a search query to peers that are likely to hold more files having search tags that are similar to the query. The similarity between the search query and the search tags is measured in terms of their dot product. The simulation experiments examine if the Kansei query-forwarding method can provide equal search performance for all peers in a network in which only the Kansei information and the tendency with respect to file collection are different among all of the peers. The simulation results show that the Kansei query forwarding method and a random-walk-based query forwarding method, for comparison, work effectively in different situations and are complementary. Furthermore, the Kansei query forwarding method is shown, through simulations, to be superior to or equal to the random-walk based one in terms of search speed.

Searching the Web: The Public and Their Queries.

ERIC Educational Resources Information Center

Spink, Amanda; Wolfram, Dietmar; Jansen, Major B. J.; Saracevic, Tefko

2001-01-01

Reports findings from a study of searching behavior by over 200,000 users of the Excite search engine. Analysis of over one million queries revealed most people use few search terms, few modified queries, view few Web pages, and rarely use advanced search features. Concludes that Web searching by the public differs significantly from searching of…

Mercury Toolset for Spatiotemporal Metadata

NASA Technical Reports Server (NTRS)

Wilson, Bruce E.; Palanisamy, Giri; Devarakonda, Ranjeet; Rhyne, B. Timothy; Lindsley, Chris; Green, James

2010-01-01

Mercury (http://mercury.ornl.gov) is a set of tools for federated harvesting, searching, and retrieving metadata, particularly spatiotemporal metadata. Version 3.0 of the Mercury toolset provides orders of magnitude improvements in search speed, support for additional metadata formats, integration with Google Maps for spatial queries, facetted type search, support for RSS (Really Simple Syndication) delivery of search results, and enhanced customization to meet the needs of the multiple projects that use Mercury. It provides a single portal to very quickly search for data and information contained in disparate data management systems, each of which may use different metadata formats. Mercury harvests metadata and key data from contributing project servers distributed around the world and builds a centralized index. The search interfaces then allow the users to perform a variety of fielded, spatial, and temporal searches across these metadata sources. This centralized repository of metadata with distributed data sources provides extremely fast search results to the user, while allowing data providers to advertise the availability of their data and maintain complete control and ownership of that data. Mercury periodically (typically daily) harvests metadata sources through a collection of interfaces and re-indexes these metadata to provide extremely rapid search capabilities, even over collections with tens of millions of metadata records. A number of both graphical and application interfaces have been constructed within Mercury, to enable both human users and other computer programs to perform queries. Mercury was also designed to support multiple different projects, so that the particular fields that can be queried and used with search filters are easy to configure for each different project.

Mercury Toolset for Spatiotemporal Metadata

NASA Astrophysics Data System (ADS)

Devarakonda, Ranjeet; Palanisamy, Giri; Green, James; Wilson, Bruce; Rhyne, B. Timothy; Lindsley, Chris

2010-06-01

Mercury (http://mercury.ornl.gov) is a set of tools for federated harvesting, searching, and retrieving metadata, particularly spatiotemporal metadata. Version 3.0 of the Mercury toolset provides orders of magnitude improvements in search speed, support for additional metadata formats, integration with Google Maps for spatial queries, facetted type search, support for RSS (Really Simple Syndication) delivery of search results, and enhanced customization to meet the needs of the multiple projects that use Mercury. It provides a single portal to very quickly search for data and information contained in disparate data management systems, each of which may use different metadata formats. Mercury harvests metadata and key data from contributing project servers distributed around the world and builds a centralized index. The search interfaces then allow the users to perform a variety of fielded, spatial, and temporal searches across these metadata sources. This centralized repository of metadata with distributed data sources provides extremely fast search results to the user, while allowing data providers to advertise the availability of their data and maintain complete control and ownership of that data. Mercury periodically (typically daily)harvests metadata sources through a collection of interfaces and re-indexes these metadata to provide extremely rapid search capabilities, even over collections with tens of millions of metadata records. A number of both graphical and application interfaces have been constructed within Mercury, to enable both human users and other computer programs to perform queries. Mercury was also designed to support multiple different projects, so that the particular fields that can be queried and used with search filters are easy to configure for each different project.

LAILAPS-QSM: A RESTful API and JAVA library for semantic query suggestions.

PubMed

Chen, Jinbo; Scholz, Uwe; Zhou, Ruonan; Lange, Matthias

2018-03-01

In order to access and filter content of life-science databases, full text search is a widely applied query interface. But its high flexibility and intuitiveness is paid for with potentially imprecise and incomplete query results. To reduce this drawback, query assistance systems suggest those combinations of keywords with the highest potential to match most of the relevant data records. Widespread approaches are syntactic query corrections that avoid misspelling and support expansion of words by suffixes and prefixes. Synonym expansion approaches apply thesauri, ontologies, and query logs. All need laborious curation and maintenance. Furthermore, access to query logs is in general restricted. Approaches that infer related queries by their query profile like research field, geographic location, co-authorship, affiliation etc. require user's registration and its public accessibility that contradict privacy concerns. To overcome these drawbacks, we implemented LAILAPS-QSM, a machine learning approach that reconstruct possible linguistic contexts of a given keyword query. The context is referred from the text records that are stored in the databases that are going to be queried or extracted for a general purpose query suggestion from PubMed abstracts and UniProt data. The supplied tool suite enables the pre-processing of these text records and the further computation of customized distributed word vectors. The latter are used to suggest alternative keyword queries. An evaluated of the query suggestion quality was done for plant science use cases. Locally present experts enable a cost-efficient quality assessment in the categories trait, biological entity, taxonomy, affiliation, and metabolic function which has been performed using ontology term similarities. LAILAPS-QSM mean information content similarity for 15 representative queries is 0.70, whereas 34% have a score above 0.80. In comparison, the information content similarity for human expert made query suggestions is 0.90. The software is either available as tool set to build and train dedicated query suggestion services or as already trained general purpose RESTful web service. The service uses open interfaces to be seamless embeddable into database frontends. The JAVA implementation uses highly optimized data structures and streamlined code to provide fast and scalable response for web service calls. The source code of LAILAPS-QSM is available under GNU General Public License version 2 in Bitbucket GIT repository: https://bitbucket.org/ipk_bit_team/bioescorte-suggestion.

PlantGI: a database for searching gene indices in agricultural plants developed at NIAB, Korea

PubMed Central

Kim, Chang Kug; Choi, Ji Weon; Park, DongSuk; Kang, Man Jung; Seol, Young-Joo; Hyun, Do Yoon; Hahn, Jang Ho

2008-01-01

The Plant Gene Index (PlantGI) database is developed as a web-based search system with search capabilities for keywords to provide information on gene indices specifically for agricultural plants. The database contains specific Gene Index information for ten agricultural species, namely, rice, Chinese cabbage, wheat, maize, soybean, barley, mushroom, Arabidopsis, hot pepper and tomato. PlantGI differs from other Gene Index databases in being specific to agricultural plant species and thus complements services from similar other developments. The database includes options for interactive mining of EST CONTIGS and assembled EST data for user specific keyword queries. The current version of PlantGI contains a total of 34,000 EST CONTIGS data for rice (8488 records), wheat (8560 records), maize (4570 records), soybean (3726 records), barley (3417 records), Chinese cabbage (3602 records), tomato (1236 records), hot pepper (998 records), mushroom (130 records) and Arabidopsis (8 records). Availability The database is available for free at http://www.niab.go.kr/nabic/. PMID:18685722

Estimating Influenza Outbreaks Using Both Search Engine Query Data and Social Media Data in South Korea.

PubMed

Woo, Hyekyung; Cho, Youngtae; Shim, Eunyoung; Lee, Jong-Koo; Lee, Chang-Gun; Kim, Seong Hwan

2016-07-04

As suggested as early as in 2006, logs of queries submitted to search engines seeking information could be a source for detection of emerging influenza epidemics if changes in the volume of search queries are monitored (infodemiology). However, selecting queries that are most likely to be associated with influenza epidemics is a particular challenge when it comes to generating better predictions. In this study, we describe a methodological extension for detecting influenza outbreaks using search query data; we provide a new approach for query selection through the exploration of contextual information gleaned from social media data. Additionally, we evaluate whether it is possible to use these queries for monitoring and predicting influenza epidemics in South Korea. Our study was based on freely available weekly influenza incidence data and query data originating from the search engine on the Korean website Daum between April 3, 2011 and April 5, 2014. To select queries related to influenza epidemics, several approaches were applied: (1) exploring influenza-related words in social media data, (2) identifying the chief concerns related to influenza, and (3) using Web query recommendations. Optimal feature selection by least absolute shrinkage and selection operator (Lasso) and support vector machine for regression (SVR) were used to construct a model predicting influenza epidemics. In total, 146 queries related to influenza were generated through our initial query selection approach. A considerable proportion of optimal features for final models were derived from queries with reference to the social media data. The SVR model performed well: the prediction values were highly correlated with the recent observed influenza-like illness (r=.956; P<.001) and virological incidence rate (r=.963; P<.001). These results demonstrate the feasibility of using search queries to enhance influenza surveillance in South Korea. In addition, an approach for query selection using social media data seems ideal for supporting influenza surveillance based on search query data.

Estimating Influenza Outbreaks Using Both Search Engine Query Data and Social Media Data in South Korea

PubMed Central

Woo, Hyekyung; Shim, Eunyoung; Lee, Jong-Koo; Lee, Chang-Gun; Kim, Seong Hwan

2016-01-01

Background As suggested as early as in 2006, logs of queries submitted to search engines seeking information could be a source for detection of emerging influenza epidemics if changes in the volume of search queries are monitored (infodemiology). However, selecting queries that are most likely to be associated with influenza epidemics is a particular challenge when it comes to generating better predictions. Objective In this study, we describe a methodological extension for detecting influenza outbreaks using search query data; we provide a new approach for query selection through the exploration of contextual information gleaned from social media data. Additionally, we evaluate whether it is possible to use these queries for monitoring and predicting influenza epidemics in South Korea. Methods Our study was based on freely available weekly influenza incidence data and query data originating from the search engine on the Korean website Daum between April 3, 2011 and April 5, 2014. To select queries related to influenza epidemics, several approaches were applied: (1) exploring influenza-related words in social media data, (2) identifying the chief concerns related to influenza, and (3) using Web query recommendations. Optimal feature selection by least absolute shrinkage and selection operator (Lasso) and support vector machine for regression (SVR) were used to construct a model predicting influenza epidemics. Results In total, 146 queries related to influenza were generated through our initial query selection approach. A considerable proportion of optimal features for final models were derived from queries with reference to the social media data. The SVR model performed well: the prediction values were highly correlated with the recent observed influenza-like illness (r=.956; P<.001) and virological incidence rate (r=.963; P<.001). Conclusions These results demonstrate the feasibility of using search queries to enhance influenza surveillance in South Korea. In addition, an approach for query selection using social media data seems ideal for supporting influenza surveillance based on search query data. PMID:27377323

A secure and efficiently searchable health information architecture.

PubMed

Yasnoff, William A

2016-06-01

Patient-centric repositories of health records are an important component of health information infrastructure. However, patient information in a single repository is potentially vulnerable to loss of the entire dataset from a single unauthorized intrusion. A new health record storage architecture, the personal grid, eliminates this risk by separately storing and encrypting each person's record. The tradeoff for this improved security is that a personal grid repository must be sequentially searched since each record must be individually accessed and decrypted. To allow reasonable search times for large numbers of records, parallel processing with hundreds (or even thousands) of on-demand virtual servers (now available in cloud computing environments) is used. Estimated search times for a 10 million record personal grid using 500 servers vary from 7 to 33min depending on the complexity of the query. Since extremely rapid searching is not a critical requirement of health information infrastructure, the personal grid may provide a practical and useful alternative architecture that eliminates the large-scale security vulnerabilities of traditional databases by sacrificing unnecessary searching speed. Copyright © 2016 Elsevier Inc. All rights reserved.

Personalized query suggestion based on user behavior

NASA Astrophysics Data System (ADS)

Chen, Wanyu; Hao, Zepeng; Shao, Taihua; Chen, Honghui

Query suggestions help users refine their queries after they input an initial query. Previous work mainly concentrated on similarity-based and context-based query suggestion approaches. However, models that focus on adapting to a specific user (personalization) can help to improve the probability of the user being satisfied. In this paper, we propose a personalized query suggestion model based on users’ search behavior (UB model), where we inject relevance between queries and users’ search behavior into a basic probabilistic model. For the relevance between queries, we consider their semantical similarity and co-occurrence which indicates the behavior information from other users in web search. Regarding the current user’s preference to a query, we combine the user’s short-term and long-term search behavior in a linear fashion and deal with the data sparse problem with Bayesian probabilistic matrix factorization (BPMF). In particular, we also investigate the impact of different personalization strategies (the combination of the user’s short-term and long-term search behavior) on the performance of query suggestion reranking. We quantify the improvement of our proposed UB model against a state-of-the-art baseline using the public AOL query logs and show that it beats the baseline in terms of metrics used in query suggestion reranking. The experimental results show that: (i) for personalized ranking, users’ behavioral information helps to improve query suggestion effectiveness; and (ii) given a query, merging information inferred from the short-term and long-term search behavior of a particular user can result in a better performance than both plain approaches.

Architecture for Knowledge-Based and Federated Search of Online Clinical Evidence

PubMed Central

Walther, Martin; Nguyen, Ken; Lovell, Nigel H

2005-01-01

Background It is increasingly difficult for clinicians to keep up-to-date with the rapidly growing biomedical literature. Online evidence retrieval methods are now seen as a core tool to support evidence-based health practice. However, standard search engine technology is not designed to manage the many different types of evidence sources that are available or to handle the very different information needs of various clinical groups, who often work in widely different settings. Objectives The objectives of this paper are (1) to describe the design considerations and system architecture of a wrapper-mediator approach to federate search system design, including the use of knowledge-based, meta-search filters, and (2) to analyze the implications of system design choices on performance measurements. Methods A trial was performed to evaluate the technical performance of a federated evidence retrieval system, which provided access to eight distinct online resources, including e-journals, PubMed, and electronic guidelines. The Quick Clinical system architecture utilized a universal query language to reformulate queries internally and utilized meta-search filters to optimize search strategies across resources. We recruited 227 family physicians from across Australia who used the system to retrieve evidence in a routine clinical setting over a 4-week period. The total search time for a query was recorded, along with the duration of individual queries sent to different online resources. Results Clinicians performed 1662 searches over the trial. The average search duration was 4.9 ± 3.2 s (N = 1662 searches). Mean search duration to the individual sources was between 0.05 s and 4.55 s. Average system time (ie, system overhead) was 0.12 s. Conclusions The relatively small system overhead compared to the average time it takes to perform a search for an individual source shows that the system achieves a good trade-off between performance and reliability. Furthermore, despite the additional effort required to incorporate the capabilities of each individual source (to improve the quality of search results), system maintenance requires only a small additional overhead. PMID:16403716

Categorical and Specificity Differences between User-Supplied Tags and Search Query Terms for Images. An Analysis of "Flickr" Tags and Web Image Search Queries

ERIC Educational Resources Information Center

Chung, EunKyung; Yoon, JungWon

2009-01-01

Introduction: The purpose of this study is to compare characteristics and features of user supplied tags and search query terms for images on the "Flickr" Website in terms of categories of pictorial meanings and level of term specificity. Method: This study focuses on comparisons between tags and search queries using Shatford's categorization…

Targeted exploration and analysis of large cross-platform human transcriptomic compendia

PubMed Central

Zhu, Qian; Wong, Aaron K; Krishnan, Arjun; Aure, Miriam R; Tadych, Alicja; Zhang, Ran; Corney, David C; Greene, Casey S; Bongo, Lars A; Kristensen, Vessela N; Charikar, Moses; Li, Kai; Troyanskaya, Olga G.

2016-01-01

We present SEEK (http://seek.princeton.edu), a query-based search engine across very large transcriptomic data collections, including thousands of human data sets from almost 50 microarray and next-generation sequencing platforms. SEEK uses a novel query-level cross-validation-based algorithm to automatically prioritize data sets relevant to the query and a robust search approach to identify query-coregulated genes, pathways, and processes. SEEK provides cross-platform handling, multi-gene query search, iterative metadata-based search refinement, and extensive visualization-based analysis options. PMID:25581801

Ontological Approach to Military Knowledge Modeling and Management

DTIC Science & Technology

2004-03-01

federated search mechanism has to reformulate user queries (expressed using the ontology) in the query languages of the different sources (e.g. SQL...ontologies as a common terminology – Unified query to perform federated search • Query processing – Ontology mapping to sources reformulate queries

Efficient strategies to find diagnostic test accuracy studies in kidney journals.

PubMed

Rogerson, Thomas E; Ladhani, Maleeka; Mitchell, Ruth; Craig, Jonathan C; Webster, Angela C

2015-08-01

Nephrologists looking for quick answers to diagnostic clinical questions in MEDLINE can use a range of published search strategies or Clinical Query limits to improve the precision of their searches. We aimed to evaluate existing search strategies for finding diagnostic test accuracy studies in nephrology journals. We assessed the accuracy of 14 search strategies for retrieving diagnostic test accuracy studies from three nephrology journals indexed in MEDLINE. Two investigators hand searched the same journals to create a reference set of diagnostic test accuracy studies to compare search strategy results against. We identified 103 diagnostic test accuracy studies, accounting for 2.1% of all studies published. The most specific search strategy was the Narrow Clinical Queries limit (sensitivity: 0.20, 95% CI 0.13-0.29; specificity: 0.99, 95% CI 0.99-0.99). Using the Narrow Clinical Queries limit, a searcher would need to screen three (95% CI 2-6) articles to find one diagnostic study. The most sensitive search strategy was van der Weijden 1999 Extended (sensitivity: 0.95; 95% CI 0.89-0.98; specificity 0.55, 95% CI 0.53-0.56) but required a searcher to screen 24 (95% CI 23-26) articles to find one diagnostic study. Bachmann 2002 was the best balanced search strategy, which was sensitive (0.88, 95% CI 0.81-0.94), but also specific (0.74, 95% CI 0.73-0.75), with a number needed to screen of 15 (95% CI 14-17). Diagnostic studies are infrequently published in nephrology journals. The addition of a strategy for diagnostic studies to a subject search strategy in MEDLINE may reduce the records needed to screen while preserving adequate search sensitivity for routine clinical use. © 2015 Asian Pacific Society of Nephrology.

Analysis of Online Information Searching for Cardiovascular Diseases on a Consumer Health Information Portal

PubMed Central

Jadhav, Ashutosh; Sheth, Amit; Pathak, Jyotishman

2014-01-01

Since the early 2000’s, Internet usage for health information searching has increased significantly. Studying search queries can help us to understand users “information need” and how do they formulate search queries (“expression of information need”). Although cardiovascular diseases (CVD) affect a large percentage of the population, few studies have investigated how and what users search for CVD. We address this knowledge gap in the community by analyzing a large corpus of 10 million CVD related search queries from MayoClinic.com. Using UMLS MetaMap and UMLS semantic types/concepts, we developed a rule-based approach to categorize the queries into 14 health categories. We analyzed structural properties, types (keyword-based/Wh-questions/Yes-No questions) and linguistic structure of the queries. Our results show that the most searched health categories are ‘Diseases/Conditions’, ‘Vital-Sings’, ‘Symptoms’ and ‘Living-with’. CVD queries are longer and are predominantly keyword-based. This study extends our knowledge about online health information searching and provides useful insights for Web search engines and health websites. PMID:25954380

Locality in Search Engine Queries and Its Implications for Caching

DTIC Science & Technology

2001-05-01

in the question of whether caching might be effective for search engines as well. They study two real search engine traces by examining query...locality and its implications for caching. The two search engines studied are Vivisimo and Excite. Their trace analysis results show that queries have

Constructing Uniform Resource Locators (URLs) for Searching the Marine Realms Information Bank

USGS Publications Warehouse

Linck, Guthrie A.; Allwardt, Alan O.; Lightsom, Frances L.

2009-01-01

The Marine Realms Information Bank (MRIB) is a digital library that provides access to free online scientific information about the oceans and coastal regions. To search its collection, MRIB uses a Common Gateway Interface (CGI) program, which allows automated search requests using Uniform Resource Locators (URLs). This document provides an overview of how to construct URLs to execute MRIB queries. The parameters listed allow detailed control of which records are retrieved, how they are returned, and how their display is formatted.

Conceptual search in electronic patient record.

PubMed

Baud, R H; Lovis, C; Ruch, P; Rassinoux, A M

2001-01-01

Search by content in a large corpus of free texts in the medical domain is, today, only partially solved. The so-called GREP approach (Get Regular Expression and Print), based on highly efficient string matching techniques, is subject to inherent limitations, especially its inability to recognize domain specific knowledge. Such methods oblige the user to formulate his or her query in a logical Boolean style; if this constraint is not fulfilled, the results are poor. The authors present an enhancement to string matching search by the addition of a light conceptual model behind the word lexicon. The new system accepts any sentence as a query and radically improves the quality of results. Efficiency regarding execution time is obtained at the expense of implementing advanced indexing algorithms in a pre-processing phase. The method is described and commented and a brief account of the results illustrates this paper.

Matching health information seekers' queries to medical terms

PubMed Central

2012-01-01

Background The Internet is a major source of health information but most seekers are not familiar with medical vocabularies. Hence, their searches fail due to bad query formulation. Several methods have been proposed to improve information retrieval: query expansion, syntactic and semantic techniques or knowledge-based methods. However, it would be useful to clean those queries which are misspelled. In this paper, we propose a simple yet efficient method in order to correct misspellings of queries submitted by health information seekers to a medical online search tool. Methods In addition to query normalizations and exact phonetic term matching, we tested two approximate string comparators: the similarity score function of Stoilos and the normalized Levenshtein edit distance. We propose here to combine them to increase the number of matched medical terms in French. We first took a sample of query logs to determine the thresholds and processing times. In the second run, at a greater scale we tested different combinations of query normalizations before or after misspelling correction with the retained thresholds in the first run. Results According to the total number of suggestions (around 163, the number of the first sample of queries), at a threshold comparator score of 0.3, the normalized Levenshtein edit distance gave the highest F-Measure (88.15%) and at a threshold comparator score of 0.7, the Stoilos function gave the highest F-Measure (84.31%). By combining Levenshtein and Stoilos, the highest F-Measure (80.28%) is obtained with 0.2 and 0.7 thresholds respectively. However, queries are composed by several terms that may be combination of medical terms. The process of query normalization and segmentation is thus required. The highest F-Measure (64.18%) is obtained when this process is realized before spelling-correction. Conclusions Despite the widely known high performance of the normalized edit distance of Levenshtein, we show in this paper that its combination with the Stoilos algorithm improved the results for misspelling correction of user queries. Accuracy is improved by combining spelling, phoneme-based information and string normalizations and segmentations into medical terms. These encouraging results have enabled the integration of this method into two projects funded by the French National Research Agency-Technologies for Health Care. The first aims to facilitate the coding process of clinical free texts contained in Electronic Health Records and discharge summaries, whereas the second aims at improving information retrieval through Electronic Health Records. PMID:23095521

Context-Aware Online Commercial Intention Detection

NASA Astrophysics Data System (ADS)

Hu, Derek Hao; Shen, Dou; Sun, Jian-Tao; Yang, Qiang; Chen, Zheng

With more and more commercial activities moving onto the Internet, people tend to purchase what they need through Internet or conduct some online research before the actual transactions happen. For many Web users, their online commercial activities start from submitting a search query to search engines. Just like the common Web search queries, the queries with commercial intention are usually very short. Recognizing the queries with commercial intention against the common queries will help search engines provide proper search results and advertisements, help Web users obtain the right information they desire and help the advertisers benefit from the potential transactions. However, the intentions behind a query vary a lot for users with different background and interest. The intentions can even be different for the same user, when the query is issued in different contexts. In this paper, we present a new algorithm framework based on skip-chain conditional random field (SCCRF) for automatically classifying Web queries according to context-based online commercial intention. We analyze our algorithm performance both theoretically and empirically. Extensive experiments on several real search engine log datasets show that our algorithm can improve more than 10% on F1 score than previous algorithms on commercial intention detection.

[On the seasonality of dermatoses: a retrospective analysis of search engine query data depending on the season].

PubMed

Köhler, M J; Springer, S; Kaatz, M

2014-09-01

The volume of search engine queries about disease-relevant items reflects public interest and correlates with disease prevalence as proven by the example of flu (influenza). Other influences include media attention or holidays. The present work investigates if the seasonality of prevalence or symptom severity of dermatoses correlates with search engine query data. The relative weekly volume of dermatological relevant search terms was assessed by the online tool Google Trends for the years 2009-2013. For each item, the degree of seasonality was calculated via frequency analysis and a geometric approach. Many dermatoses show a marked seasonality, reflected by search engine query volumes. Unexpected seasonal variations of these queries suggest a previously unknown variability of the respective disease prevalence. Furthermore, using the example of allergic rhinitis, a close correlation of search engine query data with actual pollen count can be demonstrated. In many cases, search engine query data are appropriate to estimate seasonal variability in prevalence of common dermatoses. This finding may be useful for real-time analysis and formation of hypotheses concerning pathogenetic or symptom aggravating mechanisms and may thus contribute to improvement of diagnostics and prevention of skin diseases.

Using search engine query data to track pharmaceutical utilization: a study of statins.

PubMed

Schuster, Nathaniel M; Rogers, Mary A M; McMahon, Laurence F

2010-08-01

To examine temporal and geographic associations between Google queries for health information and healthcare utilization benchmarks. Retrospective longitudinal study. Using Google Trends and Google Insights for Search data, the search terms Lipitor (atorvastatin calcium; Pfizer, Ann Arbor, MI) and simvastatin were evaluated for change over time and for association with Lipitor revenues. The relationship between query data and community-based resource use per Medicare beneficiary was assessed for 35 US metropolitan areas. Google queries for Lipitor significantly decreased from January 2004 through June 2009 and queries for simvastatin significantly increased (P <.001 for both), particularly after Lipitor came off patent (P <.001 for change in slope). The mean number of Google queries for Lipitor correlated (r = 0.98) with the percentage change in Lipitor global revenues from 2004 to 2008 (P <.001). Query preference for Lipitor over simvastatin was positively associated (r = 0.40) with a community's use of Medicare services. For every 1% increase in utilization of Medicare services in a community, there was a 0.2-unit increase in the ratio of Lipitor queries to simvastatin queries in that community (P = .02). Specific search engine queries for medical information correlate with pharmaceutical revenue and with overall healthcare utilization in a community. This suggests that search query data can track community-wide characteristics in healthcare utilization and have the potential for informing payers and policy makers regarding trends in utilization.

Using Search Engine Query Data to Explore the Epidemiology of Common Gastrointestinal Symptoms.

PubMed

Hassid, Benjamin G; Day, Lukejohn W; Awad, Mohannad A; Sewell, Justin L; Osterberg, E Charles; Breyer, Benjamin N

2017-03-01

Internet searches are an increasingly used tool in medical research. To date, no studies have examined Google search data in relation to common gastrointestinal symptoms. The aim of this study was to compare trends in Internet search volume with clinical datasets for common gastrointestinal symptoms. Using Google Trends, we recorded relative changes in volume of searches related to dysphagia, vomiting, and diarrhea in the USA between January 2008 and January 2011. We queried the National Inpatient Sample (NIS) and the National Hospital Ambulatory Medical Care Survey (NHAMCS) during this time period and identified cases related to these symptoms. We assessed the correlation between Google Trends and these two clinical datasets, as well as examined seasonal variation trends. Changes to Google search volume for all three symptoms correlated significantly with changes to NIS output (dysphagia: r = 0.5, P = 0.002; diarrhea: r = 0.79, P < 0.001; vomiting: r = 0.76, P < 0.001). Both Google and NIS data showed that the prevalence of all three symptoms rose during the time period studied. On the other hand, the NHAMCS data trends during this time period did not correlate well with either the NIS or the Google data for any of the three symptoms studied. Both the NIS and Google data showed modest seasonal variation. Changes to the population burden of chronic GI symptoms may be tracked by monitoring changes to Google search engine query volume over time. These data demonstrate that the prevalence of common GI symptoms is rising over time.

Privacy-Aware Relevant Data Access with Semantically Enriched Search Queries for Untrusted Cloud Storage Services.

PubMed

Pervez, Zeeshan; Ahmad, Mahmood; Khattak, Asad Masood; Lee, Sungyoung; Chung, Tae Choong

2016-01-01

Privacy-aware search of outsourced data ensures relevant data access in the untrusted domain of a public cloud service provider. Subscriber of a public cloud storage service can determine the presence or absence of a particular keyword by submitting search query in the form of a trapdoor. However, these trapdoor-based search queries are limited in functionality and cannot be used to identify secure outsourced data which contains semantically equivalent information. In addition, trapdoor-based methodologies are confined to pre-defined trapdoors and prevent subscribers from searching outsourced data with arbitrarily defined search criteria. To solve the problem of relevant data access, we have proposed an index-based privacy-aware search methodology that ensures semantic retrieval of data from an untrusted domain. This method ensures oblivious execution of a search query and leverages authorized subscribers to model conjunctive search queries without relying on predefined trapdoors. A security analysis of our proposed methodology shows that, in a conspired attack, unauthorized subscribers and untrusted cloud service providers cannot deduce any information that can lead to the potential loss of data privacy. A computational time analysis on commodity hardware demonstrates that our proposed methodology requires moderate computational resources to model a privacy-aware search query and for its oblivious evaluation on a cloud service provider.

Privacy-Aware Relevant Data Access with Semantically Enriched Search Queries for Untrusted Cloud Storage Services

PubMed Central

Pervez, Zeeshan; Ahmad, Mahmood; Khattak, Asad Masood; Lee, Sungyoung; Chung, Tae Choong

2016-01-01

Privacy-aware search of outsourced data ensures relevant data access in the untrusted domain of a public cloud service provider. Subscriber of a public cloud storage service can determine the presence or absence of a particular keyword by submitting search query in the form of a trapdoor. However, these trapdoor-based search queries are limited in functionality and cannot be used to identify secure outsourced data which contains semantically equivalent information. In addition, trapdoor-based methodologies are confined to pre-defined trapdoors and prevent subscribers from searching outsourced data with arbitrarily defined search criteria. To solve the problem of relevant data access, we have proposed an index-based privacy-aware search methodology that ensures semantic retrieval of data from an untrusted domain. This method ensures oblivious execution of a search query and leverages authorized subscribers to model conjunctive search queries without relying on predefined trapdoors. A security analysis of our proposed methodology shows that, in a conspired attack, unauthorized subscribers and untrusted cloud service providers cannot deduce any information that can lead to the potential loss of data privacy. A computational time analysis on commodity hardware demonstrates that our proposed methodology requires moderate computational resources to model a privacy-aware search query and for its oblivious evaluation on a cloud service provider. PMID:27571421

Searching for cancer information on the internet: analyzing natural language search queries.

PubMed

Bader, Judith L; Theofanos, Mary Frances

2003-12-11

Searching for health information is one of the most-common tasks performed by Internet users. Many users begin searching on popular search engines rather than on prominent health information sites. We know that many visitors to our (National Cancer Institute) Web site, cancer.gov, arrive via links in search engine result. To learn more about the specific needs of our general-public users, we wanted to understand what lay users really wanted to know about cancer, how they phrased their questions, and how much detail they used. The National Cancer Institute partnered with AskJeeves, Inc to develop a methodology to capture, sample, and analyze 3 months of cancer-related queries on the Ask.com Web site, a prominent United States consumer search engine, which receives over 35 million queries per week. Using a benchmark set of 500 terms and word roots supplied by the National Cancer Institute, AskJeeves identified a test sample of cancer queries for 1 week in August 2001. From these 500 terms only 37 appeared >or= 5 times/day over the trial test week in 17208 queries. Using these 37 terms, 204165 instances of cancer queries were found in the Ask.com query logs for the actual test period of June-August 2001. Of these, 7500 individual user questions were randomly selected for detailed analysis and assigned to appropriate categories. The exact language of sample queries is presented. Considering multiples of the same questions, the sample of 7500 individual user queries represented 76077 queries (37% of the total 3-month pool). Overall 78.37% of sampled Cancer queries asked about 14 specific cancer types. Within each cancer type, queries were sorted into appropriate subcategories including at least the following: General Information, Symptoms, Diagnosis and Testing, Treatment, Statistics, Definition, and Cause/Risk/Link. The most-common specific cancer types mentioned in queries were Digestive/Gastrointestinal/Bowel (15.0%), Breast (11.7%), Skin (11.3%), and Genitourinary (10.5%). Additional subcategories of queries about specific cancer types varied, depending on user input. Queries that were not specific to a cancer type were also tracked and categorized. Natural-language searching affords users the opportunity to fully express their information needs and can aid users naïve to the content and vocabulary. The specific queries analyzed for this study reflect news and research studies reported during the study dates and would surely change with different study dates. Analyzing queries from search engines represents one way of knowing what kinds of content to provide to users of a given Web site. Users ask questions using whole sentences and keywords, often misspelling words. Providing the option for natural-language searching does not obviate the need for good information architecture, usability engineering, and user testing in order to optimize user experience.

Searching for Cancer Information on the Internet: Analyzing Natural Language Search Queries

PubMed Central

Theofanos, Mary Frances

2003-01-01

Background Searching for health information is one of the most-common tasks performed by Internet users. Many users begin searching on popular search engines rather than on prominent health information sites. We know that many visitors to our (National Cancer Institute) Web site, cancer.gov, arrive via links in search engine result. Objective To learn more about the specific needs of our general-public users, we wanted to understand what lay users really wanted to know about cancer, how they phrased their questions, and how much detail they used. Methods The National Cancer Institute partnered with AskJeeves, Inc to develop a methodology to capture, sample, and analyze 3 months of cancer-related queries on the Ask.com Web site, a prominent United States consumer search engine, which receives over 35 million queries per week. Using a benchmark set of 500 terms and word roots supplied by the National Cancer Institute, AskJeeves identified a test sample of cancer queries for 1 week in August 2001. From these 500 terms only 37 appeared ≥ 5 times/day over the trial test week in 17208 queries. Using these 37 terms, 204165 instances of cancer queries were found in the Ask.com query logs for the actual test period of June-August 2001. Of these, 7500 individual user questions were randomly selected for detailed analysis and assigned to appropriate categories. The exact language of sample queries is presented. Results Considering multiples of the same questions, the sample of 7500 individual user queries represented 76077 queries (37% of the total 3-month pool). Overall 78.37% of sampled Cancer queries asked about 14 specific cancer types. Within each cancer type, queries were sorted into appropriate subcategories including at least the following: General Information, Symptoms, Diagnosis and Testing, Treatment, Statistics, Definition, and Cause/Risk/Link. The most-common specific cancer types mentioned in queries were Digestive/Gastrointestinal/Bowel (15.0%), Breast (11.7%), Skin (11.3%), and Genitourinary (10.5%). Additional subcategories of queries about specific cancer types varied, depending on user input. Queries that were not specific to a cancer type were also tracked and categorized. Conclusions Natural-language searching affords users the opportunity to fully express their information needs and can aid users naïve to the content and vocabulary. The specific queries analyzed for this study reflect news and research studies reported during the study dates and would surely change with different study dates. Analyzing queries from search engines represents one way of knowing what kinds of content to provide to users of a given Web site. Users ask questions using whole sentences and keywords, often misspelling words. Providing the option for natural-language searching does not obviate the need for good information architecture, usability engineering, and user testing in order to optimize user experience. PMID:14713659

System, method and apparatus for conducting a keyterm search

NASA Technical Reports Server (NTRS)

McGreevy, Michael W. (Inventor)

2004-01-01

A keyterm search is a method of searching a database for subsets of the database that are relevant to an input query. First, a number of relational models of subsets of a database are provided. A query is then input. The query can include one or more keyterms. Next, a gleaning model of the query is created. The gleaning model of the query is then compared to each one of the relational models of subsets of the database. The identifiers of the relevant subsets are then output.

System, method and apparatus for conducting a phrase search

NASA Technical Reports Server (NTRS)

McGreevy, Michael W. (Inventor)

2004-01-01

A phrase search is a method of searching a database for subsets of the database that are relevant to an input query. First, a number of relational models of subsets of a database are provided. A query is then input. The query can include one or more sequences of terms. Next, a relational model of the query is created. The relational model of the query is then compared to each one of the relational models of subsets of the database. The identifiers of the relevant subsets are then output.

Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillance.

PubMed

Chan, Emily H; Sahai, Vikram; Conrad, Corrie; Brownstein, John S

2011-05-01

A variety of obstacles including bureaucracy and lack of resources have interfered with timely detection and reporting of dengue cases in many endemic countries. Surveillance efforts have turned to modern data sources, such as Internet search queries, which have been shown to be effective for monitoring influenza-like illnesses. However, few have evaluated the utility of web search query data for other diseases, especially those of high morbidity and mortality or where a vaccine may not exist. In this study, we aimed to assess whether web search queries are a viable data source for the early detection and monitoring of dengue epidemics. Bolivia, Brazil, India, Indonesia and Singapore were chosen for analysis based on available data and adequate search volume. For each country, a univariate linear model was then built by fitting a time series of the fraction of Google search query volume for specific dengue-related queries from that country against a time series of official dengue case counts for a time-frame within 2003-2010. The specific combination of queries used was chosen to maximize model fit. Spurious spikes in the data were also removed prior to model fitting. The final models, fit using a training subset of the data, were cross-validated against both the overall dataset and a holdout subset of the data. All models were found to fit the data quite well, with validation correlations ranging from 0.82 to 0.99. Web search query data were found to be capable of tracking dengue activity in Bolivia, Brazil, India, Indonesia and Singapore. Whereas traditional dengue data from official sources are often not available until after some substantial delay, web search query data are available in near real-time. These data represent valuable complement to assist with traditional dengue surveillance.

A Dimensional Bus model for integrating clinical and research data.

PubMed

Wade, Ted D; Hum, Richard C; Murphy, James R

2011-12-01

Many clinical research data integration platforms rely on the Entity-Attribute-Value model because of its flexibility, even though it presents problems in query formulation and execution time. The authors sought more balance in these traits. Borrowing concepts from Entity-Attribute-Value and from enterprise data warehousing, the authors designed an alternative called the Dimensional Bus model and used it to integrate electronic medical record, sponsored study, and biorepository data. Each type of observational collection has its own table, and the structure of these tables varies to suit the source data. The observational tables are linked to the Bus, which holds provenance information and links to various classificatory dimensions that amplify the meaning of the data or facilitate its query and exposure management. The authors implemented a Bus-based clinical research data repository with a query system that flexibly manages data access and confidentiality, facilitates catalog search, and readily formulates and compiles complex queries. The design provides a workable way to manage and query mixed schemas in a data warehouse.

Classification of Automated Search Traffic

NASA Astrophysics Data System (ADS)

Buehrer, Greg; Stokes, Jack W.; Chellapilla, Kumar; Platt, John C.

As web search providers seek to improve both relevance and response times, they are challenged by the ever-increasing tax of automated search query traffic. Third party systems interact with search engines for a variety of reasons, such as monitoring a web site’s rank, augmenting online games, or possibly to maliciously alter click-through rates. In this paper, we investigate automated traffic (sometimes referred to as bot traffic) in the query stream of a large search engine provider. We define automated traffic as any search query not generated by a human in real time. We first provide examples of different categories of query logs generated by automated means. We then develop many different features that distinguish between queries generated by people searching for information, and those generated by automated processes. We categorize these features into two classes, either an interpretation of the physical model of human interactions, or as behavioral patterns of automated interactions. Using the these detection features, we next classify the query stream using multiple binary classifiers. In addition, a multiclass classifier is then developed to identify subclasses of both normal and automated traffic. An active learning algorithm is used to suggest which user sessions to label to improve the accuracy of the multiclass classifier, while also seeking to discover new classes of automated traffic. Performance analysis are then provided. Finally, the multiclass classifier is used to predict the subclass distribution for the search query stream.

Fast and Flexible Multivariate Time Series Subsequence Search

NASA Technical Reports Server (NTRS)

Bhaduri, Kanishka; Oza, Nikunj C.; Zhu, Qiang; Srivastava, Ashok N.

2010-01-01

Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which often contain several gigabytes of data. Surprisingly, research on MTS search is very limited. Most of the existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two algorithms to solve this problem (1) a List Based Search (LBS) algorithm which uses sorted lists for indexing, and (2) a R*-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences. Both algorithms guarantee that all matching patterns within the specified thresholds will be returned (no false dismissals). The very few false alarms can be removed by a post-processing step. Since our framework is also capable of Univariate Time-Series (UTS) subsequence search, we first demonstrate the efficiency of our algorithms on several UTS datasets previously used in the literature. We follow this up with experiments using two large MTS databases from the aviation domain, each containing several millions of observations. Both these tests show that our algorithms have very high prune rates (>99%) thus needing actual disk access for only less than 1% of the observations. To the best of our knowledge, MTS subsequence search has never been attempted on datasets of the size we have used in this paper.

Use of a data warehouse at an academic medical center for clinical pathology quality improvement, education, and research

PubMed Central

Krasowski, Matthew D.; Schriever, Andy; Mathur, Gagan; Blau, John L.; Stauffer, Stephanie L.; Ford, Bradley A.

2015-01-01

Background: Pathology data contained within the electronic health record (EHR), and laboratory information system (LIS) of hospitals represents a potentially powerful resource to improve clinical care. However, existing reporting tools within commercial EHR and LIS software may not be able to efficiently and rapidly mine data for quality improvement and research applications. Materials and Methods: We present experience using a data warehouse produced collaboratively between an academic medical center and a private company. The data warehouse contains data from the EHR, LIS, admission/discharge/transfer system, and billing records and can be accessed using a self-service data access tool known as Starmaker. The Starmaker software allows users to use complex Boolean logic, include and exclude rules, unit conversion and reference scaling, and value aggregation using a straightforward visual interface. More complex queries can be achieved by users with experience with Structured Query Language. Queries can use biomedical ontologies such as Logical Observation Identifiers Names and Codes and Systematized Nomenclature of Medicine. Result: We present examples of successful searches using Starmaker, falling mostly in the realm of microbiology and clinical chemistry/toxicology. The searches were ones that were either very difficult or basically infeasible using reporting tools within the EHR and LIS used in the medical center. One of the main strengths of Starmaker searches is rapid results, with typical searches covering 5 years taking only 1–2 min. A “Run Count” feature quickly outputs the number of cases meeting criteria, allowing for refinement of searches before downloading patient-identifiable data. The Starmaker tool is available to pathology residents and fellows, with some using this tool for quality improvement and scholarly projects. Conclusion: A data warehouse has significant potential for improving utilization of clinical pathology testing. Software that can access data warehouse using a straightforward visual interface can be incorporated into pathology training programs. PMID:26284156

Use of a data warehouse at an academic medical center for clinical pathology quality improvement, education, and research.

PubMed

Krasowski, Matthew D; Schriever, Andy; Mathur, Gagan; Blau, John L; Stauffer, Stephanie L; Ford, Bradley A

2015-01-01

Pathology data contained within the electronic health record (EHR), and laboratory information system (LIS) of hospitals represents a potentially powerful resource to improve clinical care. However, existing reporting tools within commercial EHR and LIS software may not be able to efficiently and rapidly mine data for quality improvement and research applications. We present experience using a data warehouse produced collaboratively between an academic medical center and a private company. The data warehouse contains data from the EHR, LIS, admission/discharge/transfer system, and billing records and can be accessed using a self-service data access tool known as Starmaker. The Starmaker software allows users to use complex Boolean logic, include and exclude rules, unit conversion and reference scaling, and value aggregation using a straightforward visual interface. More complex queries can be achieved by users with experience with Structured Query Language. Queries can use biomedical ontologies such as Logical Observation Identifiers Names and Codes and Systematized Nomenclature of Medicine. We present examples of successful searches using Starmaker, falling mostly in the realm of microbiology and clinical chemistry/toxicology. The searches were ones that were either very difficult or basically infeasible using reporting tools within the EHR and LIS used in the medical center. One of the main strengths of Starmaker searches is rapid results, with typical searches covering 5 years taking only 1-2 min. A "Run Count" feature quickly outputs the number of cases meeting criteria, allowing for refinement of searches before downloading patient-identifiable data. The Starmaker tool is available to pathology residents and fellows, with some using this tool for quality improvement and scholarly projects. A data warehouse has significant potential for improving utilization of clinical pathology testing. Software that can access data warehouse using a straightforward visual interface can be incorporated into pathology training programs.

Sensitivity and Predictive Value of 15 PubMed Search Strategies to Answer Clinical Questions Rated Against Full Systematic Reviews

PubMed Central

Merglen, Arnaud; Courvoisier, Delphine S; Combescure, Christophe; Garin, Nicolas; Perrier, Arnaud; Perneger, Thomas V

2012-01-01

Background Clinicians perform searches in PubMed daily, but retrieving relevant studies is challenging due to the rapid expansion of medical knowledge. Little is known about the performance of search strategies when they are applied to answer specific clinical questions. Objective To compare the performance of 15 PubMed search strategies in retrieving relevant clinical trials on therapeutic interventions. Methods We used Cochrane systematic reviews to identify relevant trials for 30 clinical questions. Search terms were extracted from the abstract using a predefined procedure based on the population, interventions, comparison, outcomes (PICO) framework and combined into queries. We tested 15 search strategies that varied in their query (PIC or PICO), use of PubMed’s Clinical Queries therapeutic filters (broad or narrow), search limits, and PubMed links to related articles. We assessed sensitivity (recall) and positive predictive value (precision) of each strategy on the first 2 PubMed pages (40 articles) and on the complete search output. Results The performance of the search strategies varied widely according to the clinical question. Unfiltered searches and those using the broad filter of Clinical Queries produced large outputs and retrieved few relevant articles within the first 2 pages, resulting in a median sensitivity of only 10%–25%. In contrast, all searches using the narrow filter performed significantly better, with a median sensitivity of about 50% (all P < .001 compared with unfiltered queries) and positive predictive values of 20%–30% (P < .001 compared with unfiltered queries). This benefit was consistent for most clinical questions. Searches based on related articles retrieved about a third of the relevant studies. Conclusions The Clinical Queries narrow filter, along with well-formulated queries based on the PICO framework, provided the greatest aid in retrieving relevant clinical trials within the 2 first PubMed pages. These results can help clinicians apply effective strategies to answer their questions at the point of care. PMID:22693047

Sensitivity and predictive value of 15 PubMed search strategies to answer clinical questions rated against full systematic reviews.

PubMed

Agoritsas, Thomas; Merglen, Arnaud; Courvoisier, Delphine S; Combescure, Christophe; Garin, Nicolas; Perrier, Arnaud; Perneger, Thomas V

2012-06-12

Clinicians perform searches in PubMed daily, but retrieving relevant studies is challenging due to the rapid expansion of medical knowledge. Little is known about the performance of search strategies when they are applied to answer specific clinical questions. To compare the performance of 15 PubMed search strategies in retrieving relevant clinical trials on therapeutic interventions. We used Cochrane systematic reviews to identify relevant trials for 30 clinical questions. Search terms were extracted from the abstract using a predefined procedure based on the population, interventions, comparison, outcomes (PICO) framework and combined into queries. We tested 15 search strategies that varied in their query (PIC or PICO), use of PubMed's Clinical Queries therapeutic filters (broad or narrow), search limits, and PubMed links to related articles. We assessed sensitivity (recall) and positive predictive value (precision) of each strategy on the first 2 PubMed pages (40 articles) and on the complete search output. The performance of the search strategies varied widely according to the clinical question. Unfiltered searches and those using the broad filter of Clinical Queries produced large outputs and retrieved few relevant articles within the first 2 pages, resulting in a median sensitivity of only 10%-25%. In contrast, all searches using the narrow filter performed significantly better, with a median sensitivity of about 50% (all P < .001 compared with unfiltered queries) and positive predictive values of 20%-30% (P < .001 compared with unfiltered queries). This benefit was consistent for most clinical questions. Searches based on related articles retrieved about a third of the relevant studies. The Clinical Queries narrow filter, along with well-formulated queries based on the PICO framework, provided the greatest aid in retrieving relevant clinical trials within the 2 first PubMed pages. These results can help clinicians apply effective strategies to answer their questions at the point of care.

Improving Web Search for Difficult Queries

ERIC Educational Resources Information Center

Wang, Xuanhui

2009-01-01

Search engines have now become essential tools in all aspects of our life. Although a variety of information needs can be served very successfully, there are still a lot of queries that search engines can not answer very effectively and these queries always make users feel frustrated. Since it is quite often that users encounter such "difficult…

Analysis of queries sent to PubMed at the point of care: Observation of search behaviour in a medical teaching hospital

PubMed Central

Hoogendam, Arjen; Stalenhoef, Anton FH; Robbé, Pieter F de Vries; Overbeke, A John PM

2008-01-01

Background The use of PubMed to answer daily medical care questions is limited because it is challenging to retrieve a small set of relevant articles and time is restricted. Knowing what aspects of queries are likely to retrieve relevant articles can increase the effectiveness of PubMed searches. The objectives of our study were to identify queries that are likely to retrieve relevant articles by relating PubMed search techniques and tools to the number of articles retrieved and the selection of articles for further reading. Methods This was a prospective observational study of queries regarding patient-related problems sent to PubMed by residents and internists in internal medicine working in an Academic Medical Centre. We analyzed queries, search results, query tools (Mesh, Limits, wildcards, operators), selection of abstract and full-text for further reading, using a portal that mimics PubMed. Results PubMed was used to solve 1121 patient-related problems, resulting in 3205 distinct queries. Abstracts were viewed in 999 (31%) of these queries, and in 126 (39%) of 321 queries using query tools. The average term count per query was 2.5. Abstracts were selected in more than 40% of queries using four or five terms, increasing to 63% if the use of four or five terms yielded 2–161 articles. Conclusion Queries sent to PubMed by physicians at our hospital during daily medical care contain fewer than three terms. Queries using four to five terms, retrieving less than 161 article titles, are most likely to result in abstract viewing. PubMed search tools are used infrequently by our population and are less effective than the use of four or five terms. Methods to facilitate the formulation of precise queries, using more relevant terms, should be the focus of education and research. PMID:18816391

Improving biomedical information retrieval by linear combinations of different query expansion techniques.

PubMed

Abdulla, Ahmed AbdoAziz Ahmed; Lin, Hongfei; Xu, Bo; Banbhrani, Santosh Kumar

2016-07-25

Biomedical literature retrieval is becoming increasingly complex, and there is a fundamental need for advanced information retrieval systems. Information Retrieval (IR) programs scour unstructured materials such as text documents in large reserves of data that are usually stored on computers. IR is related to the representation, storage, and organization of information items, as well as to access. In IR one of the main problems is to determine which documents are relevant and which are not to the user's needs. Under the current regime, users cannot precisely construct queries in an accurate way to retrieve particular pieces of data from large reserves of data. Basic information retrieval systems are producing low-quality search results. In our proposed system for this paper we present a new technique to refine Information Retrieval searches to better represent the user's information need in order to enhance the performance of information retrieval by using different query expansion techniques and apply a linear combinations between them, where the combinations was linearly between two expansion results at one time. Query expansions expand the search query, for example, by finding synonyms and reweighting original terms. They provide significantly more focused, particularized search results than do basic search queries. The retrieval performance is measured by some variants of MAP (Mean Average Precision) and according to our experimental results, the combination of best results of query expansion is enhanced the retrieved documents and outperforms our baseline by 21.06 %, even it outperforms a previous study by 7.12 %. We propose several query expansion techniques and their combinations (linearly) to make user queries more cognizable to search engines and to produce higher-quality search results.

Sexual information seeking on web search engines.

PubMed

Spink, Amanda; Koricich, Andrew; Jansen, B J; Cole, Charles

2004-02-01

Sexual information seeking is an important element within human information behavior. Seeking sexually related information on the Internet takes many forms and channels, including chat rooms discussions, accessing Websites or searching Web search engines for sexual materials. The study of sexual Web queries provides insight into sexually-related information-seeking behavior, of value to Web users and providers alike. We qualitatively analyzed queries from logs of 1,025,910 Alta Vista and AlltheWeb.com Web user queries from 2001. We compared the differences in sexually-related Web searching between Alta Vista and AlltheWeb.com users. Differences were found in session duration, query outcomes, and search term choices. Implications of the findings for sexual information seeking are discussed.

Towards computational improvement of DNA database indexing and short DNA query searching.

PubMed

Stojanov, Done; Koceski, Sašo; Mileva, Aleksandra; Koceska, Nataša; Bande, Cveta Martinovska

2014-09-03

In order to facilitate and speed up the search of massive DNA databases, the database is indexed at the beginning, employing a mapping function. By searching through the indexed data structure, exact query hits can be identified. If the database is searched against an annotated DNA query, such as a known promoter consensus sequence, then the starting locations and the number of potential genes can be determined. This is particularly relevant if unannotated DNA sequences have to be functionally annotated. However, indexing a massive DNA database and searching an indexed data structure with millions of entries is a time-demanding process. In this paper, we propose a fast DNA database indexing and searching approach, identifying all query hits in the database, without having to examine all entries in the indexed data structure, limiting the maximum length of a query that can be searched against the database. By applying the proposed indexing equation, the whole human genome could be indexed in 10 hours on a personal computer, under the assumption that there is enough RAM to store the indexed data structure. Analysing the methodology proposed by Reneker, we observed that hits at starting positions [Formula: see text] are not reported, if the database is searched against a query shorter than [Formula: see text] nucleotides, such that [Formula: see text] is the length of the DNA database words being mapped and [Formula: see text] is the length of the query. A solution of this drawback is also presented.

muBLASTP: database-indexed protein sequence search on multicore CPUs.

PubMed

Zhang, Jing; Misra, Sanchit; Wang, Hao; Feng, Wu-Chun

2016-11-04

The Basic Local Alignment Search Tool (BLAST) is a fundamental program in the life sciences that searches databases for sequences that are most similar to a query sequence. Currently, the BLAST algorithm utilizes a query-indexed approach. Although many approaches suggest that sequence search with a database index can achieve much higher throughput (e.g., BLAT, SSAHA, and CAFE), they cannot deliver the same level of sensitivity as the query-indexed BLAST, i.e., NCBI BLAST, or they can only support nucleotide sequence search, e.g., MegaBLAST. Due to different challenges and characteristics between query indexing and database indexing, the existing techniques for query-indexed search cannot be used into database indexed search. muBLASTP, a novel database-indexed BLAST for protein sequence search, delivers identical hits returned to NCBI BLAST. On Intel Haswell multicore CPUs, for a single query, the single-threaded muBLASTP achieves up to a 4.41-fold speedup for alignment stages, and up to a 1.75-fold end-to-end speedup over single-threaded NCBI BLAST. For a batch of queries, the multithreaded muBLASTP achieves up to a 5.7-fold speedups for alignment stages, and up to a 4.56-fold end-to-end speedup over multithreaded NCBI BLAST. With a newly designed index structure for protein database and associated optimizations in BLASTP algorithm, we re-factored BLASTP algorithm for modern multicore processors that achieves much higher throughput with acceptable memory footprint for the database index.

PolySearch2: a significantly improved text-mining system for discovering associations between human diseases, genes, drugs, metabolites, toxins and more.

PubMed

Liu, Yifeng; Liang, Yongjie; Wishart, David

2015-07-01

PolySearch2 (http://polysearch.ca) is an online text-mining system for identifying relationships between biomedical entities such as human diseases, genes, SNPs, proteins, drugs, metabolites, toxins, metabolic pathways, organs, tissues, subcellular organelles, positive health effects, negative health effects, drug actions, Gene Ontology terms, MeSH terms, ICD-10 medical codes, biological taxonomies and chemical taxonomies. PolySearch2 supports a generalized 'Given X, find all associated Ys' query, where X and Y can be selected from the aforementioned biomedical entities. An example query might be: 'Find all diseases associated with Bisphenol A'. To find its answers, PolySearch2 searches for associations against comprehensive collections of free-text collections, including local versions of MEDLINE abstracts, PubMed Central full-text articles, Wikipedia full-text articles and US Patent application abstracts. PolySearch2 also searches 14 widely used, text-rich biological databases such as UniProt, DrugBank and Human Metabolome Database to improve its accuracy and coverage. PolySearch2 maintains an extensive thesaurus of biological terms and exploits the latest search engine technology to rapidly retrieve relevant articles and databases records. PolySearch2 also generates, ranks and annotates associative candidates and present results with relevancy statistics and highlighted key sentences to facilitate user interpretation. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

PolySearch2: a significantly improved text-mining system for discovering associations between human diseases, genes, drugs, metabolites, toxins and more

PubMed Central

Liu, Yifeng; Liang, Yongjie; Wishart, David

2015-01-01

PolySearch2 (http://polysearch.ca) is an online text-mining system for identifying relationships between biomedical entities such as human diseases, genes, SNPs, proteins, drugs, metabolites, toxins, metabolic pathways, organs, tissues, subcellular organelles, positive health effects, negative health effects, drug actions, Gene Ontology terms, MeSH terms, ICD-10 medical codes, biological taxonomies and chemical taxonomies. PolySearch2 supports a generalized ‘Given X, find all associated Ys’ query, where X and Y can be selected from the aforementioned biomedical entities. An example query might be: ‘Find all diseases associated with Bisphenol A’. To find its answers, PolySearch2 searches for associations against comprehensive collections of free-text collections, including local versions of MEDLINE abstracts, PubMed Central full-text articles, Wikipedia full-text articles and US Patent application abstracts. PolySearch2 also searches 14 widely used, text-rich biological databases such as UniProt, DrugBank and Human Metabolome Database to improve its accuracy and coverage. PolySearch2 maintains an extensive thesaurus of biological terms and exploits the latest search engine technology to rapidly retrieve relevant articles and databases records. PolySearch2 also generates, ranks and annotates associative candidates and present results with relevancy statistics and highlighted key sentences to facilitate user interpretation. PMID:25925572

Web Searching: A Process-Oriented Experimental Study of Three Interactive Search Paradigms.

ERIC Educational Resources Information Center

Dennis, Simon; Bruza, Peter; McArthur, Robert

2002-01-01

Compares search effectiveness when using query-based Internet search via the Google search engine, directory-based search via Yahoo, and phrase-based query reformulation-assisted search via the Hyperindex browser by means of a controlled, user-based experimental study of undergraduates at the University of Queensland. Discusses cognitive load,…

Use of controlled vocabularies to improve biomedical information retrieval tasks.

PubMed

Pasche, Emilie; Gobeill, Julien; Vishnyakova, Dina; Ruch, Patrick; Lovis, Christian

2013-01-01

The high heterogeneity of biomedical vocabulary is a major obstacle for information retrieval in large biomedical collections. Therefore, using biomedical controlled vocabularies is crucial for managing these contents. We investigate the impact of query expansion based on controlled vocabularies to improve the effectiveness of two search engines. Our strategy relies on the enrichment of users' queries with additional terms, directly derived from such vocabularies applied to infectious diseases and chemical patents. We observed that query expansion based on pathogen names resulted in improvements of the top-precision of our first search engine, while the normalization of diseases degraded the top-precision. The expansion of chemical entities, which was performed on the second search engine, positively affected the mean average precision. We have shown that query expansion of some types of biomedical entities has a great potential to improve search effectiveness; therefore a fine-tuning of query expansion strategies could help improving the performances of search engines.

Searching for Images: The Analysis of Users' Queries for Image Retrieval in American History.

ERIC Educational Resources Information Center

Choi, Youngok; Rasmussen, Edie M.

2003-01-01

Studied users' queries for visual information in American history to identify the image attributes important for retrieval and the characteristics of users' queries for digital images, based on queries from 38 faculty and graduate students. Results of pre- and post-test questionnaires and interviews suggest principle categories of search terms.…

Using Web Search Query Data to Monitor Dengue Epidemics: A New Model for Neglected Tropical Disease Surveillance

PubMed Central

Chan, Emily H.; Sahai, Vikram; Conrad, Corrie; Brownstein, John S.

2011-01-01

Background A variety of obstacles including bureaucracy and lack of resources have interfered with timely detection and reporting of dengue cases in many endemic countries. Surveillance efforts have turned to modern data sources, such as Internet search queries, which have been shown to be effective for monitoring influenza-like illnesses. However, few have evaluated the utility of web search query data for other diseases, especially those of high morbidity and mortality or where a vaccine may not exist. In this study, we aimed to assess whether web search queries are a viable data source for the early detection and monitoring of dengue epidemics. Methodology/Principal Findings Bolivia, Brazil, India, Indonesia and Singapore were chosen for analysis based on available data and adequate search volume. For each country, a univariate linear model was then built by fitting a time series of the fraction of Google search query volume for specific dengue-related queries from that country against a time series of official dengue case counts for a time-frame within 2003–2010. The specific combination of queries used was chosen to maximize model fit. Spurious spikes in the data were also removed prior to model fitting. The final models, fit using a training subset of the data, were cross-validated against both the overall dataset and a holdout subset of the data. All models were found to fit the data quite well, with validation correlations ranging from 0.82 to 0.99. Conclusions/Significance Web search query data were found to be capable of tracking dengue activity in Bolivia, Brazil, India, Indonesia and Singapore. Whereas traditional dengue data from official sources are often not available until after some substantial delay, web search query data are available in near real-time. These data represent valuable complement to assist with traditional dengue surveillance. PMID:21647308

Multimedia Web Searching Trends.

ERIC Educational Resources Information Center

Ozmutlu, Seda; Spink, Amanda; Ozmutlu, H. Cenk

2002-01-01

Examines and compares multimedia Web searching by Excite and FAST search engine users in 2001. Highlights include audio and video queries; time spent on searches; terms per query; ranking of the most frequently used terms; and differences in Web search behaviors of U.S. and European Web users. (Author/LRW)

Cumulative query method for influenza surveillance using search engine data.

PubMed

Seo, Dong-Woo; Jo, Min-Woo; Sohn, Chang Hwan; Shin, Soo-Yong; Lee, JaeHo; Yu, Maengsoo; Kim, Won Young; Lim, Kyoung Soo; Lee, Sang-Il

2014-12-16

Internet search queries have become an important data source in syndromic surveillance system. However, there is currently no syndromic surveillance system using Internet search query data in South Korea. The objective of this study was to examine correlations between our cumulative query method and national influenza surveillance data. Our study was based on the local search engine, Daum (approximately 25% market share), and influenza-like illness (ILI) data from the Korea Centers for Disease Control and Prevention. A quota sampling survey was conducted with 200 participants to obtain popular queries. We divided the study period into two sets: Set 1 (the 2009/10 epidemiological year for development set 1 and 2010/11 for validation set 1) and Set 2 (2010/11 for development Set 2 and 2011/12 for validation Set 2). Pearson's correlation coefficients were calculated between the Daum data and the ILI data for the development set. We selected the combined queries for which the correlation coefficients were .7 or higher and listed them in descending order. Then, we created a cumulative query method n representing the number of cumulative combined queries in descending order of the correlation coefficient. In validation set 1, 13 cumulative query methods were applied, and 8 had higher correlation coefficients (min=.916, max=.943) than that of the highest single combined query. Further, 11 of 13 cumulative query methods had an r value of ≥.7, but 4 of 13 combined queries had an r value of ≥.7. In validation set 2, 8 of 15 cumulative query methods showed higher correlation coefficients (min=.975, max=.987) than that of the highest single combined query. All 15 cumulative query methods had an r value of ≥.7, but 6 of 15 combined queries had an r value of ≥.7. Cumulative query method showed relatively higher correlation with national influenza surveillance data than combined queries in the development and validation set.

Retrieval of diagnostic and treatment studies for clinical use through PubMed and PubMed's Clinical Queries filters.

PubMed

Lokker, Cynthia; Haynes, R Brian; Wilczynski, Nancy L; McKibbon, K Ann; Walter, Stephen D

2011-01-01

Clinical Queries filters were developed to improve the retrieval of high-quality studies in searches on clinical matters. The study objective was to determine the yield of relevant citations and physician satisfaction while searching for diagnostic and treatment studies using the Clinical Queries page of PubMed compared with searching PubMed without these filters. Forty practicing physicians, presented with standardized treatment and diagnosis questions and one question of their choosing, entered search terms which were processed in a random, blinded fashion through PubMed alone and PubMed Clinical Queries. Participants rated search retrievals for applicability to the question at hand and satisfaction. For treatment, the primary outcome of retrieval of relevant articles was not significantly different between the groups, but a higher proportion of articles from the Clinical Queries searches met methodologic criteria (p=0.049), and more articles were published in core internal medicine journals (p=0.056). For diagnosis, the filtered results returned more relevant articles (p=0.031) and fewer irrelevant articles (overall retrieval less, p=0.023); participants needed to screen fewer articles before arriving at the first relevant citation (p<0.05). Relevance was also influenced by content terms used by participants in searching. Participants varied greatly in their search performance. Clinical Queries filtered searches returned more high-quality studies, though the retrieval of relevant articles was only statistically different between the groups for diagnosis questions. Retrieving clinically important research studies from Medline is a challenging task for physicians. Methodological search filters can improve search retrieval.

FastBit Reference Manual

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

Wu, Kesheng

2007-08-02

An index in a database system is a data structure that utilizes redundant information about the base data to speed up common searching and retrieval operations. Most commonly used indexes are variants of B-trees, such as B+-tree and B*-tree. FastBit implements a set of alternative indexes call compressed bitmap indexes. Compared with B-tree variants, these indexes provide very efficient searching and retrieval operations by sacrificing the efficiency of updating the indexes after the modification of an individual record. In addition to the well-known strengths of bitmap indexes, FastBit has a special strength stemming from the bitmap compression scheme used. Themore » compression method is called the Word-Aligned Hybrid (WAH) code. It reduces the bitmap indexes to reasonable sizes and at the same time allows very efficient bitwise logical operations directly on the compressed bitmaps. Compared with the well-known compression methods such as LZ77 and Byte-aligned Bitmap code (BBC), WAH sacrifices some space efficiency for a significant improvement in operational efficiency. Since the bitwise logical operations are the most important operations needed to answer queries, using WAH compression has been shown to answer queries significantly faster than using other compression schemes. Theoretical analyses showed that WAH compressed bitmap indexes are optimal for one-dimensional range queries. Only the most efficient indexing schemes such as B+-tree and B*-tree have this optimality property. However, bitmap indexes are superior because they can efficiently answer multi-dimensional range queries by combining the answers to one-dimensional queries.« less

Saying What You're Looking For: Linguistics Meets Video Search.

PubMed

Barrett, Daniel Paul; Barbu, Andrei; Siddharth, N; Siskind, Jeffrey Mark

2016-10-01

We present an approach to searching large video corpora for clips which depict a natural-language query in the form of a sentence. Compositional semantics is used to encode subtle meaning differences lost in other approaches, such as the difference between two sentences which have identical words but entirely different meaning: The person rode the horse versus The horse rode the person. Given a sentential query and a natural-language parser, we produce a score indicating how well a video clip depicts that sentence for each clip in a corpus and return a ranked list of clips. Two fundamental problems are addressed simultaneously: detecting and tracking objects, and recognizing whether those tracks depict the query. Because both tracking and object detection are unreliable, our approach uses the sentential query to focus the tracker on the relevant participants and ensures that the resulting tracks are described by the sentential query. While most earlier work was limited to single-word queries which correspond to either verbs or nouns, we search for complex queries which contain multiple phrases, such as prepositional phrases, and modifiers, such as adverbs. We demonstrate this approach by searching for 2,627 naturally elicited sentential queries in 10 Hollywood movies.

Quality performance measurement using the text of electronic medical records.

PubMed

Pakhomov, Serguei; Bjornsen, Susan; Hanson, Penny; Smith, Steven

2008-01-01

Annual foot examinations (FE) constitute a critical component of care for diabetes. Documented evidence of FE is central to quality-of-care reporting; however, manual abstraction of electronic medical records (EMR) is slow, expensive, and subject to error. The objective of this study was to test the hypothesis that text mining of the EMR results in ascertaining FE evidence with accuracy comparable to manual abstraction. The text of inpatient and outpatient clinical reports was searched with natural-language (NL) queries for evidence of neurological, vascular, and structural components of FE. A manual medical records audit was used for validation. The reference standard consisted of 3 independent sets used for development (n=200 ), validation (n=118), and reliability (n=80). The reliability of manual auditing was 91% (95% confidence interval [CI]= 85-97) and was determined by comparing the results of an additional audit to the original audit using the records in the reliability set. The accuracy of the NL query requiring 1 of 3 FE components was 89% (95% CI=83-95). The accuracy of the query requiring any 2 of 3 components was 88% (95% CI=82-94). The accuracy of the query requiring all 3 components was 75% (95% CI= 68- 83). The free text of the EMR is a viable source of information necessary for quality of health care reporting on the evidence of FE for patients with diabetes. The low-cost methodology is scalable to monitoring large numbers of patients and can be used to streamline quality-of-care reporting.

A Firefly Algorithm-based Approach for Pseudo-Relevance Feedback: Application to Medical Database.

PubMed

Khennak, Ilyes; Drias, Habiba

2016-11-01

The difficulty of disambiguating the sense of the incomplete and imprecise keywords that are extensively used in the search queries has caused the failure of search systems to retrieve the desired information. One of the most powerful and promising method to overcome this shortcoming and improve the performance of search engines is Query Expansion, whereby the user's original query is augmented by new keywords that best characterize the user's information needs and produce more useful query. In this paper, a new Firefly Algorithm-based approach is proposed to enhance the retrieval effectiveness of query expansion while maintaining low computational complexity. In contrast to the existing literature, the proposed approach uses a Firefly Algorithm to find the best expanded query among a set of expanded query candidates. Moreover, this new approach allows the determination of the length of the expanded query empirically. Experimental results on MEDLINE, the on-line medical information database, show that our proposed approach is more effective and efficient compared to the state-of-the-art.

Shuttle-Data-Tape XML Translator

NASA Technical Reports Server (NTRS)

Barry, Matthew R.; Osborne, Richard N.

2005-01-01

JSDTImport is a computer program for translating native Shuttle Data Tape (SDT) files from American Standard Code for Information Interchange (ASCII) format into databases in other formats. JSDTImport solves the problem of organizing the SDT content, affording flexibility to enable users to choose how to store the information in a database to better support client and server applications. JSDTImport can be dynamically configured by use of a simple Extensible Markup Language (XML) file. JSDTImport uses this XML file to define how each record and field will be parsed, its layout and definition, and how the resulting database will be structured. JSDTImport also includes a client application programming interface (API) layer that provides abstraction for the data-querying process. The API enables a user to specify the search criteria to apply in gathering all the data relevant to a query. The API can be used to organize the SDT content and translate into a native XML database. The XML format is structured into efficient sections, enabling excellent query performance by use of the XPath query language. Optionally, the content can be translated into a Structured Query Language (SQL) database for fast, reliable SQL queries on standard database server computers.

A Dimensional Bus model for integrating clinical and research data

PubMed Central

Hum, Richard C; Murphy, James R

2011-01-01

Objectives Many clinical research data integration platforms rely on the Entity–Attribute–Value model because of its flexibility, even though it presents problems in query formulation and execution time. The authors sought more balance in these traits. Materials and Methods Borrowing concepts from Entity–Attribute–Value and from enterprise data warehousing, the authors designed an alternative called the Dimensional Bus model and used it to integrate electronic medical record, sponsored study, and biorepository data. Each type of observational collection has its own table, and the structure of these tables varies to suit the source data. The observational tables are linked to the Bus, which holds provenance information and links to various classificatory dimensions that amplify the meaning of the data or facilitate its query and exposure management. Results The authors implemented a Bus-based clinical research data repository with a query system that flexibly manages data access and confidentiality, facilitates catalog search, and readily formulates and compiles complex queries. Conclusion The design provides a workable way to manage and query mixed schemas in a data warehouse. PMID:21856687

Accessing suicide-related information on the internet: a retrospective observational study of search behavior.

PubMed

Wong, Paul Wai-Ching; Fu, King-Wa; Yau, Rickey Sai-Pong; Ma, Helen Hei-Man; Law, Yik-Wa; Chang, Shu-Sen; Yip, Paul Siu-Fai

2013-01-11

The Internet's potential impact on suicide is of major public health interest as easy online access to pro-suicide information or specific suicide methods may increase suicide risk among vulnerable Internet users. Little is known, however, about users' actual searching and browsing behaviors of online suicide-related information. To investigate what webpages people actually clicked on after searching with suicide-related queries on a search engine and to examine what queries people used to get access to pro-suicide websites. A retrospective observational study was done. We used a web search dataset released by America Online (AOL). The dataset was randomly sampled from all AOL subscribers' web queries between March and May 2006 and generated by 657,000 service subscribers. We found 5526 search queries (0.026%, 5526/21,000,000) that included the keyword "suicide". The 5526 search queries included 1586 different search terms and were generated by 1625 unique subscribers (0.25%, 1625/657,000). Of these queries, 61.38% (3392/5526) were followed by users clicking on a search result. Of these 3392 queries, 1344 (39.62%) webpages were clicked on by 930 unique users but only 1314 of those webpages were accessible during the study period. Each clicked-through webpage was classified into 11 categories. The categories of the most visited webpages were: entertainment (30.13%; 396/1314), scientific information (18.31%; 240/1314), and community resources (14.53%; 191/1314). Among the 1314 accessed webpages, we could identify only two pro-suicide websites. We found that the search terms used to access these sites included "commiting suicide with a gas oven", "hairless goat", "pictures of murder by strangulation", and "photo of a severe burn". A limitation of our study is that the database may be dated and confined to mainly English webpages. Searching or browsing suicide-related or pro-suicide webpages was uncommon, although a small group of users did access websites that contain detailed suicide method information.

Federated Space-Time Query for Earth Science Data Using OpenSearch Conventions

NASA Astrophysics Data System (ADS)

Lynnes, C.; Beaumont, B.; Duerr, R. E.; Hua, H.

2009-12-01

The past decade has seen a burgeoning of remote sensing and Earth science data providers, as evidenced in the growth of the Earth Science Information Partner (ESIP) federation. At the same time, the need to combine diverse data sets to enable understanding of the Earth as a system has also grown. While the expansion of data providers is in general a boon to such studies, the diversity presents a challenge to finding useful data for a given study. Locating all the data files with aerosol information for a particular volcanic eruption, for example, may involve learning and using several different search tools to execute the requisite space-time queries. To address this issue, the ESIP federation is developing a federated space-time query framework, based on the OpenSearch convention (www.opensearch.org), with Geo and Time extensions. In this framework, data providers publish OpenSearch Description Documents that describe in a machine-readable form how to execute queries against the provider. The novelty of OpenSearch is that the space-time query interface becomes both machine callable and easy enough to integrate into the web browser's search box. This flexibility, together with a simple REST (HTTP-get) interface, should allow a variety of data providers to participate in the federated search framework, from large institutional data centers to individual scientists. The simple interface enables trivial querying of multiple data sources and participation in recursive-like federated searches--all using the same common OpenSearch interface. This simplicity also makes the construction of clients easy, as does existing OpenSearch client libraries in a variety of languages. Moreover, a number of clients and aggregation services already exist and OpenSearch is already supported by a number of web browsers such as Firefox and Internet Explorer.

A rank-based Prediction Algorithm of Learning User's Intention

NASA Astrophysics Data System (ADS)

Shen, Jie; Gao, Ying; Chen, Cang; Gong, HaiPing

Internet search has become an important part in people's daily life. People can find many types of information to meet different needs through search engines on the Internet. There are two issues for the current search engines: first, the users should predetermine the types of information they want and then change to the appropriate types of search engine interfaces. Second, most search engines can support multiple kinds of search functions, each function has its own separate search interface. While users need different types of information, they must switch between different interfaces. In practice, most queries are corresponding to various types of information results. These queries can search the relevant results in various search engines, such as query "Palace" contains the websites about the introduction of the National Palace Museum, blog, Wikipedia, some pictures and video information. This paper presents a new aggregative algorithm for all kinds of search results. It can filter and sort the search results by learning three aspects about the query words, search results and search history logs to achieve the purpose of detecting user's intention. Experiments demonstrate that this rank-based method for multi-types of search results is effective. It can meet the user's search needs well, enhance user's satisfaction, provide an effective and rational model for optimizing search engines and improve user's search experience.

Noesis: Ontology based Scoped Search Engine and Resource Aggregator for Atmospheric Science

NASA Astrophysics Data System (ADS)

Ramachandran, R.; Movva, S.; Li, X.; Cherukuri, P.; Graves, S.

2006-12-01

The goal for search engines is to return results that are both accurate and complete. The search engines should find only what you really want and find everything you really want. Search engines (even meta search engines) lack semantics. The basis for search is simply based on string matching between the user's query term and the resource database and the semantics associated with the search string is not captured. For example, if an atmospheric scientist is searching for "pressure" related web resources, most search engines return inaccurate results such as web resources related to blood pressure. In this presentation Noesis, which is a meta-search engine and a resource aggregator that uses domain ontologies to provide scoped search capabilities will be described. Noesis uses domain ontologies to help the user scope the search query to ensure that the search results are both accurate and complete. The domain ontologies guide the user to refine their search query and thereby reduce the user's burden of experimenting with different search strings. Semantics are captured by refining the query terms to cover synonyms, specializations, generalizations and related concepts. Noesis also serves as a resource aggregator. It categorizes the search results from different online resources such as education materials, publications, datasets, web search engines that might be of interest to the user.

Structuring Legacy Pathology Reports by openEHR Archetypes to Enable Semantic Querying.

PubMed

Kropf, Stefan; Krücken, Peter; Mueller, Wolf; Denecke, Kerstin

2017-05-18

Clinical information is often stored as free text, e.g. in discharge summaries or pathology reports. These documents are semi-structured using section headers, numbered lists, items and classification strings. However, it is still challenging to retrieve relevant documents since keyword searches applied on complete unstructured documents result in many false positive retrieval results. We are concentrating on the processing of pathology reports as an example for unstructured clinical documents. The objective is to transform reports semi-automatically into an information structure that enables an improved access and retrieval of relevant data. The data is expected to be stored in a standardized, structured way to make it accessible for queries that are applied to specific sections of a document (section-sensitive queries) and for information reuse. Our processing pipeline comprises information modelling, section boundary detection and section-sensitive queries. For enabling a focused search in unstructured data, documents are automatically structured and transformed into a patient information model specified through openEHR archetypes. The resulting XML-based pathology electronic health records (PEHRs) are queried by XQuery and visualized by XSLT in HTML. Pathology reports (PRs) can be reliably structured into sections by a keyword-based approach. The information modelling using openEHR allows saving time in the modelling process since many archetypes can be reused. The resulting standardized, structured PEHRs allow accessing relevant data by retrieving data matching user queries. Mapping unstructured reports into a standardized information model is a practical solution for a better access to data. Archetype-based XML enables section-sensitive retrieval and visualisation by well-established XML techniques. Focussing the retrieval to particular sections has the potential of saving retrieval time and improving the accuracy of the retrieval.

Bat-Inspired Algorithm Based Query Expansion for Medical Web Information Retrieval.

PubMed

Khennak, Ilyes; Drias, Habiba

2017-02-01

With the increasing amount of medical data available on the Web, looking for health information has become one of the most widely searched topics on the Internet. Patients and people of several backgrounds are now using Web search engines to acquire medical information, including information about a specific disease, medical treatment or professional advice. Nonetheless, due to a lack of medical knowledge, many laypeople have difficulties in forming appropriate queries to articulate their inquiries, which deem their search queries to be imprecise due the use of unclear keywords. The use of these ambiguous and vague queries to describe the patients' needs has resulted in a failure of Web search engines to retrieve accurate and relevant information. One of the most natural and promising method to overcome this drawback is Query Expansion. In this paper, an original approach based on Bat Algorithm is proposed to improve the retrieval effectiveness of query expansion in medical field. In contrast to the existing literature, the proposed approach uses Bat Algorithm to find the best expanded query among a set of expanded query candidates, while maintaining low computational complexity. Moreover, this new approach allows the determination of the length of the expanded query empirically. Numerical results on MEDLINE, the on-line medical information database, show that the proposed approach is more effective and efficient compared to the baseline.

Automated mapping of clinical terms into SNOMED-CT. An application to codify procedures in pathology.

PubMed

Allones, J L; Martinez, D; Taboada, M

2014-10-01

Clinical terminologies are considered a key technology for capturing clinical data in a precise and standardized manner, which is critical to accurately exchange information among different applications, medical records and decision support systems. An important step to promote the real use of clinical terminologies, such as SNOMED-CT, is to facilitate the process of finding mappings between local terms of medical records and concepts of terminologies. In this paper, we propose a mapping tool to discover text-to-concept mappings in SNOMED-CT. Name-based techniques were combined with a query expansion system to generate alternative search terms, and with a strategy to analyze and take advantage of the semantic relationships of the SNOMED-CT concepts. The developed tool was evaluated and compared to the search services provided by two SNOMED-CT browsers. Our tool automatically mapped clinical terms from a Spanish glossary of procedures in pathology with 88.0% precision and 51.4% recall, providing a substantial improvement of recall (28% and 60%) over other publicly accessible mapping services. The improvements reached by the mapping tool are encouraging. Our results demonstrate the feasibility of accurately mapping clinical glossaries to SNOMED-CT concepts, by means a combination of structural, query expansion and named-based techniques. We have shown that SNOMED-CT is a great source of knowledge to infer synonyms for the medical domain. Results show that an automated query expansion system overcomes the challenge of vocabulary mismatch partially.

Building a Smart Portal for Astronomy

NASA Astrophysics Data System (ADS)

Derriere, S.; Boch, T.

2011-07-01

The development of a portal for accessing astronomical resources is not an easy task. The ever-increasing complexity of the data products can result in very complex user interfaces, requiring a lot of effort and learning from the user in order to perform searches. This is often a design choice, where the user must explicitly set many constraints, while the portal search logic remains simple. We investigated a different approach, where the query interface is kept as simple as possible (ideally, a simple text field, like for Google search), and the search logic is made much more complex to interpret the query in a relevant manner. We will present the implications of this approach in terms of interpretation and categorization of the query parameters (related to astronomical vocabularies), translation (mapping) of these concepts into the portal components metadata, identification of query schemes and use cases matching the input parameters, and delivery of query results to the user.

Searching Databases without Query-Building Aids: Implications for Dyslexic Users

ERIC Educational Resources Information Center

Berget, Gerd; Sandnes, Frode Eika

2015-01-01

Introduction: Few studies document the information searching behaviour of users with cognitive impairments. This paper therefore addresses the effect of dyslexia on information searching in a database with no tolerance for spelling errors and no query-building aids. The purpose was to identify effective search interface design guidelines that…

Hybrid Filtering in Semantic Query Processing

ERIC Educational Resources Information Center

Jeong, Hanjo

2011-01-01

This dissertation presents a hybrid filtering method and a case-based reasoning framework for enhancing the effectiveness of Web search. Web search may not reflect user needs, intent, context, and preferences, because today's keyword-based search is lacking semantic information to capture the user's context and intent in posing the search query.…

Asking better questions: How presentation formats influence information search.

PubMed

Wu, Charley M; Meder, Björn; Filimon, Flavia; Nelson, Jonathan D

2017-08-01

While the influence of presentation formats have been widely studied in Bayesian reasoning tasks, we present the first systematic investigation of how presentation formats influence information search decisions. Four experiments were conducted across different probabilistic environments, where subjects (N = 2,858) chose between 2 possible search queries, each with binary probabilistic outcomes, with the goal of maximizing classification accuracy. We studied 14 different numerical and visual formats for presenting information about the search environment, constructed across 6 design features that have been prominently related to improvements in Bayesian reasoning accuracy (natural frequencies, posteriors, complement, spatial extent, countability, and part-to-whole information). The posterior variants of the icon array and bar graph formats led to the highest proportion of correct responses, and were substantially better than the standard probability format. Results suggest that presenting information in terms of posterior probabilities and visualizing natural frequencies using spatial extent (a perceptual feature) were especially helpful in guiding search decisions, although environments with a mixture of probabilistic and certain outcomes were challenging across all formats. Subjects who made more accurate probability judgments did not perform better on the search task, suggesting that simple decision heuristics may be used to make search decisions without explicitly applying Bayesian inference to compute probabilities. We propose a new take-the-difference (TTD) heuristic that identifies the accuracy-maximizing query without explicit computation of posterior probabilities. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

SymDex: increasing the efficiency of chemical fingerprint similarity searches for comparing large chemical libraries by using query set indexing.

PubMed

Tai, David; Fang, Jianwen

2012-08-27

The large sizes of today's chemical databases require efficient algorithms to perform similarity searches. It can be very time consuming to compare two large chemical databases. This paper seeks to build upon existing research efforts by describing a novel strategy for accelerating existing search algorithms for comparing large chemical collections. The quest for efficiency has focused on developing better indexing algorithms by creating heuristics for searching individual chemical against a chemical library by detecting and eliminating needless similarity calculations. For comparing two chemical collections, these algorithms simply execute searches for each chemical in the query set sequentially. The strategy presented in this paper achieves a speedup upon these algorithms by indexing the set of all query chemicals so redundant calculations that arise in the case of sequential searches are eliminated. We implement this novel algorithm by developing a similarity search program called Symmetric inDexing or SymDex. SymDex shows over a 232% maximum speedup compared to the state-of-the-art single query search algorithm over real data for various fingerprint lengths. Considerable speedup is even seen for batch searches where query set sizes are relatively small compared to typical database sizes. To the best of our knowledge, SymDex is the first search algorithm designed specifically for comparing chemical libraries. It can be adapted to most, if not all, existing indexing algorithms and shows potential for accelerating future similarity search algorithms for comparing chemical databases.

Querying archetype-based EHRs by search ontology-based XPath engineering.

PubMed

Kropf, Stefan; Uciteli, Alexandr; Schierle, Katrin; Krücken, Peter; Denecke, Kerstin; Herre, Heinrich

2018-05-11

Legacy data and new structured data can be stored in a standardized format as XML-based EHRs on XML databases. Querying documents on these databases is crucial for answering research questions. Instead of using free text searches, that lead to false positive results, the precision can be increased by constraining the search to certain parts of documents. A search ontology-based specification of queries on XML documents defines search concepts and relates them to parts in the XML document structure. Such query specification method is practically introduced and evaluated by applying concrete research questions formulated in natural language on a data collection for information retrieval purposes. The search is performed by search ontology-based XPath engineering that reuses ontologies and XML-related W3C standards. The key result is that the specification of research questions can be supported by the usage of search ontology-based XPath engineering. A deeper recognition of entities and a semantic understanding of the content is necessary for a further improvement of precision and recall. Key limitation is that the application of the introduced process requires skills in ontology and software development. In future, the time consuming ontology development could be overcome by implementing a new clinical role: the clinical ontologist. The introduced Search Ontology XML extension connects Search Terms to certain parts in XML documents and enables an ontology-based definition of queries. Search ontology-based XPath engineering can support research question answering by the specification of complex XPath expressions without deep syntax knowledge about XPaths.

Query-by-example surgical activity detection.

PubMed

Gao, Yixin; Vedula, S Swaroop; Lee, Gyusung I; Lee, Mija R; Khudanpur, Sanjeev; Hager, Gregory D

2016-06-01

Easy acquisition of surgical data opens many opportunities to automate skill evaluation and teaching. Current technology to search tool motion data for surgical activity segments of interest is limited by the need for manual pre-processing, which can be prohibitive at scale. We developed a content-based information retrieval method, query-by-example (QBE), to automatically detect activity segments within surgical data recordings of long duration that match a query. The example segment of interest (query) and the surgical data recording (target trial) are time series of kinematics. Our approach includes an unsupervised feature learning module using a stacked denoising autoencoder (SDAE), two scoring modules based on asymmetric subsequence dynamic time warping (AS-DTW) and template matching, respectively, and a detection module. A distance matrix of the query against the trial is computed using the SDAE features, followed by AS-DTW combined with template scoring, to generate a ranked list of candidate subsequences (substrings). To evaluate the quality of the ranked list against the ground-truth, thresholding conventional DTW distances and bipartite matching are applied. We computed the recall, precision, F1-score, and a Jaccard index-based score on three experimental setups. We evaluated our QBE method using a suture throw maneuver as the query, on two tool motion datasets (JIGSAWS and MISTIC-SL) captured in a training laboratory. We observed a recall of 93, 90 and 87 % and a precision of 93, 91, and 88 % with same surgeon same trial (SSST), same surgeon different trial (SSDT) and different surgeon (DS) experiment setups on JIGSAWS, and a recall of 87, 81 and 75 % and a precision of 72, 61, and 53 % with SSST, SSDT and DS experiment setups on MISTIC-SL, respectively. We developed a novel, content-based information retrieval method to automatically detect multiple instances of an activity within long surgical recordings. Our method demonstrated adequate recall across different complexity datasets and experimental conditions.

Integrating unified medical language system and association mining techniques into relevance feedback for biomedical literature search.

PubMed

Ji, Yanqing; Ying, Hao; Tran, John; Dews, Peter; Massanari, R Michael

2016-07-19

Finding highly relevant articles from biomedical databases is challenging not only because it is often difficult to accurately express a user's underlying intention through keywords but also because a keyword-based query normally returns a long list of hits with many citations being unwanted by the user. This paper proposes a novel biomedical literature search system, called BiomedSearch, which supports complex queries and relevance feedback. The system employed association mining techniques to build a k-profile representing a user's relevance feedback. More specifically, we developed a weighted interest measure and an association mining algorithm to find the strength of association between a query and each concept in the article(s) selected by the user as feedback. The top concepts were utilized to form a k-profile used for the next-round search. BiomedSearch relies on Unified Medical Language System (UMLS) knowledge sources to map text files to standard biomedical concepts. It was designed to support queries with any levels of complexity. A prototype of BiomedSearch software was made and it was preliminarily evaluated using the Genomics data from TREC (Text Retrieval Conference) 2006 Genomics Track. Initial experiment results indicated that BiomedSearch increased the mean average precision (MAP) for a set of queries. With UMLS and association mining techniques, BiomedSearch can effectively utilize users' relevance feedback to improve the performance of biomedical literature search.

EasyKSORD: A Platform of Keyword Search Over Relational Databases

NASA Astrophysics Data System (ADS)

Peng, Zhaohui; Li, Jing; Wang, Shan

Keyword Search Over Relational Databases (KSORD) enables casual users to use keyword queries (a set of keywords) to search relational databases just like searching the Web, without any knowledge of the database schema or any need of writing SQL queries. Based on our previous work, we design and implement a novel KSORD platform named EasyKSORD for users and system administrators to use and manage different KSORD systems in a novel and simple manner. EasyKSORD supports advanced queries, efficient data-graph-based search engines, multiform result presentations, and system logging and analysis. Through EasyKSORD, users can search relational databases easily and read search results conveniently, and system administrators can easily monitor and analyze the operations of KSORD and manage KSORD systems much better.

How Do Children Reformulate Their Search Queries?

ERIC Educational Resources Information Center

Rutter, Sophie; Ford, Nigel; Clough, Paul

2015-01-01

Introduction: This paper investigates techniques used by children in year 4 (age eight to nine) of a UK primary school to reformulate their queries, and how they use information retrieval systems to support query reformulation. Method: An in-depth study analysing the interactions of twelve children carrying out search tasks in a primary school…

Cognitive search model and a new query paradigm

NASA Astrophysics Data System (ADS)

Xu, Zhonghui

2001-06-01

This paper proposes a cognitive model in which people begin to search pictures by using semantic content and find a right picture by judging whether its visual content is a proper visualization of the semantics desired. It is essential that human search is not just a process of matching computation on visual feature but rather a process of visualization of the semantic content known. For people to search electronic images in the way as they manually do in the model, we suggest that querying be a semantic-driven process like design. A query-by-design paradigm is prosed in the sense that what you design is what you find. Unlike query-by-example, query-by-design allows users to specify the semantic content through an iterative and incremental interaction process so that a retrieval can start with association and identification of the given semantic content and get refined while further visual cues are available. An experimental image retrieval system, Kuafu, has been under development using the query-by-design paradigm and an iconic language is adopted.

A Taxonomic Search Engine: Federating taxonomic databases using web services

PubMed Central

Page, Roderic DM

2005-01-01

Background The taxonomic name of an organism is a key link between different databases that store information on that organism. However, in the absence of a single, comprehensive database of organism names, individual databases lack an easy means of checking the correctness of a name. Furthermore, the same organism may have more than one name, and the same name may apply to more than one organism. Results The Taxonomic Search Engine (TSE) is a web application written in PHP that queries multiple taxonomic databases (ITIS, Index Fungorum, IPNI, NCBI, and uBIO) and summarises the results in a consistent format. It supports "drill-down" queries to retrieve a specific record. The TSE can optionally suggest alternative spellings the user can try. It also acts as a Life Science Identifier (LSID) authority for the source taxonomic databases, providing globally unique identifiers (and associated metadata) for each name. Conclusion The Taxonomic Search Engine is available at and provides a simple demonstration of the potential of the federated approach to providing access to taxonomic names. PMID:15757517

Manually Classifying User Search Queries on an Academic Library Web Site

ERIC Educational Resources Information Center

Chapman, Suzanne; Desai, Shevon; Hagedorn, Kat; Varnum, Ken; Mishra, Sonali; Piacentine, Julie

2013-01-01

The University of Michigan Library wanted to learn more about the kinds of searches its users were conducting through the "one search" search box on the Library Web site. Library staff conducted two investigations. A preliminary investigation in 2011 involved the manual review of the 100 most frequently occurring queries conducted…

GeoSearcher: Location-Based Ranking of Search Engine Results.

ERIC Educational Resources Information Center

Watters, Carolyn; Amoudi, Ghada

2003-01-01

Discussion of Web queries with geospatial dimensions focuses on an algorithm that assigns location coordinates dynamically to Web sites based on the URL. Describes a prototype search system that uses the algorithm to re-rank search engine results for queries with a geospatial dimension, thus providing an alternative ranking order for search engine…

An end user evaluation of query formulation and results review tools in three medical meta-search engines.

PubMed

Leroy, Gondy; Xu, Jennifer; Chung, Wingyan; Eggers, Shauna; Chen, Hsinchun

2007-01-01

Retrieving sufficient relevant information online is difficult for many people because they use too few keywords to search and search engines do not provide many support tools. To further complicate the search, users often ignore support tools when available. Our goal is to evaluate in a realistic setting when users use support tools and how they perceive these tools. We compared three medical search engines with support tools that require more or less effort from users to form a query and evaluate results. We carried out an end user study with 23 users who were asked to find information, i.e., subtopics and supporting abstracts, for a given theme. We used a balanced within-subjects design and report on the effectiveness, efficiency and usability of the support tools from the end user perspective. We found significant differences in efficiency but did not find significant differences in effectiveness between the three search engines. Dynamic user support tools requiring less effort led to higher efficiency. Fewer searches were needed and more documents were found per search when both query reformulation and result review tools dynamically adjust to the user query. The query reformulation tool that provided a long list of keywords, dynamically adjusted to the user query, was used most often and led to more subtopics. As hypothesized, the dynamic result review tools were used more often and led to more subtopics than static ones. These results were corroborated by the usability questionnaires, which showed that support tools that dynamically optimize output were preferred.

Biomedical Requirements for High Productivity Computing Systems

DTIC Science & Technology

2005-04-01

server at http://www.ncbi.nlm.nih.gov/BLAST/. There are many variants of BLAST, including: 1. BLASTN - Compares a DNA query to a DNA database. Searches ...database (3 reading frames from each strand of the DNA) searching . 13 4. TBLASTN - Compares a protein query to a DNA database, in the 6 possible...the molecular during this phase. After eliminating molecules that could not match the query , an atom-by-atom search for the molecules in conducted

From headache to tumour: An examination of health anxiety, health-related Internet use and 'query escalation'.

PubMed

Singh, Karmpaul; Brown, Richard J

2016-09-01

The current study aimed to explore the phenomenon of disease-related 'query escalation' in high/low health anxious Internet users (N = 40). During a 15-minute health-related Internet search, participants rated their anxiety and the perceived seriousness of information on each page. Post-search interviews determined the reasons for, and effects of, escalating queries to consider serious diseases. Both groups were found to be significantly more anxious after escalating queries. The high group was significantly more likely to escalate queries. Evaluating personal relevance of material was the main reason for escalations and moderated anxiety post-escalation. We conclude that searching for online disease information can increase anxiety, particularly for people worried about their health. © The Author(s) 2015.

Determination of geographic variance in stroke prevalence using Internet search engine analytics.

PubMed

Walcott, Brian P; Nahed, Brian V; Kahle, Kristopher T; Redjal, Navid; Coumans, Jean-Valery

2011-06-01

Previous methods to determine stroke prevalence, such as nationwide surveys, are labor-intensive endeavors. Recent advances in search engine query analytics have led to a new metric for disease surveillance to evaluate symptomatic phenomenon, such as influenza. The authors hypothesized that the use of search engine query data can determine the prevalence of stroke. The Google Insights for Search database was accessed to analyze anonymized search engine query data. The authors' search strategy utilized common search queries used when attempting either to identify the signs and symptoms of a stroke or to perform stroke education. The search logic was as follows: (stroke signs + stroke symptoms + mini stroke--heat) from January 1, 2005, to December 31, 2010. The relative number of searches performed (the interest level) for this search logic was established for all 50 states and the District of Columbia. A Pearson product-moment correlation coefficient was calculated from the statespecific stroke prevalence data previously reported. Web search engine interest level was available for all 50 states and the District of Columbia over the time period for January 1, 2005-December 31, 2010. The interest level was highest in Alabama and Tennessee (100 and 96, respectively) and lowest in California and Virginia (58 and 53, respectively). The Pearson correlation coefficient (r) was calculated to be 0.47 (p = 0.0005, 2-tailed). Search engine query data analysis allows for the determination of relative stroke prevalence. Further investigation will reveal the reliability of this metric to determine temporal pattern analysis and prevalence in this and other symptomatic diseases.

Accessing Suicide-Related Information on the Internet: A Retrospective Observational Study of Search Behavior

PubMed Central

2013-01-01

Background The Internet’s potential impact on suicide is of major public health interest as easy online access to pro-suicide information or specific suicide methods may increase suicide risk among vulnerable Internet users. Little is known, however, about users’ actual searching and browsing behaviors of online suicide-related information. Objective To investigate what webpages people actually clicked on after searching with suicide-related queries on a search engine and to examine what queries people used to get access to pro-suicide websites. Methods A retrospective observational study was done. We used a web search dataset released by America Online (AOL). The dataset was randomly sampled from all AOL subscribers’ web queries between March and May 2006 and generated by 657,000 service subscribers. Results We found 5526 search queries (0.026%, 5526/21,000,000) that included the keyword "suicide". The 5526 search queries included 1586 different search terms and were generated by 1625 unique subscribers (0.25%, 1625/657,000). Of these queries, 61.38% (3392/5526) were followed by users clicking on a search result. Of these 3392 queries, 1344 (39.62%) webpages were clicked on by 930 unique users but only 1314 of those webpages were accessible during the study period. Each clicked-through webpage was classified into 11 categories. The categories of the most visited webpages were: entertainment (30.13%; 396/1314), scientific information (18.31%; 240/1314), and community resources (14.53%; 191/1314). Among the 1314 accessed webpages, we could identify only two pro-suicide websites. We found that the search terms used to access these sites included “commiting suicide with a gas oven”, “hairless goat”, “pictures of murder by strangulation”, and “photo of a severe burn”. A limitation of our study is that the database may be dated and confined to mainly English webpages. Conclusions Searching or browsing suicide-related or pro-suicide webpages was uncommon, although a small group of users did access websites that contain detailed suicide method information. PMID:23305632

Drexel at TREC 2014 Federated Web Search Track

DTIC Science & Technology

2014-11-01

of its input RS results. 1. INTRODUCTION Federated Web Search is the task of searching multiple search engines simultaneously and combining their...or distributed properly[5]. The goal of RS is then, for a given query, to select only the most promising search engines from all those available. Most...result pages of 149 search engines . 4000 queries are used in building the sample set. As a part of the Vertical Selection task, search engines are

Adverse Reactions Associated With Cannabis Consumption as Evident From Search Engine Queries

PubMed Central

Lev-Ran, Shaul

2017-01-01

Background Cannabis is one of the most widely used psychoactive substances worldwide, but adverse drug reactions (ADRs) associated with its use are difficult to study because of its prohibited status in many countries. Objective Internet search engine queries have been used to investigate ADRs in pharmaceutical drugs. In this proof-of-concept study, we tested whether these queries can be used to detect the adverse reactions of cannabis use. Methods We analyzed anonymized queries from US-based users of Bing, a widely used search engine, made over a period of 6 months and compared the results with the prevalence of cannabis use as reported in the US National Survey on Drug Use in the Household (NSDUH) and with ADRs reported in the Food and Drug Administration’s Adverse Drug Reporting System. Predicted prevalence of cannabis use was estimated from the fraction of people making queries about cannabis, marijuana, and 121 additional synonyms. Predicted ADRs were estimated from queries containing layperson descriptions to 195 ICD-10 symptoms list. Results Our results indicated that the predicted prevalence of cannabis use at the US census regional level reaches an R2 of .71 NSDUH data. Queries for ADRs made by people who also searched for cannabis reveal many of the known adverse effects of cannabis (eg, cough and psychotic symptoms), as well as plausible unknown reactions (eg, pyrexia). Conclusions These results indicate that search engine queries can serve as an important tool for the study of adverse reactions of illicit drugs, which are difficult to study in other settings. PMID:29074469

Concept Based Tie-breaking and Maximal Marginal Relevance Retrieval in Microblog Retrieval

DTIC Science & Technology

2014-11-01

the same score, another singal will be used to rank these documents to break the ties , but the relative orders of other documents against these...documents remain the same. The tie- breaking step above is repeatedly applied to further break ties until all candidate signals are applied and the ranking...searched it on the Yahoo! search engine, which returned some query sug- gestions for the query. The original queries as well as their query suggestions

Which factors predict the time spent answering queries to a drug information centre?

PubMed Central

Reppe, Linda A.; Spigset, Olav

2010-01-01

Objective To develop a model based upon factors able to predict the time spent answering drug-related queries to Norwegian drug information centres (DICs). Setting and method Drug-related queries received at 5 DICs in Norway from March to May 2007 were randomly assigned to 20 employees until each of them had answered a minimum of five queries. The employees reported the number of drugs involved, the type of literature search performed, and whether the queries were considered judgmental or not, using a specifically developed scoring system. Main outcome measures The scores of these three factors were added together to define a workload score for each query. Workload and its individual factors were subsequently related to the measured time spent answering the queries by simple or multiple linear regression analyses. Results Ninety-six query/answer pairs were analyzed. Workload significantly predicted the time spent answering the queries (adjusted R2 = 0.22, P < 0.001). Literature search was the individual factor best predicting the time spent answering the queries (adjusted R2 = 0.17, P < 0.001), and this variable also contributed the most in the multiple regression analyses. Conclusion The most important workload factor predicting the time spent handling the queries in this study was the type of literature search that had to be performed. The categorisation of queries as judgmental or not, also affected the time spent answering the queries. The number of drugs involved did not significantly influence the time spent answering drug information queries. PMID:20922480

World Wide Web Metaphors for Search Mission Data

NASA Technical Reports Server (NTRS)

Norris, Jeffrey S.; Wallick, Michael N.; Joswig, Joseph C.; Powell, Mark W.; Torres, Recaredo J.; Mittman, David S.; Abramyan, Lucy; Crockett, Thomas M.; Shams, Khawaja S.; Fox, Jason M.;

Project Lefty: More Bang for the Search Query

ERIC Educational Resources Information Center

Varnum, Ken

2010-01-01

This article describes the Project Lefty, a search system that, at a minimum, adds a layer on top of traditional federated search tools that will make the wait for results more worthwhile for researchers. At best, Project Lefty improves search queries and relevance rankings for web-scale discovery tools to make the results themselves more relevant…

An Analysis of Web Image Queries for Search.

ERIC Educational Resources Information Center

Pu, Hsiao-Tieh

2003-01-01

Examines the differences between Web image and textual queries, and attempts to develop an analytic model to investigate their implications for Web image retrieval systems. Provides results that give insight into Web image searching behavior and suggests implications for improvement of current Web image search engines. (AEF)

Using Concept Relations to Improve Ranking in Information Retrieval

PubMed Central

Price, Susan L.; Delcambre, Lois M.

2005-01-01

Despite improved search engine technology, most searches return numerous documents not directly related to the query. This problem is mitigated if relevant documents appear high on a ranked list of search results. We propose that some queries and the underlying information needs can be modeled as relationships between concepts (relations), and we match relations in queries to relations in documents to try to improve ranking of search results. We investigate four techniques to identify two relationships important in medicine, causes and treats, to improve the ranking of medical text documents relevant to clinical questions about causation and treatment. Preliminary results suggest that identifying relation instances can improve the ranking of search results. PMID:16779114

BioCarian: search engine for exploratory searches in heterogeneous biological databases.

PubMed

Zaki, Nazar; Tennakoon, Chandana

2017-10-02

There are a large number of biological databases publicly available for scientists in the web. Also, there are many private databases generated in the course of research projects. These databases are in a wide variety of formats. Web standards have evolved in the recent times and semantic web technologies are now available to interconnect diverse and heterogeneous sources of data. Therefore, integration and querying of biological databases can be facilitated by techniques used in semantic web. Heterogeneous databases can be converted into Resource Description Format (RDF) and queried using SPARQL language. Searching for exact queries in these databases is trivial. However, exploratory searches need customized solutions, especially when multiple databases are involved. This process is cumbersome and time consuming for those without a sufficient background in computer science. In this context, a search engine facilitating exploratory searches of databases would be of great help to the scientific community. We present BioCarian, an efficient and user-friendly search engine for performing exploratory searches on biological databases. The search engine is an interface for SPARQL queries over RDF databases. We note that many of the databases can be converted to tabular form. We first convert the tabular databases to RDF. The search engine provides a graphical interface based on facets to explore the converted databases. The facet interface is more advanced than conventional facets. It allows complex queries to be constructed, and have additional features like ranking of facet values based on several criteria, visually indicating the relevance of a facet value and presenting the most important facet values when a large number of choices are available. For the advanced users, SPARQL queries can be run directly on the databases. Using this feature, users will be able to incorporate federated searches of SPARQL endpoints. We used the search engine to do an exploratory search on previously published viral integration data and were able to deduce the main conclusions of the original publication. BioCarian is accessible via http://www.biocarian.com . We have developed a search engine to explore RDF databases that can be used by both novice and advanced users.

Multi-INT Complex Event Processing using Approximate, Incremental Graph Pattern Search

DTIC Science & Technology

2012-06-01

graph pattern search and SPARQL queries . Total execution time for 10 executions each of 5 random pattern searches in synthetic data sets...01/11 1000 10000 100000 RDF triples Time (secs) 10 20 Graph pattern algorithm SPARQL queries Initial Performance Comparisons 09/18/11 2011 Thrust Area

System for Performing Single Query Searches of Heterogeneous and Dispersed Databases

NASA Technical Reports Server (NTRS)

Maluf, David A. (Inventor); Okimura, Takeshi (Inventor); Gurram, Mohana M. (Inventor); Tran, Vu Hoang (Inventor); Knight, Christopher D. (Inventor); Trinh, Anh Ngoc (Inventor)

2017-01-01

The present invention is a distributed computer system of heterogeneous databases joined in an information grid and configured with an Application Programming Interface hardware which includes a search engine component for performing user-structured queries on multiple heterogeneous databases in real time. This invention reduces overhead associated with the impedance mismatch that commonly occurs in heterogeneous database queries.

Using a terminology server and consumer search phrases to help patients find physicians with particular expertise.

PubMed

Cole, Curtis L; Kanter, Andrew S; Cummens, Michael; Vostinar, Sean; Naeymi-Rad, Frank

2004-01-01

To design and implement a real world application using a terminology server to assist patients and physicians who use common language search terms to find specialist physicians with a particular clinical expertise. Terminology servers have been developed to help users encoding of information using complicated structured vocabulary during data entry tasks, such as recording clinical information. We describe a methodology using Personal Health Terminology trade mark and a SNOMED CT-based hierarchical concept server. Construction of a pilot mediated-search engine to assist users who use vernacular speech in querying data which is more technical than vernacular. This approach, which combines theoretical and practical requirements, provides a useful example of concept-based searching for physician referrals.

Enhancing SAMOS Data Access in DOMS via a Neo4j Property Graph Database.

NASA Astrophysics Data System (ADS)

Stallard, A. P.; Smith, S. R.; Elya, J. L.

2016-12-01

The Shipboard Automated Meteorological and Oceanographic System (SAMOS) initiative provides routine access to high-quality marine meteorological and near-surface oceanographic observations from research vessels. The Distributed Oceanographic Match-Up Service (DOMS) under development is a centralized service that allows researchers to easily match in situ and satellite oceanographic data from distributed sources to facilitate satellite calibration, validation, and retrieval algorithm development. The service currently uses Apache Solr as a backend search engine on each node in the distributed network. While Solr is a high-performance solution that facilitates creation and maintenance of indexed data, it is limited in the sense that its schema is fixed. The property graph model escapes this limitation by creating relationships between data objects. The authors will present the development of the SAMOS Neo4j property graph database including new search possibilities that take advantage of the property graph model, performance comparisons with Apache Solr, and a vision for graph databases as a storage tool for oceanographic data. The integration of the SAMOS Neo4j graph into DOMS will also be described. Currently, Neo4j contains spatial and temporal records from SAMOS which are modeled into a time tree and r-tree using Graph Aware and Spatial plugin tools for Neo4j. These extensions provide callable Java procedures within CYPHER (Neo4j's query language) that generate in-graph structures. Once generated, these structures can be queried using procedures from these libraries, or directly via CYPHER statements. Neo4j excels at performing relationship and path-based queries, which challenge relational-SQL databases because they require memory intensive joins due to the limitation of their design. Consider a user who wants to find records over several years, but only for specific months. If a traditional database only stores timestamps, this type of query would be complex and likely prohibitively slow. Using the time tree model, one can specify a path from the root to the data which restricts resolutions to certain timeframes (e.g., months). This query can be executed without joins, unions, or other compute-intensive operations, putting Neo4j at a computational advantage to the SQL database alternative.

Adverse Reactions Associated With Cannabis Consumption as Evident From Search Engine Queries.

PubMed

Yom-Tov, Elad; Lev-Ran, Shaul

2017-10-26

Cannabis is one of the most widely used psychoactive substances worldwide, but adverse drug reactions (ADRs) associated with its use are difficult to study because of its prohibited status in many countries. Internet search engine queries have been used to investigate ADRs in pharmaceutical drugs. In this proof-of-concept study, we tested whether these queries can be used to detect the adverse reactions of cannabis use. We analyzed anonymized queries from US-based users of Bing, a widely used search engine, made over a period of 6 months and compared the results with the prevalence of cannabis use as reported in the US National Survey on Drug Use in the Household (NSDUH) and with ADRs reported in the Food and Drug Administration's Adverse Drug Reporting System. Predicted prevalence of cannabis use was estimated from the fraction of people making queries about cannabis, marijuana, and 121 additional synonyms. Predicted ADRs were estimated from queries containing layperson descriptions to 195 ICD-10 symptoms list. Our results indicated that the predicted prevalence of cannabis use at the US census regional level reaches an R 2 of .71 NSDUH data. Queries for ADRs made by people who also searched for cannabis reveal many of the known adverse effects of cannabis (eg, cough and psychotic symptoms), as well as plausible unknown reactions (eg, pyrexia). These results indicate that search engine queries can serve as an important tool for the study of adverse reactions of illicit drugs, which are difficult to study in other settings. ©Elad Yom-Tov, Shaul Lev-Ran. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 26.10.2017.

Improving Concept-Based Web Image Retrieval by Mixing Semantically Similar Greek Queries

ERIC Educational Resources Information Center

Lazarinis, Fotis

2008-01-01

Purpose: Image searching is a common activity for web users. Search engines offer image retrieval services based on textual queries. Previous studies have shown that web searching is more demanding when the search is not in English and does not use a Latin-based language. The aim of this paper is to explore the behaviour of the major search…

Query Transformations for Result Merging

DTIC Science & Technology

2014-11-01

tors, term dependence, query expansion 1. INTRODUCTION Federated search deals with the problem of aggregating results from multiple search engines . The...invidual search engines are (i) typically focused on a particular domain or a particular corpus, (ii) employ diverse retrieval models, and (iii...determine which search engines are appropri- ate for addressing the information need (resource selection), and (ii) merging the results returned by

G-Bean: an ontology-graph based web tool for biomedical literature retrieval

PubMed Central

2014-01-01

Background Currently, most people use NCBI's PubMed to search the MEDLINE database, an important bibliographical information source for life science and biomedical information. However, PubMed has some drawbacks that make it difficult to find relevant publications pertaining to users' individual intentions, especially for non-expert users. To ameliorate the disadvantages of PubMed, we developed G-Bean, a graph based biomedical search engine, to search biomedical articles in MEDLINE database more efficiently. Methods G-Bean addresses PubMed's limitations with three innovations: (1) Parallel document index creation: a multithreaded index creation strategy is employed to generate the document index for G-Bean in parallel; (2) Ontology-graph based query expansion: an ontology graph is constructed by merging four major UMLS (Version 2013AA) vocabularies, MeSH, SNOMEDCT, CSP and AOD, to cover all concepts in National Library of Medicine (NLM) database; a Personalized PageRank algorithm is used to compute concept relevance in this ontology graph and the Term Frequency - Inverse Document Frequency (TF-IDF) weighting scheme is used to re-rank the concepts. The top 500 ranked concepts are selected for expanding the initial query to retrieve more accurate and relevant information; (3) Retrieval and re-ranking of documents based on user's search intention: after the user selects any article from the existing search results, G-Bean analyzes user's selections to determine his/her true search intention and then uses more relevant and more specific terms to retrieve additional related articles. The new articles are presented to the user in the order of their relevance to the already selected articles. Results Performance evaluation with 106 OHSUMED benchmark queries shows that G-Bean returns more relevant results than PubMed does when using these queries to search the MEDLINE database. PubMed could not even return any search result for some OHSUMED queries because it failed to form the appropriate Boolean query statement automatically from the natural language query strings. G-Bean is available at http://bioinformatics.clemson.edu/G-Bean/index.php. Conclusions G-Bean addresses PubMed's limitations with ontology-graph based query expansion, automatic document indexing, and user search intention discovery. It shows significant advantages in finding relevant articles from the MEDLINE database to meet the information need of the user. PMID:25474588

G-Bean: an ontology-graph based web tool for biomedical literature retrieval.

PubMed

Wang, James Z; Zhang, Yuanyuan; Dong, Liang; Li, Lin; Srimani, Pradip K; Yu, Philip S

2014-01-01

Currently, most people use NCBI's PubMed to search the MEDLINE database, an important bibliographical information source for life science and biomedical information. However, PubMed has some drawbacks that make it difficult to find relevant publications pertaining to users' individual intentions, especially for non-expert users. To ameliorate the disadvantages of PubMed, we developed G-Bean, a graph based biomedical search engine, to search biomedical articles in MEDLINE database more efficiently. G-Bean addresses PubMed's limitations with three innovations: (1) Parallel document index creation: a multithreaded index creation strategy is employed to generate the document index for G-Bean in parallel; (2) Ontology-graph based query expansion: an ontology graph is constructed by merging four major UMLS (Version 2013AA) vocabularies, MeSH, SNOMEDCT, CSP and AOD, to cover all concepts in National Library of Medicine (NLM) database; a Personalized PageRank algorithm is used to compute concept relevance in this ontology graph and the Term Frequency - Inverse Document Frequency (TF-IDF) weighting scheme is used to re-rank the concepts. The top 500 ranked concepts are selected for expanding the initial query to retrieve more accurate and relevant information; (3) Retrieval and re-ranking of documents based on user's search intention: after the user selects any article from the existing search results, G-Bean analyzes user's selections to determine his/her true search intention and then uses more relevant and more specific terms to retrieve additional related articles. The new articles are presented to the user in the order of their relevance to the already selected articles. Performance evaluation with 106 OHSUMED benchmark queries shows that G-Bean returns more relevant results than PubMed does when using these queries to search the MEDLINE database. PubMed could not even return any search result for some OHSUMED queries because it failed to form the appropriate Boolean query statement automatically from the natural language query strings. G-Bean is available at http://bioinformatics.clemson.edu/G-Bean/index.php. G-Bean addresses PubMed's limitations with ontology-graph based query expansion, automatic document indexing, and user search intention discovery. It shows significant advantages in finding relevant articles from the MEDLINE database to meet the information need of the user.

USGS launches online database: Lichens in National Parks

USGS Publications Warehouse

Bennett, Jim

2005-01-01

If you are interested in lichens and National Parks, now you can query a lichen database that combines these two elements. Using pull-down menus you can: search by park, specifying either species list or the references used for that area; search by species (a report will show the parks in which species are found); and search by reference codes, which are available from the first query. The reference code search allows you to obtain the complete citation for each lichen species listed in a National Park.The result pages from these queries can be printed directly from the web browser, or can be copied and pasted into a word processor.

Improving accuracy for identifying related PubMed queries by an integrated approach.

PubMed

Lu, Zhiyong; Wilbur, W John

2009-10-01

PubMed is the most widely used tool for searching biomedical literature online. As with many other online search tools, a user often types a series of multiple related queries before retrieving satisfactory results to fulfill a single information need. Meanwhile, it is also a common phenomenon to see a user type queries on unrelated topics in a single session. In order to study PubMed users' search strategies, it is necessary to be able to automatically separate unrelated queries and group together related queries. Here, we report a novel approach combining both lexical and contextual analyses for segmenting PubMed query sessions and identifying related queries and compare its performance with the previous approach based solely on concept mapping. We experimented with our integrated approach on sample data consisting of 1539 pairs of consecutive user queries in 351 user sessions. The prediction results of 1396 pairs agreed with the gold-standard annotations, achieving an overall accuracy of 90.7%. This demonstrates that our approach is significantly better than the previously published method. By applying this approach to a one day query log of PubMed, we found that a significant proportion of information needs involved more than one PubMed query, and that most of the consecutive queries for the same information need are lexically related. Finally, the proposed PubMed distance is shown to be an accurate and meaningful measure for determining the contextual similarity between biological terms. The integrated approach can play a critical role in handling real-world PubMed query log data as is demonstrated in our experiments.

Improving accuracy for identifying related PubMed queries by an integrated approach

PubMed Central

Lu, Zhiyong; Wilbur, W. John

2009-01-01

PubMed is the most widely used tool for searching biomedical literature online. As with many other online search tools, a user often types a series of multiple related queries before retrieving satisfactory results to fulfill a single information need. Meanwhile, it is also a common phenomenon to see a user type queries on unrelated topics in a single session. In order to study PubMed users’ search strategies, it is necessary to be able to automatically separate unrelated queries and group together related queries. Here, we report a novel approach combining both lexical and contextual analyses for segmenting PubMed query sessions and identifying related queries and compare its performance with the previous approach based solely on concept mapping. We experimented with our integrated approach on sample data consisting of 1,539 pairs of consecutive user queries in 351 user sessions. The prediction results of 1,396 pairs agreed with the gold-standard annotations, achieving an overall accuracy of 90.7%. This demonstrates that our approach is significantly better than the previously published method. By applying this approach to a one day query log of PubMed, we found that a significant proportion of information needs involved more than one PubMed query, and that most of the consecutive queries for the same information need are lexically related. Finally, the proposed PubMed distance is shown to be an accurate and meaningful measure for determining the contextual similarity between biological terms. The integrated approach can play a critical role in handling real-world PubMed query log data as is demonstrated in our experiments. PMID:19162232

Fast Nonparametric Machine Learning Algorithms for High-Dimensional Massive Data and Applications

DTIC Science & Technology

2006-03-01

know the probability of that from Lemma 2. Using the union bound, we know that for any query q, the probability that i-am-feeling-lucky search algorithm...and each point in a d-dimensional space, a naive k-NN search needs to do a linear scan of T for every single query q, and thus the computational time...algorithm based on partition trees with priority search , and give an expected query time O((1/)d log n). But the constant in the O((1/)d log n

TokSearch: A search engine for fusion experimental data

DOE PAGES

Sammuli, Brian S.; Barr, Jayson L.; Eidietis, Nicholas W.; ...

2018-04-01

At a typical fusion research site, experimental data is stored using archive technologies that deal with each discharge as an independent set of data. These technologies (e.g. MDSplus or HDF5) are typically supplemented with a database that aggregates metadata for multiple shots to allow for efficient querying of certain predefined quantities. Often, however, a researcher will need to extract information from the archives, possibly for many shots, that is not available in the metadata store or otherwise indexed for quick retrieval. To address this need, a new search tool called TokSearch has been added to the General Atomics TokSys controlmore » design and analysis suite [1]. This tool provides the ability to rapidly perform arbitrary, parallelized queries of archived tokamak shot data (both raw and analyzed) over large numbers of shots. The TokSearch query API borrows concepts from SQL, and users can choose to implement queries in either MatlabTM or Python.« less

TokSearch: A search engine for fusion experimental data

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

Sammuli, Brian S.; Barr, Jayson L.; Eidietis, Nicholas W.

At a typical fusion research site, experimental data is stored using archive technologies that deal with each discharge as an independent set of data. These technologies (e.g. MDSplus or HDF5) are typically supplemented with a database that aggregates metadata for multiple shots to allow for efficient querying of certain predefined quantities. Often, however, a researcher will need to extract information from the archives, possibly for many shots, that is not available in the metadata store or otherwise indexed for quick retrieval. To address this need, a new search tool called TokSearch has been added to the General Atomics TokSys controlmore » design and analysis suite [1]. This tool provides the ability to rapidly perform arbitrary, parallelized queries of archived tokamak shot data (both raw and analyzed) over large numbers of shots. The TokSearch query API borrows concepts from SQL, and users can choose to implement queries in either MatlabTM or Python.« less

Evaluation of the Feasibility of Screening Patients for Early Signs of Lung Carcinoma in Web Search Logs.

PubMed

White, Ryen W; Horvitz, Eric

2017-03-01

A statistical model that predicts the appearance of strong evidence of a lung carcinoma diagnosis via analysis of large-scale anonymized logs of web search queries from millions of people across the United States. To evaluate the feasibility of screening patients at risk of lung carcinoma via analysis of signals from online search activity. We identified people who issue special queries that provide strong evidence of a recent diagnosis of lung carcinoma. We then considered patterns of symptoms expressed as searches about concerning symptoms over several months prior to the appearance of the landmark web queries. We built statistical classifiers that predict the future appearance of landmark queries based on the search log signals. This was a retrospective log analysis of the online activity of millions of web searchers seeking health-related information online. Of web searchers who queried for symptoms related to lung carcinoma, some (n = 5443 of 4 813 985) later issued queries that provide strong evidence of recent clinical diagnosis of lung carcinoma and are regarded as positive cases in our analysis. Additional evidence on the reliability of these queries as representing clinical diagnoses is based on the significant increase in follow-on searches for treatments and medications for these searchers and on the correlation between lung carcinoma incidence rates and our log-based statistics. The remaining symptom searchers (n = 4 808 542) are regarded as negative cases. Performance of the statistical model for early detection from online search behavior, for different lead times, different sets of signals, and different cohorts of searchers stratified by potential risk. The statistical classifier predicting the future appearance of landmark web queries based on search log signals identified searchers who later input queries consistent with a lung carcinoma diagnosis, with a true-positive rate ranging from 3% to 57% for false-positive rates ranging from 0.00001 to 0.001, respectively. The methods can be used to identify people at highest risk up to a year in advance of the inferred diagnosis time. The 5 factors associated with the highest relative risk (RR) were evidence of family history (RR = 7.548; 95% CI, 3.937-14.470), age (RR = 3.558; 95% CI, 3.357-3.772), radon (RR = 2.529; 95% CI, 1.137-5.624), primary location (RR = 2.463; 95% CI, 1.364-4.446), and occupation (RR = 1.969; 95% CI, 1.143-3.391). Evidence of smoking (RR = 1.646; 95% CI, 1.032-2.260) was important but not top-ranked, which was due to the difficulty of identifying smoking history from search terms. Pattern recognition based on data drawn from large-scale web search queries holds opportunity for identifying risk factors and frames new directions with early detection of lung carcinoma.

DOE Research and Development Accomplishments Website Policies/Important

Science.gov Websites

Links RSS Archive Videos XML DOE R&D Accomplishments DOE R&D Accomplishments searchQuery × Find searchQuery x Find DOE R&D Acccomplishments Navigation dropdown arrow The Basics Stories Snapshots R&D Nuggets Database dropdown arrow Search Tag Cloud Browse Reports Database Help

Exploration of Web Users' Search Interests through Automatic Subject Categorization of Query Terms.

ERIC Educational Resources Information Center

Pu, Hsiao-tieh; Yang, Chyan; Chuang, Shui-Lung

2001-01-01

Proposes a mechanism that carefully integrates human and machine efforts to explore Web users' search interests. The approach consists of a four-step process: extraction of core terms; construction of subject taxonomy; automatic subject categorization of query terms; and observation of users' search interests. Research findings are proved valuable…

Design of an On-Line Query Language for Full Text Patent Search.

ERIC Educational Resources Information Center

Glantz, Richard S.

The design of an English-like query language and an interactive computer environment for searching the full text of the U.S. patent collection are discussed. Special attention is paid to achieving a transparent user interface, to providing extremely broad search capabilities (including nested substitution classes, Kleene star events, and domain…

Automatic query formulations in information retrieval.

PubMed

Salton, G; Buckley, C; Fox, E A

1983-07-01

Modern information retrieval systems are designed to supply relevant information in response to requests received from the user population. In most retrieval environments the search requests consist of keywords, or index terms, interrelated by appropriate Boolean operators. Since it is difficult for untrained users to generate effective Boolean search requests, trained search intermediaries are normally used to translate original statements of user need into useful Boolean search formulations. Methods are introduced in this study which reduce the role of the search intermediaries by making it possible to generate Boolean search formulations completely automatically from natural language statements provided by the system patrons. Frequency considerations are used automatically to generate appropriate term combinations as well as Boolean connectives relating the terms. Methods are covered to produce automatic query formulations both in a standard Boolean logic system, as well as in an extended Boolean system in which the strict interpretation of the connectives is relaxed. Experimental results are supplied to evaluate the effectiveness of the automatic query formulation process, and methods are described for applying the automatic query formulation process in practice.

VisGets: coordinated visualizations for web-based information exploration and discovery.

PubMed

Dörk, Marian; Carpendale, Sheelagh; Collins, Christopher; Williamson, Carey

2008-01-01

In common Web-based search interfaces, it can be difficult to formulate queries that simultaneously combine temporal, spatial, and topical data filters. We investigate how coordinated visualizations can enhance search and exploration of information on the World Wide Web by easing the formulation of these types of queries. Drawing from visual information seeking and exploratory search, we introduce VisGets--interactive query visualizations of Web-based information that operate with online information within a Web browser. VisGets provide the information seeker with visual overviews of Web resources and offer a way to visually filter the data. Our goal is to facilitate the construction of dynamic search queries that combine filters from more than one data dimension. We present a prototype information exploration system featuring three linked VisGets (temporal, spatial, and topical), and used it to visually explore news items from online RSS feeds.

SemanticFind: Locating What You Want in a Patient Record, Not Just What You Ask For

PubMed Central

Prager, John M.; Liang, Jennifer J.; Devarakonda, Murthy V.

2017-01-01

We present a new model of patient record search, called SemanticFind, which goes beyond traditional textual and medical synonym matches by locating patient data that a clinician would want to see rather than just what they ask for. The new model is implemented by making extensive use of the UMLS semantic network, distributional semantics, and NLP, to match query terms along several dimensions in a patient record with the returned matches organized accordingly. The new approach finds all clinically related concepts without the user having to ask for them. An evaluation of the accuracy of SemanticFind shows that it found twice as many relevant matches compared to those found by literal (traditional) search alone, along with very high precision and recall. These results suggest potential uses for SemanticFind in clinical practice, retrospective chart reviews, and in automated extraction of quality metrics. PMID:28815139

New Quality Metrics for Web Search Results

NASA Astrophysics Data System (ADS)

Metaxas, Panagiotis Takis; Ivanova, Lilia; Mustafaraj, Eni

Web search results enjoy an increasing importance in our daily lives. But what can be said about their quality, especially when querying a controversial issue? The traditional information retrieval metrics of precision and recall do not provide much insight in the case of web information retrieval. In this paper we examine new ways of evaluating quality in search results: coverage and independence. We give examples on how these new metrics can be calculated and what their values reveal regarding the two major search engines, Google and Yahoo. We have found evidence of low coverage for commercial and medical controversial queries, and high coverage for a political query that is highly contested. Given the fact that search engines are unwilling to tune their search results manually, except in a few cases that have become the source of bad publicity, low coverage and independence reveal the efforts of dedicated groups to manipulate the search results.

search GenBank: interactive orchestration and ad-hoc choreography of Web services in the exploration of the biomedical resources of the National Center For Biotechnology Information

PubMed Central

2013-01-01

Background Due to the growing number of biomedical entries in data repositories of the National Center for Biotechnology Information (NCBI), it is difficult to collect, manage and process all of these entries in one place by third-party software developers without significant investment in hardware and software infrastructure, its maintenance and administration. Web services allow development of software applications that integrate in one place the functionality and processing logic of distributed software components, without integrating the components themselves and without integrating the resources to which they have access. This is achieved by appropriate orchestration or choreography of available Web services and their shared functions. After the successful application of Web services in the business sector, this technology can now be used to build composite software tools that are oriented towards biomedical data processing. Results We have developed a new tool for efficient and dynamic data exploration in GenBank and other NCBI databases. A dedicated search GenBank system makes use of NCBI Web services and a package of Entrez Programming Utilities (eUtils) in order to provide extended searching capabilities in NCBI data repositories. In search GenBank users can use one of the three exploration paths: simple data searching based on the specified user’s query, advanced data searching based on the specified user’s query, and advanced data exploration with the use of macros. search GenBank orchestrates calls of particular tools available through the NCBI Web service providing requested functionality, while users interactively browse selected records in search GenBank and traverse between NCBI databases using available links. On the other hand, by building macros in the advanced data exploration mode, users create choreographies of eUtils calls, which can lead to the automatic discovery of related data in the specified databases. Conclusions search GenBank extends standard capabilities of the NCBI Entrez search engine in querying biomedical databases. The possibility of creating and saving macros in the search GenBank is a unique feature and has a great potential. The potential will further grow in the future with the increasing density of networks of relationships between data stored in particular databases. search GenBank is available for public use at http://sgb.biotools.pl/. PMID:23452691

search GenBank: interactive orchestration and ad-hoc choreography of Web services in the exploration of the biomedical resources of the National Center For Biotechnology Information.

PubMed

Mrozek, Dariusz; Małysiak-Mrozek, Bożena; Siążnik, Artur

2013-03-01

Due to the growing number of biomedical entries in data repositories of the National Center for Biotechnology Information (NCBI), it is difficult to collect, manage and process all of these entries in one place by third-party software developers without significant investment in hardware and software infrastructure, its maintenance and administration. Web services allow development of software applications that integrate in one place the functionality and processing logic of distributed software components, without integrating the components themselves and without integrating the resources to which they have access. This is achieved by appropriate orchestration or choreography of available Web services and their shared functions. After the successful application of Web services in the business sector, this technology can now be used to build composite software tools that are oriented towards biomedical data processing. We have developed a new tool for efficient and dynamic data exploration in GenBank and other NCBI databases. A dedicated search GenBank system makes use of NCBI Web services and a package of Entrez Programming Utilities (eUtils) in order to provide extended searching capabilities in NCBI data repositories. In search GenBank users can use one of the three exploration paths: simple data searching based on the specified user's query, advanced data searching based on the specified user's query, and advanced data exploration with the use of macros. search GenBank orchestrates calls of particular tools available through the NCBI Web service providing requested functionality, while users interactively browse selected records in search GenBank and traverse between NCBI databases using available links. On the other hand, by building macros in the advanced data exploration mode, users create choreographies of eUtils calls, which can lead to the automatic discovery of related data in the specified databases. search GenBank extends standard capabilities of the NCBI Entrez search engine in querying biomedical databases. The possibility of creating and saving macros in the search GenBank is a unique feature and has a great potential. The potential will further grow in the future with the increasing density of networks of relationships between data stored in particular databases. search GenBank is available for public use at http://sgb.biotools.pl/.

Visual graph query formulation and exploration: a new perspective on information retrieval at the edge

NASA Astrophysics Data System (ADS)

Kase, Sue E.; Vanni, Michelle; Knight, Joanne A.; Su, Yu; Yan, Xifeng

2016-05-01

Within operational environments decisions must be made quickly based on the information available. Identifying an appropriate knowledge base and accurately formulating a search query are critical tasks for decision-making effectiveness in dynamic situations. The spreading of graph data management tools to access large graph databases is a rapidly emerging research area of potential benefit to the intelligence community. A graph representation provides a natural way of modeling data in a wide variety of domains. Graph structures use nodes, edges, and properties to represent and store data. This research investigates the advantages of information search by graph query initiated by the analyst and interactively refined within the contextual dimensions of the answer space toward a solution. The paper introduces SLQ, a user-friendly graph querying system enabling the visual formulation of schemaless and structureless graph queries. SLQ is demonstrated with an intelligence analyst information search scenario focused on identifying individuals responsible for manufacturing a mosquito-hosted deadly virus. The scenario highlights the interactive construction of graph queries without prior training in complex query languages or graph databases, intuitive navigation through the problem space, and visualization of results in graphical format.

Forecasting influenza in Hong Kong with Google search queries and statistical model fusion.

PubMed

Xu, Qinneng; Gel, Yulia R; Ramirez Ramirez, L Leticia; Nezafati, Kusha; Zhang, Qingpeng; Tsui, Kwok-Leung

2017-01-01

The objective of this study is to investigate predictive utility of online social media and web search queries, particularly, Google search data, to forecast new cases of influenza-like-illness (ILI) in general outpatient clinics (GOPC) in Hong Kong. To mitigate the impact of sensitivity to self-excitement (i.e., fickle media interest) and other artifacts of online social media data, in our approach we fuse multiple offline and online data sources. Four individual models: generalized linear model (GLM), least absolute shrinkage and selection operator (LASSO), autoregressive integrated moving average (ARIMA), and deep learning (DL) with Feedforward Neural Networks (FNN) are employed to forecast ILI-GOPC both one week and two weeks in advance. The covariates include Google search queries, meteorological data, and previously recorded offline ILI. To our knowledge, this is the first study that introduces deep learning methodology into surveillance of infectious diseases and investigates its predictive utility. Furthermore, to exploit the strength from each individual forecasting models, we use statistical model fusion, using Bayesian model averaging (BMA), which allows a systematic integration of multiple forecast scenarios. For each model, an adaptive approach is used to capture the recent relationship between ILI and covariates. DL with FNN appears to deliver the most competitive predictive performance among the four considered individual models. Combing all four models in a comprehensive BMA framework allows to further improve such predictive evaluation metrics as root mean squared error (RMSE) and mean absolute predictive error (MAPE). Nevertheless, DL with FNN remains the preferred method for predicting locations of influenza peaks. The proposed approach can be viewed a feasible alternative to forecast ILI in Hong Kong or other countries where ILI has no constant seasonal trend and influenza data resources are limited. The proposed methodology is easily tractable and computationally efficient.

Cafe Variome: general-purpose software for making genotype-phenotype data discoverable in restricted or open access contexts.

PubMed

Lancaster, Owen; Beck, Tim; Atlan, David; Swertz, Morris; Thangavelu, Dhiwagaran; Veal, Colin; Dalgleish, Raymond; Brookes, Anthony J

2015-10-01

Biomedical data sharing is desirable, but problematic. Data "discovery" approaches-which establish the existence rather than the substance of data-precisely connect data owners with data seekers, and thereby promote data sharing. Cafe Variome (http://www.cafevariome.org) was therefore designed to provide a general-purpose, Web-based, data discovery tool that can be quickly installed by any genotype-phenotype data owner, or network of data owners, to make safe or sensitive content appropriately discoverable. Data fields or content of any type can be accommodated, from simple ID and label fields through to extensive genotype and phenotype details based on ontologies. The system provides a "shop window" in front of data, with main interfaces being a simple search box and a powerful "query-builder" that enable very elaborate queries to be formulated. After a successful search, counts of records are reported grouped by "openAccess" (data may be directly accessed), "linkedAccess" (a source link is provided), and "restrictedAccess" (facilitated data requests and subsequent provision of approved records). An administrator interface provides a wide range of options for system configuration, enabling highly customized single-site or federated networks to be established. Current uses include rare disease data discovery, patient matchmaking, and a Beacon Web service. © 2015 WILEY PERIODICALS, INC.

Net Improvement of Correct Answers to Therapy Questions After PubMed Searches: Pre/Post Comparison

PubMed Central

Keepanasseril, Arun

2013-01-01

Background Clinicians search PubMed for answers to clinical questions although it is time consuming and not always successful. Objective To determine if PubMed used with its Clinical Queries feature to filter results based on study quality would improve search success (more correct answers to clinical questions related to therapy). Methods We invited 528 primary care physicians to participate, 143 (27.1%) consented, and 111 (21.0% of the total and 77.6% of those who consented) completed the study. Participants answered 14 yes/no therapy questions and were given 4 of these (2 originally answered correctly and 2 originally answered incorrectly) to search using either the PubMed main screen or PubMed Clinical Queries narrow therapy filter via a purpose-built system with identical search screens. Participants also picked 3 of the first 20 retrieved citations that best addressed each question. They were then asked to re-answer the original 14 questions. Results We found no statistically significant differences in the rates of correct or incorrect answers using the PubMed main screen or PubMed Clinical Queries. The rate of correct answers increased from 50.0% to 61.4% (95% CI 55.0%-67.8%) for the PubMed main screen searches and from 50.0% to 59.1% (95% CI 52.6%-65.6%) for Clinical Queries searches. These net absolute increases of 11.4% and 9.1%, respectively, included previously correct answers changing to incorrect at a rate of 9.5% (95% CI 5.6%-13.4%) for PubMed main screen searches and 9.1% (95% CI 5.3%-12.9%) for Clinical Queries searches, combined with increases in the rate of being correct of 20.5% (95% CI 15.2%-25.8%) for PubMed main screen searches and 17.7% (95% CI 12.7%-22.7%) for Clinical Queries searches. Conclusions PubMed can assist clinicians answering clinical questions with an approximately 10% absolute rate of improvement in correct answers. This small increase includes more correct answers partially offset by a decrease in previously correct answers. PMID:24217329

Net improvement of correct answers to therapy questions after pubmed searches: pre/post comparison.

PubMed

McKibbon, Kathleen Ann; Lokker, Cynthia; Keepanasseril, Arun; Wilczynski, Nancy L; Haynes, R Brian

2013-11-08

Clinicians search PubMed for answers to clinical questions although it is time consuming and not always successful. To determine if PubMed used with its Clinical Queries feature to filter results based on study quality would improve search success (more correct answers to clinical questions related to therapy). We invited 528 primary care physicians to participate, 143 (27.1%) consented, and 111 (21.0% of the total and 77.6% of those who consented) completed the study. Participants answered 14 yes/no therapy questions and were given 4 of these (2 originally answered correctly and 2 originally answered incorrectly) to search using either the PubMed main screen or PubMed Clinical Queries narrow therapy filter via a purpose-built system with identical search screens. Participants also picked 3 of the first 20 retrieved citations that best addressed each question. They were then asked to re-answer the original 14 questions. We found no statistically significant differences in the rates of correct or incorrect answers using the PubMed main screen or PubMed Clinical Queries. The rate of correct answers increased from 50.0% to 61.4% (95% CI 55.0%-67.8%) for the PubMed main screen searches and from 50.0% to 59.1% (95% CI 52.6%-65.6%) for Clinical Queries searches. These net absolute increases of 11.4% and 9.1%, respectively, included previously correct answers changing to incorrect at a rate of 9.5% (95% CI 5.6%-13.4%) for PubMed main screen searches and 9.1% (95% CI 5.3%-12.9%) for Clinical Queries searches, combined with increases in the rate of being correct of 20.5% (95% CI 15.2%-25.8%) for PubMed main screen searches and 17.7% (95% CI 12.7%-22.7%) for Clinical Queries searches. PubMed can assist clinicians answering clinical questions with an approximately 10% absolute rate of improvement in correct answers. This small increase includes more correct answers partially offset by a decrease in previously correct answers.

DOE Research and Development Accomplishments Nobel Chemists Associated with

Science.gov Websites

the DOE and Predecessors RSS Archive Videos XML DOE R&D Accomplishments DOE R&D Accomplishments searchQuery × Find searchQuery x Find DOE R&D Acccomplishments Navigation dropdown arrow The Blog Archive SC Stories Snapshots R&D Nuggets Database dropdown arrow Search Tag Cloud Browse

A novel adaptive Cuckoo search for optimal query plan generation.

PubMed

Gomathi, Ramalingam; Sharmila, Dhandapani

2014-01-01

The emergence of multiple web pages day by day leads to the development of the semantic web technology. A World Wide Web Consortium (W3C) standard for storing semantic web data is the resource description framework (RDF). To enhance the efficiency in the execution time for querying large RDF graphs, the evolving metaheuristic algorithms become an alternate to the traditional query optimization methods. This paper focuses on the problem of query optimization of semantic web data. An efficient algorithm called adaptive Cuckoo search (ACS) for querying and generating optimal query plan for large RDF graphs is designed in this research. Experiments were conducted on different datasets with varying number of predicates. The experimental results have exposed that the proposed approach has provided significant results in terms of query execution time. The extent to which the algorithm is efficient is tested and the results are documented.

Predicting Drug Recalls From Internet Search Engine Queries.

PubMed

Yom-Tov, Elad

2017-01-01

Batches of pharmaceuticals are sometimes recalled from the market when a safety issue or a defect is detected in specific production runs of a drug. Such problems are usually detected when patients or healthcare providers report abnormalities to medical authorities. Here, we test the hypothesis that defective production lots can be detected earlier by monitoring queries to Internet search engines. We extracted queries from the USA to the Bing search engine, which mentioned one of the 5195 pharmaceutical drugs during 2015 and all recall notifications issued by the Food and Drug Administration (FDA) during that year. By using attributes that quantify the change in query volume at the state level, we attempted to predict if a recall of a specific drug will be ordered by FDA in a time horizon ranging from 1 to 40 days in future. Our results show that future drug recalls can indeed be identified with an AUC of 0.791 and a lift at 5% of approximately 6 when predicting a recall occurring one day ahead. This performance degrades as prediction is made for longer periods ahead. The most indicative attributes for prediction are sudden spikes in query volume about a specific medicine in each state. Recalls of prescription drugs and those estimated to be of medium-risk are more likely to be identified using search query data. These findings suggest that aggregated Internet search engine data can be used to facilitate in early warning of faulty batches of medicines.

Adaptive search in mobile peer-to-peer databases

NASA Technical Reports Server (NTRS)

Wolfson, Ouri (Inventor); Xu, Bo (Inventor)

2010-01-01

Information is stored in a plurality of mobile peers. The peers communicate in a peer to peer fashion, using a short-range wireless network. Occasionally, a peer initiates a search for information in the peer to peer network by issuing a query. Queries and pieces of information, called reports, are transmitted among peers that are within a transmission range. For each search additional peers are utilized, wherein these additional peers search and relay information on behalf of the originator of the search.

Automatic Query Formulations in Information Retrieval.

ERIC Educational Resources Information Center

Salton, G.; And Others

1983-01-01

Introduces methods designed to reduce role of search intermediaries by generating Boolean search formulations automatically using term frequency considerations from natural language statements provided by system patrons. Experimental results are supplied and methods are described for applying automatic query formulation process in practice.…

Generating Personalized Web Search Using Semantic Context

PubMed Central

Xu, Zheng; Chen, Hai-Yan; Yu, Jie

2015-01-01

The “one size fits the all” criticism of search engines is that when queries are submitted, the same results are returned to different users. In order to solve this problem, personalized search is proposed, since it can provide different search results based upon the preferences of users. However, existing methods concentrate more on the long-term and independent user profile, and thus reduce the effectiveness of personalized search. In this paper, the method captures the user context to provide accurate preferences of users for effectively personalized search. First, the short-term query context is generated to identify related concepts of the query. Second, the user context is generated based on the click through data of users. Finally, a forgetting factor is introduced to merge the independent user context in a user session, which maintains the evolution of user preferences. Experimental results fully confirm that our approach can successfully represent user context according to individual user information needs. PMID:26000335

Seasonal trends in tinnitus symptomatology: evidence from Internet search engine query data.

PubMed

Plante, David T; Ingram, David G

2015-10-01

The primary aim of this study was to test the hypothesis that the symptom of tinnitus demonstrates a seasonal pattern with worsening in the winter relative to the summer using Internet search engine query data. Normalized search volume for the term 'tinnitus' from January 2004 through December 2013 was retrieved from Google Trends. Seasonal effects were evaluated using cosinor regression models. Primary countries of interest were the United States and Australia. Secondary exploratory analyses were also performed using data from Germany, the United Kingdom, Canada, Sweden, and Switzerland. Significant seasonal effects for 'tinnitus' search queries were found in the United States and Australia (p < 0.00001 for both countries), with peaks in the winter and troughs in the summer. Secondary analyses demonstrated similarly significant seasonal effects for Germany (p < 0.00001), Canada (p < 0.00001), and Sweden (p = 0.0008), again with increased search volume in the winter relative to the summer. Our findings indicate that there are significant seasonal trends for Internet search queries for tinnitus, with a zenith in winter months. Further research is indicated to determine the biological mechanisms underlying these findings, as they may provide insights into the pathophysiology of this common and debilitating medical symptom.

An intuitive graphical webserver for multiple-choice protein sequence search.

PubMed

Banky, Daniel; Szalkai, Balazs; Grolmusz, Vince

2014-04-10

Every day tens of thousands of sequence searches and sequence alignment queries are submitted to webservers. The capitalized word "BLAST" becomes a verb, describing the act of performing sequence search and alignment. However, if one needs to search for sequences that contain, for example, two hydrophobic and three polar residues at five given positions, the query formation on the most frequently used webservers will be difficult. Some servers support the formation of queries with regular expressions, but most of the users are unfamiliar with their syntax. Here we present an intuitive, easily applicable webserver, the Protein Sequence Analysis server, that allows the formation of multiple choice queries by simply drawing the residues to their positions; if more than one residue are drawn to the same position, then they will be nicely stacked on the user interface, indicating the multiple choice at the given position. This computer-game-like interface is natural and intuitive, and the coloring of the residues makes possible to form queries requiring not just certain amino acids in the given positions, but also small nonpolar, negatively charged, hydrophobic, positively charged, or polar ones. The webserver is available at http://psa.pitgroup.org. Copyright © 2014 Elsevier B.V. All rights reserved.

Sundanese ancient manuscripts search engine using probability approach

NASA Astrophysics Data System (ADS)

Suryani, Mira; Hadi, Setiawan; Paulus, Erick; Nurma Yulita, Intan; Supriatna, Asep K.

2017-10-01

Today, Information and Communication Technology (ICT) has become a regular thing for every aspect of live include cultural and heritage aspect. Sundanese ancient manuscripts as Sundanese heritage are in damage condition and also the information that containing on it. So in order to preserve the information in Sundanese ancient manuscripts and make them easier to search, a search engine has been developed. The search engine must has good computing ability. In order to get the best computation in developed search engine, three types of probabilistic approaches: Bayesian Networks Model, Divergence from Randomness with PL2 distribution, and DFR-PL2F as derivative form DFR-PL2 have been compared in this study. The three probabilistic approaches supported by index of documents and three different weighting methods: term occurrence, term frequency, and TF-IDF. The experiment involved 12 Sundanese ancient manuscripts. From 12 manuscripts there are 474 distinct terms. The developed search engine tested by 50 random queries for three types of query. The experiment results showed that for the single query and multiple query, the best searching performance given by the combination of PL2F approach and TF-IDF weighting method. The performance has been evaluated using average time responds with value about 0.08 second and Mean Average Precision (MAP) about 0.33.

28 CFR 25.7 - Querying records in the system.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 28 Judicial Administration 1 2010-07-01 2010-07-01 false Querying records in the system. 25.7 Section 25.7 Judicial Administration DEPARTMENT OF JUSTICE DEPARTMENT OF JUSTICE INFORMATION SYSTEMS The National Instant Criminal Background Check System § 25.7 Querying records in the system. (a) The following...

Monitoring Influenza Epidemics in China with Search Query from Baidu

PubMed Central

Lv, Benfu; Peng, Geng; Chunara, Rumi; Brownstein, John S.

2013-01-01

Several approaches have been proposed for near real-time detection and prediction of the spread of influenza. These include search query data for influenza-related terms, which has been explored as a tool for augmenting traditional surveillance methods. In this paper, we present a method that uses Internet search query data from Baidu to model and monitor influenza activity in China. The objectives of the study are to present a comprehensive technique for: (i) keyword selection, (ii) keyword filtering, (iii) index composition and (iv) modeling and detection of influenza activity in China. Sequential time-series for the selected composite keyword index is significantly correlated with Chinese influenza case data. In addition, one-month ahead prediction of influenza cases for the first eight months of 2012 has a mean absolute percent error less than 11%. To our knowledge, this is the first study on the use of search query data from Baidu in conjunction with this approach for estimation of influenza activity in China. PMID:23750192

Information Retrieval Using UMLS-based Structured Queries

PubMed Central

Fagan, Lawrence M.; Berrios, Daniel C.; Chan, Albert; Cucina, Russell; Datta, Anupam; Shah, Maulik; Surendran, Sujith

2001-01-01

During the last three years, we have developed and described components of ELBook, a semantically based information-retrieval system [1-4]. Using these components, domain experts can specify a query model, indexers can use the query model to index documents, and end-users can search these documents for instances of indexed queries.

Federated Space-Time Query for Earth Science Data Using OpenSearch Conventions

NASA Technical Reports Server (NTRS)

Lynnes, Chris; Beaumont, Bruce; Duerr, Ruth; Hua, Hook

2009-01-01

This slide presentation reviews a Space-time query system that has been developed to assist the user in finding Earth science data that fulfills the researchers needs. It reviews the reasons why finding Earth science data can be so difficult, and explains the workings of the Space-Time Query with OpenSearch and how this system can assist researchers in finding the required data, It also reviews the developments with client server systems.

Examining the Relationship Between Past Orientation and US Suicide Rates: An Analysis Using Big Data-Driven Google Search Queries

PubMed Central

Lee, Donghyun; Lee, Hojun

2016-01-01

Background Internet search query data reflect the attitudes of the users, using which we can measure the past orientation to commit suicide. Examinations of past orientation often highlight certain predispositions of attitude, many of which can be suicide risk factors. Objective To investigate the relationship between past orientation and suicide rate by examining Google search queries. Methods We measured the past orientation using Google search query data by comparing the search volumes of the past year and those of the future year, across the 50 US states and the District of Columbia during the period from 2004 to 2012. We constructed a panel dataset with independent variables as control variables; we then undertook an analysis using multiple ordinary least squares regression and methods that leverage the Akaike information criterion and the Bayesian information criterion. Results It was found that past orientation had a positive relationship with the suicide rate (P≤.001) and that it improves the goodness-of-fit of the model regarding the suicide rate. Unemployment rate (P≤.001 in Models 3 and 4), Gini coefficient (P≤.001), and population growth rate (P≤.001) had a positive relationship with the suicide rate, whereas the gross state product (P≤.001) showed a negative relationship with the suicide rate. Conclusions We empirically identified the positive relationship between the suicide rate and past orientation, which was measured by big data-driven Google search query. PMID:26868917

Examining the Relationship Between Past Orientation and US Suicide Rates: An Analysis Using Big Data-Driven Google Search Queries.

PubMed

Lee, Donghyun; Lee, Hojun; Choi, Munkee

2016-02-11

Internet search query data reflect the attitudes of the users, using which we can measure the past orientation to commit suicide. Examinations of past orientation often highlight certain predispositions of attitude, many of which can be suicide risk factors. To investigate the relationship between past orientation and suicide rate by examining Google search queries. We measured the past orientation using Google search query data by comparing the search volumes of the past year and those of the future year, across the 50 US states and the District of Columbia during the period from 2004 to 2012. We constructed a panel dataset with independent variables as control variables; we then undertook an analysis using multiple ordinary least squares regression and methods that leverage the Akaike information criterion and the Bayesian information criterion. It was found that past orientation had a positive relationship with the suicide rate (P ≤ .001) and that it improves the goodness-of-fit of the model regarding the suicide rate. Unemployment rate (P ≤ .001 in Models 3 and 4), Gini coefficient (P ≤ .001), and population growth rate (P ≤ .001) had a positive relationship with the suicide rate, whereas the gross state product (P ≤ .001) showed a negative relationship with the suicide rate. We empirically identified the positive relationship between the suicide rate and past orientation, which was measured by big data-driven Google search query.

Guided Iterative Substructure Search (GI-SSS) - A New Trick for an Old Dog.

PubMed

Weskamp, Nils

2016-07-01

Substructure search (SSS) is a fundamental technique supported by various chemical information systems. Many users apply it in an iterative manner: they modify their queries to shape the composition of the retrieved hit sets according to their needs. We propose and evaluate two heuristic extensions of SSS aimed at simplifying these iterative query modifications by collecting additional information during query processing and visualizing this information in an intuitive way. This gives the user a convenient feedback on how certain changes to the query would affect the retrieved hit set and reduces the number of trial-and-error cycles needed to generate an optimal search result. The proposed heuristics are simple, yet surprisingly effective and can be easily added to existing SSS implementations. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

41. DISCOVERY, SEARCH, AND COMMUNICATION OF TEXTUAL KNOWLEDGE RESOURCES IN DISTRIBUTED SYSTEMS a. Discovering and Utilizing Knowledge Sources for Metasearch Knowledge Systems

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

Zamora, Antonio

Advanced Natural Language Processing Tools for Web Information Retrieval, Content Analysis, and Synthesis. The goal of this SBIR was to implement and evaluate several advanced Natural Language Processing (NLP) tools and techniques to enhance the precision and relevance of search results by analyzing and augmenting search queries and by helping to organize the search output obtained from heterogeneous databases and web pages containing textual information of interest to DOE and the scientific-technical user communities in general. The SBIR investigated 1) the incorporation of spelling checkers in search applications, 2) identification of significant phrases and concepts using a combination of linguisticmore » and statistical techniques, and 3) enhancement of the query interface and search retrieval results through the use of semantic resources, such as thesauri. A search program with a flexible query interface was developed to search reference databases with the objective of enhancing search results from web queries or queries of specialized search systems such as DOE's Information Bridge. The DOE ETDE/INIS Joint Thesaurus was processed to create a searchable database. Term frequencies and term co-occurrences were used to enhance the web information retrieval by providing algorithmically-derived objective criteria to organize relevant documents into clusters containing significant terms. A thesaurus provides an authoritative overview and classification of a field of knowledge. By organizing the results of a search using the thesaurus terminology, the output is more meaningful than when the results are just organized based on the terms that co-occur in the retrieved documents, some of which may not be significant. An attempt was made to take advantage of the hierarchy provided by broader and narrower terms, as well as other field-specific information in the thesauri. The search program uses linguistic morphological routines to find relevant entries regardless of whether terms are stored in singular or plural form. Implementation of additional inflectional morphology processes for verbs can enhance retrieval further, but this has to be balanced by the possibility of broadening the results too much. In addition to the DOE energy thesaurus, other sources of specialized organized knowledge such as the Medical Subject Headings (MeSH), the Unified Medical Language System (UMLS), and Wikipedia were investigated. The supporting role of the NLP thesaurus search program was enhanced by incorporating spelling aid and a part-of-speech tagger to cope with misspellings in the queries and to determine the grammatical roles of the query words and identify nouns for special processing. To improve precision, multiple modes of searching were implemented including Boolean operators, and field-specific searches. Programs to convert a thesaurus or reference file into searchable support files can be deployed easily, and the resulting files are immediately searchable to produce relevance-ranked results with builtin spelling aid, morphological processing, and advanced search logic. Demonstration systems were built for several databases, including the DOE energy thesaurus.« less

Web page sorting algorithm based on query keyword distance relation

NASA Astrophysics Data System (ADS)

Yang, Han; Cui, Hong Gang; Tang, Hao

2017-08-01

In order to optimize the problem of page sorting, according to the search keywords in the web page in the relationship between the characteristics of the proposed query keywords clustering ideas. And it is converted into the degree of aggregation of the search keywords in the web page. Based on the PageRank algorithm, the clustering degree factor of the query keyword is added to make it possible to participate in the quantitative calculation. This paper proposes an improved algorithm for PageRank based on the distance relation between search keywords. The experimental results show the feasibility and effectiveness of the method.

Raising the IQ in full-text searching via intelligent querying

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

Kero, R.; Russell, L.; Swietlik, C.

1994-11-01

Current Information Retrieval (IR) technologies allow for efficient access to relevant information, provided that user selected query terms coincide with the specific linguistical choices made by the authors whose works constitute the text-base. Therefore, the challenge is to enhance the limited searching capability of state-of-the-practice IR. This can be done either with augmented clients that overcome current server searching deficiencies, or with added capabilities that can augment searching algorithms on the servers. The technology being investigated is that of deductive databases, with a set of new techniques called cooperative answering. This technology utilizes semantic networks to allow for navigation betweenmore » possible query search term alternatives. The augmented search terms are passed to an IR engine and the results can be compared. The project utilizes the OSTI Environment, Safety and Health Thesaurus to populate the domain specific semantic network and the text base of ES&H related documents from the Facility Profile Information Management System as the domain specific search space.« less

Complex dynamics of our economic life on different scales: insights from search engine query data.

PubMed

Preis, Tobias; Reith, Daniel; Stanley, H Eugene

2010-12-28

Search engine query data deliver insight into the behaviour of individuals who are the smallest possible scale of our economic life. Individuals are submitting several hundred million search engine queries around the world each day. We study weekly search volume data for various search terms from 2004 to 2010 that are offered by the search engine Google for scientific use, providing information about our economic life on an aggregated collective level. We ask the question whether there is a link between search volume data and financial market fluctuations on a weekly time scale. Both collective 'swarm intelligence' of Internet users and the group of financial market participants can be regarded as a complex system of many interacting subunits that react quickly to external changes. We find clear evidence that weekly transaction volumes of S&P 500 companies are correlated with weekly search volume of corresponding company names. Furthermore, we apply a recently introduced method for quantifying complex correlations in time series with which we find a clear tendency that search volume time series and transaction volume time series show recurring patterns.

Index Compression and Efficient Query Processing in Large Web Search Engines

ERIC Educational Resources Information Center

Ding, Shuai

2013-01-01

The inverted index is the main data structure used by all the major search engines. Search engines build an inverted index on their collection to speed up query processing. As the size of the web grows, the length of the inverted list structures, which can easily grow to hundreds of MBs or even GBs for common terms (roughly linear in the size of…

Semantic retrieval and navigation in clinical document collections.

PubMed

Kreuzthaler, Markus; Daumke, Philipp; Schulz, Stefan

2015-01-01

Patients with chronic diseases undergo numerous in- and outpatient treatment periods, and therefore many documents accumulate in their electronic records. We report on an on-going project focussing on the semantic enrichment of medical texts, in order to support recall-oriented navigation across a patient's complete documentation. A document pool of 1,696 de-identified discharge summaries was used for prototyping. A natural language processing toolset for document annotation (based on the text-mining framework UIMA) and indexing (Solr) was used to support a browser-based platform for document import, search and navigation. The integrated search engine combines free text and concept-based querying, supported by dynamically generated facets (diagnoses, procedures, medications, lab values, and body parts). The prototype demonstrates the feasibility of semantic document enrichment within document collections of a single patient. Originally conceived as an add-on for the clinical workplace, this technology could also be adapted to support personalised health record platforms, as well as cross-patient search for cohort building and other secondary use scenarios.

OpenSearch technology for geospatial resources discovery

NASA Astrophysics Data System (ADS)

Papeschi, Fabrizio; Enrico, Boldrini; Mazzetti, Paolo

2010-05-01

In 2005, the term Web 2.0 has been coined by Tim O'Reilly to describe a quickly growing set of Web-based applications that share a common philosophy of "mutually maximizing collective intelligence and added value for each participant by formalized and dynamic information sharing". Around this same period, OpenSearch a new Web 2.0 technology, was developed. More properly, OpenSearch is a collection of technologies that allow publishing of search results in a format suitable for syndication and aggregation. It is a way for websites and search engines to publish search results in a standard and accessible format. Due to its strong impact on the way the Web is perceived by users and also due its relevance for businesses, Web 2.0 has attracted the attention of both mass media and the scientific community. This explosive growth in popularity of Web 2.0 technologies like OpenSearch, and practical applications of Service Oriented Architecture (SOA) resulted in an increased interest in similarities, convergence, and a potential synergy of these two concepts. SOA is considered as the philosophy of encapsulating application logic in services with a uniformly defined interface and making these publicly available via discovery mechanisms. Service consumers may then retrieve these services, compose and use them according to their current needs. A great degree of similarity between SOA and Web 2.0 may be leading to a convergence between the two paradigms. They also expose divergent elements, such as the Web 2.0 support to the human interaction in opposition to the typical SOA machine-to-machine interaction. According to these considerations, the Geospatial Information (GI) domain, is also moving first steps towards a new approach of data publishing and discovering, in particular taking advantage of the OpenSearch technology. A specific GI niche is represented by the OGC Catalog Service for Web (CSW) that is part of the OGC Web Services (OWS) specifications suite, which provides a set of services for discovery, access, and processing of geospatial resources in a SOA framework. GI-cat is a distributed CSW framework implementation developed by the ESSI Lab of the Italian National Research Council (CNR-IMAA) and the University of Florence. It provides brokering and mediation functionalities towards heterogeneous resources and inventories, exposing several standard interfaces for query distribution. This work focuses on a new GI-cat interface which allows the catalog to be queried according to the OpenSearch syntax specification, thus filling the gap between the SOA architectural design of the CSW and the Web 2.0. At the moment, there is no OGC standard specification about this topic, but an official change request has been proposed in order to enable the OGC catalogues to support OpenSearch queries. In this change request, an OpenSearch extension is proposed providing a standard mechanism to query a resource based on temporal and geographic extents. Two new catalog operations are also proposed, in order to publish a suitable OpenSearch interface. This extended interface is implemented by the modular GI-cat architecture adding a new profiling module called "OpenSearch profiler". Since GI-cat also acts as a clearinghouse catalog, another component called "OpenSearch accessor" is added in order to access OpenSearch compliant services. An important role in the GI-cat extension, is played by the adopted mapping strategy. Two different kind of mappings are required: query, and response elements mapping. Query mapping is provided in order to fit the simple OpenSearch query syntax to the complex CSW query expressed by the OGC Filter syntax. GI-cat internal data model is based on the ISO-19115 profile, that is more complex than the simple XML syndication formats, such as RSS 2.0 and Atom 1.0, suggested by OpenSearch. Once response elements are available, in order to be presented, they need to be translated from the GI-cat internal data model, to the above mentioned syndication formats; the mapping processing, is bidirectional. When GI-cat is used to access OpenSearch compliant services, the CSW query must be mapped to the OpenSearch query, and the response elements, must be translated according to the GI-cat internal data model. As results of such extensions, GI-cat provides a user friendly facade to the complex CSW interface, thus enabling it to be queried, for example, using a browser toolbar.

Generating PubMed Chemical Queries for Consumer Health Literature

PubMed Central

Loo, Jeffery; Chang, Hua Florence; Hochstein, Colette; Sun, Ying

2005-01-01

Two popular NLM resources that provide information for consumers about chemicals and their safety are the Household Products Database and Haz-Map. Search queries to PubMed via web links were generated from these databases. The query retrieves consumer health-oriented literature about adverse effects of chemicals. The retrieval was limited to a manageable set of 20 to 60 citations, achieved by successively applying increasing limits to the search until the desired number of references was reached. PMID:16779322

BJUT at TREC 2015 Microblog Track: Real-Time Filtering Using Non-negative Matrix Factorization

DTIC Science & Technology

2015-11-20

information to extend the query, al- leviates the problem of concept drift in query expansion. In User profiles Twitter Google Bing accurate ambiguity...index as the query expansion document set; second- ly,put the interest file in twitter search energy to get back the relevant twetts, the interest in...for clustering is demonstrated in Figure 2. We will be the result of the search energy Twitter as the original expression of interest, the initial

Essie: A Concept-based Search Engine for Structured Biomedical Text

PubMed Central

Ide, Nicholas C.; Loane, Russell F.; Demner-Fushman, Dina

2007-01-01

This article describes the algorithms implemented in the Essie search engine that is currently serving several Web sites at the National Library of Medicine. Essie is a phrase-based search engine with term and concept query expansion and probabilistic relevancy ranking. Essie’s design is motivated by an observation that query terms are often conceptually related to terms in a document, without actually occurring in the document text. Essie’s performance was evaluated using data and standard evaluation methods from the 2003 and 2006 Text REtrieval Conference (TREC) Genomics track. Essie was the best-performing search engine in the 2003 TREC Genomics track and achieved results comparable to those of the highest-ranking systems on the 2006 TREC Genomics track task. Essie shows that a judicious combination of exploiting document structure, phrase searching, and concept based query expansion is a useful approach for information retrieval in the biomedical domain. PMID:17329729

Searching for rare diseases in PubMed: a blind comparison of Orphanet expert query and query based on terminological knowledge.

PubMed

Griffon, N; Schuers, M; Dhombres, F; Merabti, T; Kerdelhué, G; Rollin, L; Darmoni, S J

2016-08-02

Despite international initiatives like Orphanet, it remains difficult to find up-to-date information about rare diseases. The aim of this study is to propose an exhaustive set of queries for PubMed based on terminological knowledge and to evaluate it versus the queries based on expertise provided by the most frequently used resource in Europe: Orphanet. Four rare disease terminologies (MeSH, OMIM, HPO and HRDO) were manually mapped to each other permitting the automatic creation of expended terminological queries for rare diseases. For 30 rare diseases, 30 citations retrieved by Orphanet expert query and/or query based on terminological knowledge were assessed for relevance by two independent reviewers unaware of the query's origin. An adjudication procedure was used to resolve any discrepancy. Precision, relative recall and F-measure were all computed. For each Orphanet rare disease (n = 8982), there was a corresponding terminological query, in contrast with only 2284 queries provided by Orphanet. Only 553 citations were evaluated due to queries with 0 or only a few hits. There were no significant differences between the Orpha query and terminological query in terms of precision, respectively 0.61 vs 0.52 (p = 0.13). Nevertheless, terminological queries retrieved more citations more often than Orpha queries (0.57 vs. 0.33; p = 0.01). Interestingly, Orpha queries seemed to retrieve older citations than terminological queries (p < 0.0001). The terminological queries proposed in this study are now currently available for all rare diseases. They may be a useful tool for both precision or recall oriented literature search.

Pattern Recognition-Assisted Infrared Library Searching of the Paint Data Query Database to Enhance Lead Information from Automotive Paint Trace Evidence.

PubMed

Lavine, Barry K; White, Collin G; Allen, Matthew D; Weakley, Andrew

2017-03-01

Multilayered automotive paint fragments, which are one of the most complex materials encountered in the forensic science laboratory, provide crucial links in criminal investigations and prosecutions. To determine the origin of these paint fragments, forensic automotive paint examiners have turned to the paint data query (PDQ) database, which allows the forensic examiner to compare the layer sequence and color, texture, and composition of the sample to paint systems of the original equipment manufacturer (OEM). However, modern automotive paints have a thin color coat and this layer on a microscopic fragment is often too thin to obtain accurate chemical and topcoat color information. A search engine has been developed for the infrared (IR) spectral libraries of the PDQ database in an effort to improve discrimination capability and permit quantification of discrimination power for OEM automotive paint comparisons. The similarity of IR spectra of the corresponding layers of various records for original finishes in the PDQ database often results in poor discrimination using commercial library search algorithms. A pattern recognition approach employing pre-filters and a cross-correlation library search algorithm that performs both a forward and backward search has been used to significantly improve the discrimination of IR spectra in the PDQ database and thus improve the accuracy of the search. This improvement permits inter-comparison of OEM automotive paint layer systems using the IR spectra alone. Such information can serve to quantify the discrimination power of the original automotive paint encountered in casework and further efforts to succinctly communicate trace evidence to the courts.

An information retrieval system for computerized patient records in the context of a daily hospital practice: the example of the Léon Bérard Cancer Center (France).

PubMed

Biron, P; Metzger, M H; Pezet, C; Sebban, C; Barthuet, E; Durand, T

2014-01-01

A full-text search tool was introduced into the daily practice of Léon Bérard Center (France), a health care facility devoted to treatment of cancer. This tool was integrated into the hospital information system by the IT department having been granted full autonomy to improve the system. To describe the development and various uses of a tool for full-text search of computerized patient records. The technology is based on Solr, an open-source search engine. It is a web-based application that processes HTTP requests and returns HTTP responses. A data processing pipeline that retrieves data from different repositories, normalizes, cleans and publishes it to Solr, was integrated in the information system of the Leon Bérard center. The IT department developed also user interfaces to allow users to access the search engine within the computerized medical record of the patient. From January to May 2013, 500 queries were launched per month by an average of 140 different users. Several usages of the tool were described, as follows: medical management of patients, medical research, and improving the traceability of medical care in medical records. The sensitivity of the tool for detecting the medical records of patients diagnosed with both breast cancer and diabetes was 83.0%, and its positive predictive value was 48.7% (gold standard: manual screening by a clinical research assistant). The project demonstrates that the introduction of full-text-search tools allowed practitioners to use unstructured medical information for various purposes.

Comparing image search behaviour in the ARRS GoldMiner search engine and a clinical PACS/RIS.

PubMed

De-Arteaga, Maria; Eggel, Ivan; Do, Bao; Rubin, Daniel; Kahn, Charles E; Müller, Henning

2015-08-01

Information search has changed the way we manage knowledge and the ubiquity of information access has made search a frequent activity, whether via Internet search engines or increasingly via mobile devices. Medical information search is in this respect no different and much research has been devoted to analyzing the way in which physicians aim to access information. Medical image search is a much smaller domain but has gained much attention as it has different characteristics than search for text documents. While web search log files have been analysed many times to better understand user behaviour, the log files of hospital internal systems for search in a PACS/RIS (Picture Archival and Communication System, Radiology Information System) have rarely been analysed. Such a comparison between a hospital PACS/RIS search and a web system for searching images of the biomedical literature is the goal of this paper. Objectives are to identify similarities and differences in search behaviour of the two systems, which could then be used to optimize existing systems and build new search engines. Log files of the ARRS GoldMiner medical image search engine (freely accessible on the Internet) containing 222,005 queries, and log files of Stanford's internal PACS/RIS search called radTF containing 18,068 queries were analysed. Each query was preprocessed and all query terms were mapped to the RadLex (Radiology Lexicon) terminology, a comprehensive lexicon of radiology terms created and maintained by the Radiological Society of North America, so the semantic content in the queries and the links between terms could be analysed, and synonyms for the same concept could be detected. RadLex was mainly created for the use in radiology reports, to aid structured reporting and the preparation of educational material (Lanlotz, 2006) [1]. In standard medical vocabularies such as MeSH (Medical Subject Headings) and UMLS (Unified Medical Language System) specific terms of radiology are often underrepresented, therefore RadLex was considered to be the best option for this task. The results show a surprising similarity between the usage behaviour in the two systems, but several subtle differences can also be noted. The average number of terms per query is 2.21 for GoldMiner and 2.07 for radTF, the used axes of RadLex (anatomy, pathology, findings, …) have almost the same distribution with clinical findings being the most frequent and the anatomical entity the second; also, combinations of RadLex axes are extremely similar between the two systems. Differences include a longer length of the sessions in radTF than in GoldMiner (3.4 and 1.9 queries per session on average). Several frequent search terms overlap but some strong differences exist in the details. In radTF the term "normal" is frequent, whereas in GoldMiner it is not. This makes intuitive sense, as in the literature normal cases are rarely described whereas in clinical work the comparison with normal cases is often a first step. The general similarity in many points is likely due to the fact that users of the two systems are influenced by their daily behaviour in using standard web search engines and follow this behaviour in their professional search. This means that many results and insights gained from standard web search can likely be transferred to more specialized search systems. Still, specialized log files can be used to find out more on reformulations and detailed strategies of users to find the right content. Copyright © 2015 Elsevier Inc. All rights reserved.

Why do people google movement disorders? An infodemiological study of information seeking behaviors.

PubMed

Brigo, Francesco; Erro, Roberto

2016-05-01

Millions of people worldwide everyday search Google or Wikipedia to look for health-related information. Aim of this study was to evaluate and interpret web search queries for terms related to movement disorders (MD) in English-speaking countries and their changes over time. We analyzed information regarding the volume of online searches in Google and Wikipedia for the most common MD and their treatments. We determined the highest search volume peaks to identify possible relation with online news headlines. The volume of searches for some queries related to MD entered in Google enormously increased over time. Most queries were related to definition, subtypes, symptoms and treatment (mostly to adverse effects, or alternatively, to possible alternative treatments). The highest peaks of MD search queries were temporally related to news about celebrities suffering from MD, to specific mass-media events or to news concerning pharmaceutic companies or scientific discoveries on MD. An increasing number of people use Google and Wikipedia to look for terms related to MD to obtain information on definitions, causes and symptoms, possibly to aid initial self-diagnosis. MD information demand and the actual prevalence of different MDs do not travel together: web search volume may mirrors patients' fears and worries about some particular disorders perceived as more serious than others, or may be driven by release of news about celebrities suffering from MD, "breaking news" or specific mass-media events regarding MD.

Query Refinement: Negation Detection and Proximity Learning Georgetown at TREC 2014 Clinical Decision Support Track

DTIC Science & Technology

2014-11-01

created to serve as idealized representations of actual medical records, and include information such as medical history , current symptoms, diagnosis...NLM Medical Text Indexer (MTI).3 MeSH, or Medical Subject Headings, are terminology used by the NLM to index articles, catalog books, and searching...MeSH- indexed databases such as PubMed. However, since many medical conditions may be expressed in varying terminology , a single representation of a

Query-seeded iterative sequence similarity searching improves selectivity 5–20-fold

PubMed Central

Li, Weizhong; Lopez, Rodrigo

2017-01-01

Abstract Iterative similarity search programs, like psiblast, jackhmmer, and psisearch, are much more sensitive than pairwise similarity search methods like blast and ssearch because they build a position specific scoring model (a PSSM or HMM) that captures the pattern of sequence conservation characteristic to a protein family. But models are subject to contamination; once an unrelated sequence has been added to the model, homologs of the unrelated sequence will also produce high scores, and the model can diverge from the original protein family. Examination of alignment errors during psiblast PSSM contamination suggested a simple strategy for dramatically reducing PSSM contamination. psiblast PSSMs are built from the query-based multiple sequence alignment (MSA) implied by the pairwise alignments between the query model (PSSM, HMM) and the subject sequences in the library. When the original query sequence residues are inserted into gapped positions in the aligned subject sequence, the resulting PSSM rarely produces alignment over-extensions or alignments to unrelated sequences. This simple step, which tends to anchor the PSSM to the original query sequence and slightly increase target percent identity, can reduce the frequency of false-positive alignments more than 20-fold compared with psiblast and jackhmmer, with little loss in search sensitivity. PMID:27923999

Child pornography in peer-to-peer networks.

PubMed

Steel, Chad M S

2009-08-01

The presence of child pornography in peer-to-peer networks is not disputed, but there has been little effort done to quantify and analyze the distribution and nature of that content to-date. By performing an analysis of queries and query hits on the largest peer-to-peer network, we are able to both quantify and describe the nature of querying by child pornographers as well as the content they are sharing. Child pornography related content was identified and analyzed in 235,513 user queries and 194,444 query hits. The research confirmed a large amount of peer-to-peer traffic is dedicated to child pornography, but supply and demand must be separated for a better understanding. The most prevalent query and the top two most prevalent filenames returned as query hits were child pornography related. However, it would be inaccurate to state child pornography dominates peer-to-peer as 1% of all queries were related to child pornography and 1.45% of all query hits (unique filenames) were related to child pornography, consistent with a smaller study (Hughes et al., 2008). In addition to the above, research indicates that the median age searched for was 13 years old, and the majority of queries were gender-neutral, but of those with gender-related terms, 79% were female-oriented. Distribution-wise, the vast majority of content-specific searches are for movies at 99%, though images are still the most prevalent in availability. There is no shortage of child pornography supply and demand on peer-to-peer networks and by analyzing how consumers seek and distributors advertise content we can better understand their motivations. Understanding the behavior of child pornographers and how they search for content when contrasted with those sharing content provides a basis for finding and combating that behavior. For law enforcement, knowing the specific terms used allows more timely and accurate forensics and better identification of those seeking and distributing child pornography. For Internet researchers, better filtering and monitoring is possible. For mental health professionals, understanding the preferences and behaviors of those searching supports more effective treatment.

Meshable: searching PubMed abstracts by utilizing MeSH and MeSH-derived topical terms.

PubMed

Kim, Sun; Yeganova, Lana; Wilbur, W John

2016-10-01

Medical Subject Headings (MeSH(®)) is a controlled vocabulary for indexing and searching biomedical literature. MeSH terms and subheadings are organized in a hierarchical structure and are used to indicate the topics of an article. Biologists can use either MeSH terms as queries or the MeSH interface provided in PubMed(®) for searching PubMed abstracts. However, these are rarely used, and there is no convenient way to link standardized MeSH terms to user queries. Here, we introduce a web interface which allows users to enter queries to find MeSH terms closely related to the queries. Our method relies on co-occurrence of text words and MeSH terms to find keywords that are related to each MeSH term. A query is then matched with the keywords for MeSH terms, and candidate MeSH terms are ranked based on their relatedness to the query. The experimental results show that our method achieves the best performance among several term extraction approaches in terms of topic coherence. Moreover, the interface can be effectively used to find full names of abbreviations and to disambiguate user queries. https://www.ncbi.nlm.nih.gov/IRET/MESHABLE/ CONTACT: sun.kim@nih.gov Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Does query expansion limit our learning? A comparison of social-based expansion to content-based expansion for medical queries on the internet.

PubMed

Pentoney, Christopher; Harwell, Jeff; Leroy, Gondy

2014-01-01

Searching for medical information online is a common activity. While it has been shown that forming good queries is difficult, Google's query suggestion tool, a type of query expansion, aims to facilitate query formation. However, it is unknown how this expansion, which is based on what others searched for, affects the information gathering of the online community. To measure the impact of social-based query expansion, this study compared it with content-based expansion, i.e., what is really in the text. We used 138,906 medical queries from the AOL User Session Collection and expanded them using Google's Autocomplete method (social-based) and the content of the Google Web Corpus (content-based). We evaluated the specificity and ambiguity of the expansion terms for trigram queries. We also looked at the impact on the actual results using domain diversity and expansion edit distance. Results showed that the social-based method provided more precise expansion terms as well as terms that were less ambiguous. Expanded queries do not differ significantly in diversity when expanded using the social-based method (6.72 different domains returned in the first ten results, on average) vs. content-based method (6.73 different domains, on average).

Searching and Filtering Tweets: CSIRO at the TREC 2012 Microblog Track

DTIC Science & Technology

2012-11-01

stages. We first evaluate the effect of tweet corpus pre- processing in vanilla runs (no query expansion), and then assess the effect of query expansion...Effect of a vanilla run on D4 index (both realtime and non-real-time), and query expansion methods based on the submitted runs for two sets of queries

Interest in tanning beds and sunscreen in German-speaking countries.

PubMed

Kirchberger, Michael C; Kirchberger, Laura F; Eigentler, Thomas K; Reinhard, Raphael; Berking, Carola; Schuler, Gerold; Heinzerling, Lucie; Heppt, Markus V

2017-12-01

The growing incidence of nearly all types of skin cancer can be attributed to increased exposure to natural or artificial ultraviolet (UV) radiation. However, there is a scarcity of statistical data on risk behavior or sunscreen use, which would be important for any prevention efforts. Using the search engine Google ® , we analyzed search patterns for the terms Solarium (tanning bed), Sonnencreme (sunscreen), and Sonnenschutz (sun protection) in Germany, Austria, and Switzerland between 2004 and 2016, and compared it to search patterns worldwide. For this purpose, "normalized search volumes" (NSVs) were calculated for the various search queries. The corresponding polynomial functions were then compared with each other over the course of time. Since 2001, there has been a marked worldwide decrease in the search queries for tanning bed, whereas those for sunscreen have steadily increased. In German-speaking countries, on the other hand, there have - for years - consistently been more search queries for tanning bed than for sunscreen. There is an annual periodicity of the queries, with the highest NSVs for tanning bed between March and May and those for sunscreen in the summer months around June. In Germany, the city-states of Hamburg and Berlin have particularly high NSVs for tanning bed. Compared to the rest of the world, German-speaking countries show a strikingly unfavorable search pattern. There is still great need for education and prevention with respect to sunscreen use and avoidance of artificial UV exposure. © 2017 Deutsche Dermatologische Gesellschaft (DDG). Published by John Wiley & Sons Ltd.

Clinician search behaviors may be influenced by search engine design.

PubMed

Lau, Annie Y S; Coiera, Enrico; Zrimec, Tatjana; Compton, Paul

2010-06-30

Searching the Web for documents using information retrieval systems plays an important part in clinicians' practice of evidence-based medicine. While much research focuses on the design of methods to retrieve documents, there has been little examination of the way different search engine capabilities influence clinician search behaviors. Previous studies have shown that use of task-based search engines allows for faster searches with no loss of decision accuracy compared with resource-based engines. We hypothesized that changes in search behaviors may explain these differences. In all, 75 clinicians (44 doctors and 31 clinical nurse consultants) were randomized to use either a resource-based or a task-based version of a clinical information retrieval system to answer questions about 8 clinical scenarios in a controlled setting in a university computer laboratory. Clinicians using the resource-based system could select 1 of 6 resources, such as PubMed; clinicians using the task-based system could select 1 of 6 clinical tasks, such as diagnosis. Clinicians in both systems could reformulate search queries. System logs unobtrusively capturing clinicians' interactions with the systems were coded and analyzed for clinicians' search actions and query reformulation strategies. The most frequent search action of clinicians using the resource-based system was to explore a new resource with the same query, that is, these clinicians exhibited a "breadth-first" search behaviour. Of 1398 search actions, clinicians using the resource-based system conducted 401 (28.7%, 95% confidence interval [CI] 26.37-31.11) in this way. In contrast, the majority of clinicians using the task-based system exhibited a "depth-first" search behavior in which they reformulated query keywords while keeping to the same task profiles. Of 585 search actions conducted by clinicians using the task-based system, 379 (64.8%, 95% CI 60.83-68.55) were conducted in this way. This study provides evidence that different search engine designs are associated with different user search behaviors.

Developing A Web-based User Interface for Semantic Information Retrieval

NASA Technical Reports Server (NTRS)

Berrios, Daniel C.; Keller, Richard M.

2003-01-01

While there are now a number of languages and frameworks that enable computer-based systems to search stored data semantically, the optimal design for effective user interfaces for such systems is still uncle ar. Such interfaces should mask unnecessary query detail from users, yet still allow them to build queries of arbitrary complexity without significant restrictions. We developed a user interface supporting s emantic query generation for Semanticorganizer, a tool used by scient ists and engineers at NASA to construct networks of knowledge and dat a. Through this interface users can select node types, node attribute s and node links to build ad-hoc semantic queries for searching the S emanticOrganizer network.

Thomas R. Cech, RNA, and Ribozymes

Science.gov Websites

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Performance of Point and Range Queries for In-memory Databases using Radix Trees on GPUs

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

Alam, Maksudul; Yoginath, Srikanth B; Perumalla, Kalyan S

In in-memory database systems augmented by hardware accelerators, accelerating the index searching operations can greatly increase the runtime performance of database queries. Recently, adaptive radix trees (ART) have been shown to provide very fast index search implementation on the CPU. Here, we focus on an accelerator-based implementation of ART. We present a detailed performance study of our GPU-based adaptive radix tree (GRT) implementation over a variety of key distributions, synthetic benchmarks, and actual keys from music and book data sets. The performance is also compared with other index-searching schemes on the GPU. GRT on modern GPUs achieves some of themore » highest rates of index searches reported in the literature. For point queries, a throughput of up to 106 million and 130 million lookups per second is achieved for sparse and dense keys, respectively. For range queries, GRT yields 600 million and 1000 million lookups per second for sparse and dense keys, respectively, on a large dataset of 64 million 32-bit keys.« less

Seasonal trends in sleep-disordered breathing: evidence from Internet search engine query data.

PubMed

Ingram, David G; Matthews, Camilla K; Plante, David T

2015-03-01

The primary aim of the current study was to test the hypothesis that there is a seasonal component to snoring and obstructive sleep apnea (OSA) through the use of Google search engine query data. Internet search engine query data were retrieved from Google Trends from January 2006 to December 2012. Monthly normalized search volume was obtained over that 7-year period in the USA and Australia for the following search terms: "snoring" and "sleep apnea". Seasonal effects were investigated by fitting cosinor regression models. In addition, the search terms "snoring children" and "sleep apnea children" were evaluated to examine seasonal effects in pediatric populations. Statistically significant seasonal effects were found using cosinor analysis in both USA and Australia for "snoring" (p < 0.00001 for both countries). Similarly, seasonal patterns were observed for "sleep apnea" in the USA (p = 0.001); however, cosinor analysis was not significant for this search term in Australia (p = 0.13). Seasonal patterns for "snoring children" and "sleep apnea children" were observed in the USA (p = 0.002 and p < 0.00001, respectively), with insufficient search volume to examine these search terms in Australia. All searches peaked in the winter or early spring in both countries, with the magnitude of seasonal effect ranging from 5 to 50 %. Our findings indicate that there are significant seasonal trends for both snoring and sleep apnea internet search engine queries, with a peak in the winter and early spring. Further research is indicated to determine the mechanisms underlying these findings, whether they have clinical impact, and if they are associated with other comorbid medical conditions that have similar patterns of seasonal exacerbation.

PlateRunner: A Search Engine to Identify EMR Boilerplates.

PubMed

Divita, Guy; Workman, T Elizabeth; Carter, Marjorie E; Redd, Andrew; Samore, Matthew H; Gundlapalli, Adi V

2016-01-01

Medical text contains boilerplated content, an artifact of pull-down forms from EMRs. Boilerplated content is the source of challenges for concept extraction on clinical text. This paper introduces PlateRunner, a search engine on boilerplates from the US Department of Veterans Affairs (VA) EMR. Boilerplates containing concepts should be identified and reviewed to recognize challenging formats, identify high yield document titles, and fine tune section zoning. This search engine has the capability to filter negated and asserted concepts, save and search query results. This tool can save queries, search results, and documents found for later analysis.

How popular is waterpipe tobacco smoking? Findings from internet search queries

PubMed Central

Salloum, Ramzi G; Osman, Amira; Maziak, Wasim; Thrasher, James F

2015-01-01

Objectives Waterpipe tobacco smoking (WTS), a traditional tobacco consumption practice in the Middle East, is gaining popularity worldwide. Estimates of population-level interest in WTS over time are not documented. We assessed the popularity of WTS using World Wide Web search query results across four English-speaking countries. Methods We analysed trends in Google search queries related to WTS, comparing these trends with those for electronic cigarettes between 2004 and 2013 in Australia, Canada, the UK and the USA. Weekly search volumes were reported as percentages relative to the week with the highest volume of searches. Results Web-based searches for WTS have increased steadily since 2004 in all four countries. Search volume for WTS was higher than for e-cigarettes in three of the four nations, with the highest volume in the USA. Online searches were primarily targeted at WTS products for home use, followed by searches for WTS cafés/lounges. Conclusions Online demand for information on WTS-related products and venues is large and increasing. Given the rise in WTS popularity, increasing evidence of exposure-related harms, and relatively lax government regulation, WTS is a serious public health concern and could reach epidemic levels in Western societies. PMID:25052859

Seeking Insights About Cycling Mood Disorders via Anonymized Search Logs

PubMed Central

White, Ryen W; Horvitz, Eric

2014-01-01

Background Mood disorders affect a significant portion of the general population. Cycling mood disorders are characterized by intermittent episodes (or events) of the disease. Objective Using anonymized Web search logs, we identify a population of people with significant interest in mood stabilizing drugs (MSD) and seek evidence of mood swings in this population. Methods We extracted queries to the Microsoft Bing search engine made by 20,046 Web searchers over six months, separately explored searcher demographics using data from a large external panel of users, and sought supporting information from people with mood disorders via a survey. We analyzed changes in information needs over time relative to searches on MSD. Results Queries for MSD focused on side effects and their relation to the disease. We found evidence of significant changes in search behavior and interests coinciding with days that MSD queries are made. These include large increases (>100%) in the access of nutrition information, commercial information, and adult materials. A survey of patients diagnosed with mood disorders provided evidence that repeated queries on MSD may come with exacerbations of mood disorder. A classifier predicting the occurrence of such queries one day before they are observed obtains strong performance (AUC=0.78). Conclusions Observed patterns in search behavior align with known behaviors and those highlighted by survey respondents. These observations suggest that searchers showing intensive interest in MSD may be patients who have been prescribed these drugs. Given behavioral dynamics, we surmise that the days on which MSD queries are made may coincide with commencement of mania or depression. Although we do not have data on mood changes and whether users have been diagnosed with bipolar illness, we see evidence of cycling in people who show interest in MSD and further show that we can predict impending shifts in behavior and interest. PMID:24568936

LEAP into the Pfizer Global Virtual Library (PGVL) space: creation of readily synthesizable design ideas automatically.

PubMed

Hu, Qiyue; Peng, Zhengwei; Kostrowicki, Jaroslav; Kuki, Atsuo

2011-01-01

Pfizer Global Virtual Library (PGVL) of 10(13) readily synthesizable molecules offers a tremendous opportunity for lead optimization and scaffold hopping in drug discovery projects. However, mining into a chemical space of this size presents a challenge for the concomitant design informatics due to the fact that standard molecular similarity searches against a collection of explicit molecules cannot be utilized, since no chemical information system could create and manage more than 10(8) explicit molecules. Nevertheless, by accepting a tolerable level of false negatives in search results, we were able to bypass the need for full 10(13) enumeration and enabled the efficient similarity search and retrieval into this huge chemical space for practical usage by medicinal chemists. In this report, two search methods (LEAP1 and LEAP2) are presented. The first method uses PGVL reaction knowledge to disassemble the incoming search query molecule into a set of reactants and then uses reactant-level similarities into actual available starting materials to focus on a much smaller sub-region of the full virtual library compound space. This sub-region is then explicitly enumerated and searched via a standard similarity method using the original query molecule. The second method uses a fuzzy mapping onto candidate reactions and does not require exact disassembly of the incoming query molecule. Instead Basis Products (or capped reactants) are mapped into the query molecule and the resultant asymmetric similarity scores are used to prioritize the corresponding reactions and reactant sets. All sets of Basis Products are inherently indexed to specific reactions and specific starting materials. This again allows focusing on a much smaller sub-region for explicit enumeration and subsequent standard product-level similarity search. A set of validation studies were conducted. The results have shown that the level of false negatives for the disassembly-based method is acceptable when the query molecule can be recognized for exact disassembly, and the fuzzy reaction mapping method based on Basis Products has an even better performance in terms of lower false-negative rate because it is not limited by the requirement that the query molecule needs to be recognized by any disassembly algorithm. Both search methods have been implemented and accessed through a powerful desktop molecular design tool (see ref. (33) for details). The chapter will end with a comparison of published search methods against large virtual chemical space.

Parallel Index and Query for Large Scale Data Analysis

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

Chou, Jerry; Wu, Kesheng; Ruebel, Oliver

2011-07-18

Modern scientific datasets present numerous data management and analysis challenges. State-of-the-art index and query technologies are critical for facilitating interactive exploration of large datasets, but numerous challenges remain in terms of designing a system for process- ing general scientific datasets. The system needs to be able to run on distributed multi-core platforms, efficiently utilize underlying I/O infrastructure, and scale to massive datasets. We present FastQuery, a novel software framework that address these challenges. FastQuery utilizes a state-of-the-art index and query technology (FastBit) and is designed to process mas- sive datasets on modern supercomputing platforms. We apply FastQuery to processing ofmore » a massive 50TB dataset generated by a large scale accelerator modeling code. We demonstrate the scalability of the tool to 11,520 cores. Motivated by the scientific need to search for inter- esting particles in this dataset, we use our framework to reduce search time from hours to tens of seconds.« less

Hybrid ontology for semantic information retrieval model using keyword matching indexing system.

PubMed

Uthayan, K R; Mala, G S Anandha

2015-01-01

Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology.

Hybrid Ontology for Semantic Information Retrieval Model Using Keyword Matching Indexing System

PubMed Central

Uthayan, K. R.; Anandha Mala, G. S.

2015-01-01

Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology. PMID:25922851

DOE Research and Development Accomplishments: Visions of Success I

Science.gov Websites

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DOE Research and Development Accomplishments Contact Us

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DOE Research and Development Accomplishments QR Code

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DOE Research and Development Accomplishments What's New

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DOE Research and Development Accomplishments: Fast Facts

Science.gov Websites

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

IRIS is a search tool plug-in that is used to implement latent topic feedback for enhancing text navigation. It accepts a list of returned documents from an information retrieval wywtem that is generated from keyword search queries. Data is pulled directly from a topic information database and processed by IRIS to determine the most prominent and relevant topics, along with topic-ngrams, associated with the list of returned documents. User selected topics are then used to expand the query and presumabley refine the search results.

Forecasting influenza in Hong Kong with Google search queries and statistical model fusion

PubMed Central

Ramirez Ramirez, L. Leticia; Nezafati, Kusha; Zhang, Qingpeng; Tsui, Kwok-Leung

2017-01-01

Background The objective of this study is to investigate predictive utility of online social media and web search queries, particularly, Google search data, to forecast new cases of influenza-like-illness (ILI) in general outpatient clinics (GOPC) in Hong Kong. To mitigate the impact of sensitivity to self-excitement (i.e., fickle media interest) and other artifacts of online social media data, in our approach we fuse multiple offline and online data sources. Methods Four individual models: generalized linear model (GLM), least absolute shrinkage and selection operator (LASSO), autoregressive integrated moving average (ARIMA), and deep learning (DL) with Feedforward Neural Networks (FNN) are employed to forecast ILI-GOPC both one week and two weeks in advance. The covariates include Google search queries, meteorological data, and previously recorded offline ILI. To our knowledge, this is the first study that introduces deep learning methodology into surveillance of infectious diseases and investigates its predictive utility. Furthermore, to exploit the strength from each individual forecasting models, we use statistical model fusion, using Bayesian model averaging (BMA), which allows a systematic integration of multiple forecast scenarios. For each model, an adaptive approach is used to capture the recent relationship between ILI and covariates. Results DL with FNN appears to deliver the most competitive predictive performance among the four considered individual models. Combing all four models in a comprehensive BMA framework allows to further improve such predictive evaluation metrics as root mean squared error (RMSE) and mean absolute predictive error (MAPE). Nevertheless, DL with FNN remains the preferred method for predicting locations of influenza peaks. Conclusions The proposed approach can be viewed a feasible alternative to forecast ILI in Hong Kong or other countries where ILI has no constant seasonal trend and influenza data resources are limited. The proposed methodology is easily tractable and computationally efficient. PMID:28464015

Twelfth International Symposium on Methodologies for Intelligent Systems (ISMIS󈧄) Held in Charlotte, North Carolina on October 11-14, 2000

DTIC Science & Technology

2000-10-14

without any knowledge of the problem area. Therefore, Darwinian-type evolutionary computation has found a very wide range of applications, including many ...the author examined many biomedical studies that included literature searches. The Science Citation Index (SCL) Abstracts of these studies...yield many records that are non-relevant to the main technical themes of the study. In summary, these types of simple limited queries can result in two

An Improvement to a Multi-Client Searchable Encryption Scheme for Boolean Queries.

PubMed

Jiang, Han; Li, Xue; Xu, Qiuliang

2016-12-01

The migration of e-health systems to the cloud computing brings huge benefits, as same as some security risks. Searchable Encryption(SE) is a cryptography encryption scheme that can protect the confidentiality of data and utilize the encrypted data at the same time. The SE scheme proposed by Cash et al. in Crypto2013 and its follow-up work in CCS2013 are most practical SE Scheme that support Boolean queries at present. In their scheme, the data user has to generate the search tokens by the counter number one by one and interact with server repeatedly, until he meets the correct one, or goes through plenty of tokens to illustrate that there is no search result. In this paper, we make an improvement to their scheme. We allow server to send back some information and help the user to generate exact search token in the search phase. In our scheme, there are only two round interaction between server and user, and the search token has [Formula: see text] elements, where n is the keywords number in query expression, and [Formula: see text] is the minimum documents number that contains one of keyword in query expression, and the computation cost of server is [Formula: see text] modular exponentiation operation.

An exponentiation method for XML element retrieval.

PubMed

Wichaiwong, Tanakorn

2014-01-01

XML document is now widely used for modelling and storing structured documents. The structure is very rich and carries important information about contents and their relationships, for example, e-Commerce. XML data-centric collections require query terms allowing users to specify constraints on the document structure; mapping structure queries and assigning the weight are significant for the set of possibly relevant documents with respect to structural conditions. In this paper, we present an extension to the MEXIR search system that supports the combination of structural and content queries in the form of content-and-structure queries, which we call the Exponentiation function. It has been shown the structural information improve the effectiveness of the search system up to 52.60% over the baseline BM25 at MAP.

Privacy-Preserving Location-Based Query Using Location Indexes and Parallel Searching in Distributed Networks

PubMed Central

Liu, Lei; Zhao, Jing

2014-01-01

An efficient location-based query algorithm of protecting the privacy of the user in the distributed networks is given. This algorithm utilizes the location indexes of the users and multiple parallel threads to search and select quickly all the candidate anonymous sets with more users and their location information with more uniform distribution to accelerate the execution of the temporal-spatial anonymous operations, and it allows the users to configure their custom-made privacy-preserving location query requests. The simulated experiment results show that the proposed algorithm can offer simultaneously the location query services for more users and improve the performance of the anonymous server and satisfy the anonymous location requests of the users. PMID:24790579

Privacy-preserving location-based query using location indexes and parallel searching in distributed networks.

PubMed

Zhong, Cheng; Liu, Lei; Zhao, Jing

2014-01-01

An efficient location-based query algorithm of protecting the privacy of the user in the distributed networks is given. This algorithm utilizes the location indexes of the users and multiple parallel threads to search and select quickly all the candidate anonymous sets with more users and their location information with more uniform distribution to accelerate the execution of the temporal-spatial anonymous operations, and it allows the users to configure their custom-made privacy-preserving location query requests. The simulated experiment results show that the proposed algorithm can offer simultaneously the location query services for more users and improve the performance of the anonymous server and satisfy the anonymous location requests of the users.

Using digital surveillance to examine the impact of public figure pancreatic cancer announcements on media and search query outcomes.

PubMed

Noar, Seth M; Ribisl, Kurt M; Althouse, Benjamin M; Willoughby, Jessica Fitts; Ayers, John W

2013-12-01

Announcements of cancer diagnoses from public figures may stimulate cancer information seeking and media coverage about cancer. This study used digital surveillance to quantify the effects of pancreatic cancer public figure announcements on online cancer information seeking and cancer media coverage. We compiled a list of public figures (N = 25) who had been diagnosed with or had died from pancreatic cancer between 2006 and 2011. We specified interrupted time series models using data from Google Trends to examine search query shifts for pancreatic cancer and other cancers. Weekly media coverage archived on Google News were also analyzed. Most public figures' pancreatic cancer announcements corresponded with no appreciable change in pancreatic cancer search queries or media coverage. In contrast, Patrick Swayze's diagnosis was associated with a 285% (95% confidence interval [CI]: 212 to 360) increase in pancreatic cancer search queries, though it was only weakly associated with increases in pancreatic cancer media coverage. Steve Jobs's death was associated with a 197% (95% CI: 131 to 266) increase in pancreatic cancer queries and a 3517% (95% CI: 2882 to 4492) increase in pancreatic cancer media coverage. In general, a doubling in pancreatic cancer-specific media coverage corresponded with a 325% increase in pancreatic cancer queries. Digital surveillance is an important tool for future cancer control research and practice. The current application of these methods suggested that pancreatic cancer announcements (diagnosis or death) by particular public figures stimulated media coverage of and online information seeking for pancreatic cancer.

Persistent Identifiers for Improved Accessibility for Linked Data Querying

NASA Astrophysics Data System (ADS)

Shepherd, A.; Chandler, C. L.; Arko, R. A.; Fils, D.; Jones, M. B.; Krisnadhi, A.; Mecum, B.

2016-12-01

The adoption of linked open data principles within the geosciences has increased the amount of accessible information available on the Web. However, this data is difficult to consume for those who are unfamiliar with Semantic Web technologies such as Web Ontology Language (OWL), Resource Description Framework (RDF) and SPARQL - the RDF query language. Consumers would need to understand the structure of the data and how to efficiently query it. Furthermore, understanding how to query doesn't solve problems of poor precision and recall in search results. For consumers unfamiliar with the data, full-text searches are most accessible, but not ideal as they arrest the advantages of data disambiguation and co-reference resolution efforts. Conversely, URI searches across linked data can deliver improved search results, but knowledge of these exact URIs may remain difficult to obtain. The increased adoption of Persistent Identifiers (PIDs) can lead to improved linked data querying by a wide variety of consumers. Because PIDs resolve to a single entity, they are an excellent data point for disambiguating content. At the same time, PIDs are more accessible and prominent than a single data provider's linked data URI. When present in linked open datasets, PIDs provide balance between the technical and social hurdles of linked data querying as evidenced by the NSF EarthCube GeoLink project. The GeoLink project, funded by NSF's EarthCube initiative, have brought together data repositories include content from field expeditions, laboratory analyses, journal publications, conference presentations, theses/reports, and funding awards that span scientific studies from marine geology to marine ecosystems and biogeochemistry to paleoclimatology.

Combinatorial Fusion Analysis for Meta Search Information Retrieval

NASA Astrophysics Data System (ADS)

Hsu, D. Frank; Taksa, Isak

Leading commercial search engines are built as single event systems. In response to a particular search query, the search engine returns a single list of ranked search results. To find more relevant results the user must frequently try several other search engines. A meta search engine was developed to enhance the process of multi-engine querying. The meta search engine queries several engines at the same time and fuses individual engine results into a single search results list. The fusion of multiple search results has been shown (mostly experimentally) to be highly effective. However, the question of why and how the fusion should be done still remains largely unanswered. In this chapter, we utilize the combinatorial fusion analysis proposed by Hsu et al. to analyze combination and fusion of multiple sources of information. A rank/score function is used in the design and analysis of our framework. The framework provides a better understanding of the fusion phenomenon in information retrieval. For example, to improve the performance of the combined multiple scoring systems, it is necessary that each of the individual scoring systems has relatively high performance and the individual scoring systems are diverse. Additionally, we illustrate various applications of the framework using two examples from the information retrieval domain.

An intelligent user interface for browsing satellite data catalogs

NASA Technical Reports Server (NTRS)

Cromp, Robert F.; Crook, Sharon

1989-01-01

A large scale domain-independent spatial data management expert system that serves as a front-end to databases containing spatial data is described. This system is unique for two reasons. First, it uses spatial search techniques to generate a list of all the primary keys that fall within a user's spatial constraints prior to invoking the database management system, thus substantially decreasing the amount of time required to answer a user's query. Second, a domain-independent query expert system uses a domain-specific rule base to preprocess the user's English query, effectively mapping a broad class of queries into a smaller subset that can be handled by a commercial natural language processing system. The methods used by the spatial search module and the query expert system are explained, and the system architecture for the spatial data management expert system is described. The system is applied to data from the International Ultraviolet Explorer (IUE) satellite, and results are given.

SPLICE: A program to assemble partial query solutions from three-dimensional database searches into novel ligands

NASA Astrophysics Data System (ADS)

Ho, Chris M. W.; Marshall, Garland R.

1993-12-01

SPLICE is a program that processes partial query solutions retrieved from 3D, structural databases to generate novel, aggregate ligands. It is designed to interface with the database searching program FOUNDATION, which retrieves fragments containing any combination of a user-specified minimum number of matching query elements. SPLICE eliminates aspects of structures that are physically incapable of binding within the active site. Then, a systematic rule-based procedure is performed upon the remaining fragments to ensure receptor complementarity. All modifications are automated and remain transparent to the user. Ligands are then assembled by linking components into composite structures through overlapping bonds. As a control experiment, FOUNDATION and SPLICE were used to reconstruct a know HIV-1 protease inhibitor after it had been fragmented, reoriented, and added to a sham database of fifty different small molecules. To illustrate the capabilities of this program, a 3D search query containing the pharmacophoric elements of an aspartic proteinase-inhibitor crystal complex was searched using FOUNDATION against a subset of the Cambridge Structural Database. One hundred thirty-one compounds were retrieved, each containing any combination of at least four query elements. Compounds were automatically screened and edited for receptor complementarity. Numerous combinations of fragments were discovered that could be linked to form novel structures, containing a greater number of pharmacophoric elements than any single retrieved fragment.

A novel visualization model for web search results.

PubMed

Nguyen, Tien N; Zhang, Jin

2006-01-01

This paper presents an interactive visualization system, named WebSearchViz, for visualizing the Web search results and acilitating users' navigation and exploration. The metaphor in our model is the solar system with its planets and asteroids revolving around the sun. Location, color, movement, and spatial distance of objects in the visual space are used to represent the semantic relationships between a query and relevant Web pages. Especially, the movement of objects and their speeds add a new dimension to the visual space, illustrating the degree of relevance among a query and Web search results in the context of users' subjects of interest. By interacting with the visual space, users are able to observe the semantic relevance between a query and a resulting Web page with respect to their subjects of interest, context information, or concern. Users' subjects of interest can be dynamically changed, redefined, added, or deleted from the visual space.

Faceted Visualization of Three Dimensional Neuroanatomy By Combining Ontology with Faceted Search

PubMed Central

Veeraraghavan, Harini; Miller, James V.

2013-01-01

In this work, we present a faceted-search based approach for visualization of anatomy by combining a three dimensional digital atlas with an anatomy ontology. Specifically, our approach provides a drill-down search interface that exposes the relevant pieces of information (obtained by searching the ontology) for a user query. Hence, the user can produce visualizations starting with minimally specified queries. Furthermore, by automatically translating the user queries into the controlled terminology our approach eliminates the need for the user to use controlled terminology. We demonstrate the scalability of our approach using an abdominal atlas and the same ontology. We implemented our visualization tool on the opensource 3D Slicer software. We present results of our visualization approach by combining a modified Foundational Model of Anatomy (FMA) ontology with the Surgical Planning Laboratory (SPL) Brain 3D digital atlas, and geometric models specific to patients computed using the SPL brain tumor dataset. PMID:24006207

Faceted visualization of three dimensional neuroanatomy by combining ontology with faceted search.

PubMed

Veeraraghavan, Harini; Miller, James V

2014-04-01

In this work, we present a faceted-search based approach for visualization of anatomy by combining a three dimensional digital atlas with an anatomy ontology. Specifically, our approach provides a drill-down search interface that exposes the relevant pieces of information (obtained by searching the ontology) for a user query. Hence, the user can produce visualizations starting with minimally specified queries. Furthermore, by automatically translating the user queries into the controlled terminology our approach eliminates the need for the user to use controlled terminology. We demonstrate the scalability of our approach using an abdominal atlas and the same ontology. We implemented our visualization tool on the opensource 3D Slicer software. We present results of our visualization approach by combining a modified Foundational Model of Anatomy (FMA) ontology with the Surgical Planning Laboratory (SPL) Brain 3D digital atlas, and geometric models specific to patients computed using the SPL brain tumor dataset.

Assessing the impact of the national smoking ban in indoor public places in china: evidence from quit smoking related online searches.

PubMed

Huang, Jidong; Zheng, Rong; Emery, Sherry

2013-01-01

Despite the tremendous economic and health costs imposed on China by tobacco use, China lacks a proactive and systematic tobacco control surveillance and evaluation system, hampering research progress on tobacco-focused surveillance and evaluation studies. This paper uses online search query analyses to investigate changes in online search behavior among Chinese Internet users in response to the adoption of the national indoor public place smoking ban. Baidu Index and Google Trends were used to examine the volume of search queries containing three key search terms "Smoking Ban(s)," "Quit Smoking," and "Electronic Cigarette(s)," along with the news coverage on the smoking ban, for the period 2009-2011. Our results show that the announcement and adoption of the indoor public place smoking ban in China generated significant increases in news coverage on smoking bans. There was a strong positive correlation between the media coverage of smoking bans and the volume of "Smoking Ban(s)" and "Quit Smoking" related search queries. The volume of search queries related to "Electronic Cigarette(s)" was also correlated with the smoking ban news coverage. To the extent it altered smoking-related online searches, our analyses suggest that the smoking ban had a significant effect, at least in the short run, on Chinese Internet users' smoking-related behaviors. This research introduces a novel analytic tool, which could serve as an alternative tobacco control evaluation and behavior surveillance tool in the absence of timely or comprehensive population surveillance system. This research also highlights the importance of a comprehensive approach to tobacco control in China.

A method of searching for related literature on protein structure analysis by considering a user's intention

PubMed Central

2015-01-01

Background In recent years, with advances in techniques for protein structure analysis, the knowledge about protein structure and function has been published in a vast number of articles. A method to search for specific publications from such a large pool of articles is needed. In this paper, we propose a method to search for related articles on protein structure analysis by using an article itself as a query. Results Each article is represented as a set of concepts in the proposed method. Then, by using similarities among concepts formulated from databases such as Gene Ontology, similarities between articles are evaluated. In this framework, the desired search results vary depending on the user's search intention because a variety of information is included in a single article. Therefore, the proposed method provides not only one input article (primary article) but also additional articles related to it as an input query to determine the search intention of the user, based on the relationship between two query articles. In other words, based on the concepts contained in the input article and additional articles, we actualize a relevant literature search that considers user intention by varying the degree of attention given to each concept and modifying the concept hierarchy graph. Conclusions We performed an experiment to retrieve relevant papers from articles on protein structure analysis registered in the Protein Data Bank by using three query datasets. The experimental results yielded search results with better accuracy than when user intention was not considered, confirming the effectiveness of the proposed method. PMID:25952498

Issues in the design of a pilot concept-based query interface for the neuroinformatics information framework.

PubMed

Marenco, Luis; Li, Yuli; Martone, Maryann E; Sternberg, Paul W; Shepherd, Gordon M; Miller, Perry L

2008-09-01

This paper describes a pilot query interface that has been constructed to help us explore a "concept-based" approach for searching the Neuroscience Information Framework (NIF). The query interface is concept-based in the sense that the search terms submitted through the interface are selected from a standardized vocabulary of terms (concepts) that are structured in the form of an ontology. The NIF contains three primary resources: the NIF Resource Registry, the NIF Document Archive, and the NIF Database Mediator. These NIF resources are very different in their nature and therefore pose challenges when designing a single interface from which searches can be automatically launched against all three resources simultaneously. The paper first discusses briefly several background issues involving the use of standardized biomedical vocabularies in biomedical information retrieval, and then presents a detailed example that illustrates how the pilot concept-based query interface operates. The paper concludes by discussing certain lessons learned in the development of the current version of the interface.

Issues in the Design of a Pilot Concept-Based Query Interface for the Neuroinformatics Information Framework

PubMed Central

Li, Yuli; Martone, Maryann E.; Sternberg, Paul W.; Shepherd, Gordon M.; Miller, Perry L.

2009-01-01

This paper describes a pilot query interface that has been constructed to help us explore a “concept-based” approach for searching the Neuroscience Information Framework (NIF). The query interface is concept-based in the sense that the search terms submitted through the interface are selected from a standardized vocabulary of terms (concepts) that are structured in the form of an ontology. The NIF contains three primary resources: the NIF Resource Registry, the NIF Document Archive, and the NIF Database Mediator. These NIF resources are very different in their nature and therefore pose challenges when designing a single interface from which searches can be automatically launched against all three resources simultaneously. The paper first discusses briefly several background issues involving the use of standardized biomedical vocabularies in biomedical information retrieval, and then presents a detailed example that illustrates how the pilot concept-based query interface operates. The paper concludes by discussing certain lessons learned in the development of the current version of the interface. PMID:18953674

Heuristic query optimization for query multiple table and multiple clausa on mobile finance application

NASA Astrophysics Data System (ADS)

Indrayana, I. N. E.; P, N. M. Wirasyanti D.; Sudiartha, I. KG

2018-01-01

Mobile application allow many users to access data from the application without being limited to space, space and time. Over time the data population of this application will increase. Data access time will cause problems if the data record has reached tens of thousands to millions of records.The objective of this research is to maintain the performance of data execution for large data records. One effort to maintain data access time performance is to apply query optimization method. The optimization used in this research is query heuristic optimization method. The built application is a mobile-based financial application using MySQL database with stored procedure therein. This application is used by more than one business entity in one database, thus enabling rapid data growth. In this stored procedure there is an optimized query using heuristic method. Query optimization is performed on a “Select” query that involves more than one table with multiple clausa. Evaluation is done by calculating the average access time using optimized and unoptimized queries. Access time calculation is also performed on the increase of population data in the database. The evaluation results shown the time of data execution with query heuristic optimization relatively faster than data execution time without using query optimization.

Method and system for efficiently searching an encoded vector index

DOEpatents

Bui, Thuan Quang; Egan, Randy Lynn; Kathmann, Kevin James

2001-09-04

Method and system aspects for efficiently searching an encoded vector index are provided. The aspects include the translation of a search query into a candidate bitmap, and the mapping of data from the candidate bitmap into a search result bitmap according to entry values in the encoded vector index. Further, the translation includes the setting of a bit in the candidate bitmap for each entry in a symbol table that corresponds to candidate of the search query. Also included in the mapping is the identification of a bit value in the candidate bitmap pointed to by an entry in an encoded vector.

LETTER TO THE EDITOR: Optimization of partial search

NASA Astrophysics Data System (ADS)

Korepin, Vladimir E.

2005-11-01

A quantum Grover search algorithm can find a target item in a database faster than any classical algorithm. One can trade accuracy for speed and find a part of the database (a block) containing the target item even faster; this is partial search. A partial search algorithm was recently suggested by Grover and Radhakrishnan. Here we optimize it. Efficiency of the search algorithm is measured by the number of queries to the oracle. The author suggests a new version of the Grover-Radhakrishnan algorithm which uses a minimal number of such queries. The algorithm can run on the same hardware that is used for the usual Grover algorithm.

Semantic technologies improving the recall and precision of the Mercury metadata search engine

NASA Astrophysics Data System (ADS)

Pouchard, L. C.; Cook, R. B.; Green, J.; Palanisamy, G.; Noy, N.

2011-12-01

The Mercury federated metadata system [1] was developed at the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC), a NASA-sponsored effort holding datasets about biogeochemical dynamics, ecological data, and environmental processes. Mercury currently indexes over 100,000 records from several data providers conforming to community standards, e.g. EML, FGDC, FGDC Biological Profile, ISO 19115 and DIF. With the breadth of sciences represented in Mercury, the potential exists to address some key interdisciplinary scientific challenges related to climate change, its environmental and ecological impacts, and mitigation of these impacts. However, this wealth of metadata also hinders pinpointing datasets relevant to a particular inquiry. We implemented a semantic solution after concluding that traditional search approaches cannot improve the accuracy of the search results in this domain because: a) unlike everyday queries, scientific queries seek to return specific datasets with numerous parameters that may or may not be exposed to search (Deep Web queries); b) the relevance of a dataset cannot be judged by its popularity, as each scientific inquiry tends to be unique; and c)each domain science has its own terminology, more or less curated, consensual, and standardized depending on the domain. The same terms may refer to different concepts across domains (homonyms), but different terms mean the same thing (synonyms). Interdisciplinary research is arduous because an expert in a domain must become fluent in the language of another, just to find relevant datasets. Thus, we decided to use scientific ontologies because they can provide a context for a free-text search, in a way that string-based keywords never will. With added context, relevant datasets are more easily discoverable. To enable search and programmatic access to ontology entities in Mercury, we are using an instance of the BioPortal ontology repository. Mercury accesses ontology entities using the BioPortal REST API by passing a search parameter to BioPortal that may return domain context, parameter attribute, or entity annotations depending on the entity's associated ontological relationships. As Mercury's facetted search is popular with users, the results are displayed as facets. Unlike a facetted search however, the ontology-based solution implements both restrictions (improving precision) and expansions (improving recall) on the results of the initial search. For instance, "carbon" acquires a scientific context and additional key terms or phrases for discovering domain-specific datasets. A limitation of our solution is that the user must perform an additional step. Another limitation is that the quality of the newly discovered metadata is contingent upon the quality of the ontologies we use. Our solution leverages Mercury's federated capabilities to collect records from heterogeneous domains, and BioPortal's storage, curation and access capabilities for ontology entities. With minimal additional development, our approach builds on two mature systems for finding relevant datasets for interdisciplinary inquiries. We thus indicate a path forward for linking environmental, ecological and biological sciences. References: [1] Devarakonda, R., Palanisamy, G., Wilson, B. E., & Green, J. M. (2010). Mercury: reusable metadata management, data discovery and access system. Earth Science Informatics, 3(1-2), 87-94.

Lyceum: A Multi-Protocol Digital Library Gateway

NASA Technical Reports Server (NTRS)

Maa, Ming-Hokng; Nelson, Michael L.; Esler, Sandra L.

1997-01-01

Lyceum is a prototype scalable query gateway that provides a logically central interface to multi-protocol and physically distributed, digital libraries of scientific and technical information. Lyceum processes queries to multiple syntactically distinct search engines used by various distributed information servers from a single logically central interface without modification of the remote search engines. A working prototype (http://www.larc.nasa.gov/lyceum/) demonstrates the capabilities, potentials, and advantages of this type of meta-search engine by providing access to over 50 servers covering over 20 disciplines.

An Index to All "Query" Computer Searches Completed from July 1973 to June 1974. Search Number 0403-0619. Information Series No. 24.

ERIC Educational Resources Information Center

Wilder, Dolores J., Comp.; Hines, Rella, Comp.

The Tennessee Research Coordinating Unit (RCU) has implemented a computerized information retrieval system known as "Query," which allows for the retrieval of documents indexed in Research in Education (RIE), Current Index to Journals in Education (CIJE), and Abstracts of Instructional and Research Materials (AIM/ARM). The document…

An Examination of Natural Language as a Query Formation Tool for Retrieving Information on E-Health from Pub Med.

ERIC Educational Resources Information Center

Peterson, Gabriel M.; Su, Kuichun; Ries, James E.; Sievert, Mary Ellen C.

2002-01-01

Discussion of Internet use for information searches on health-related topics focuses on a study that examined complexity and variability of natural language in using search terms that express the concept of electronic health (e-health). Highlights include precision of retrieved information; shift in terminology; and queries using the Pub Med…

A two-level cache for distributed information retrieval in search engines.

PubMed

Zhang, Weizhe; He, Hui; Ye, Jianwei

2013-01-01

To improve the performance of distributed information retrieval in search engines, we propose a two-level cache structure based on the queries of the users' logs. We extract the highest rank queries of users from the static cache, in which the queries are the most popular. We adopt the dynamic cache as an auxiliary to optimize the distribution of the cache data. We propose a distribution strategy of the cache data. The experiments prove that the hit rate, the efficiency, and the time consumption of the two-level cache have advantages compared with other structures of cache.

A Two-Level Cache for Distributed Information Retrieval in Search Engines

PubMed Central

Zhang, Weizhe; He, Hui; Ye, Jianwei

2013-01-01

To improve the performance of distributed information retrieval in search engines, we propose a two-level cache structure based on the queries of the users' logs. We extract the highest rank queries of users from the static cache, in which the queries are the most popular. We adopt the dynamic cache as an auxiliary to optimize the distribution of the cache data. We propose a distribution strategy of the cache data. The experiments prove that the hit rate, the efficiency, and the time consumption of the two-level cache have advantages compared with other structures of cache. PMID:24363621

Search query data to monitor interest in behavior change: application for public health.

PubMed

Carr, Lucas J; Dunsiger, Shira I

2012-01-01

There is a need for effective interventions and policies that target the leading preventable causes of death in the U.S. (e.g., smoking, overweight/obesity, physical inactivity). Such efforts could be aided by the use of publicly available, real-time search query data that illustrate times and locations of high and low public interest in behaviors related to preventable causes of death. This study explored patterns of search query activity for the terms 'weight', 'diet', 'fitness', and 'smoking' using Google Insights for Search. Search activity for 'weight', 'diet', 'fitness', and 'smoking' conducted within the United States via Google between January 4(th), 2004 (first date data was available) and November 28(th), 2011 (date of data download and analysis) were analyzed. Using a generalized linear model, we explored the effects of time (month) on mean relative search volume for all four terms. Models suggest a significant effect of month on mean search volume for all four terms. Search activity for all four terms was highest in January with observable declines throughout the remainder of the year. These findings demonstrate discernable temporal patterns of search activity for four areas of behavior change. These findings could be used to inform the timing, location and messaging of interventions, campaigns and policies targeting these behaviors.

An alternative database approach for management of SNOMED CT and improved patient data queries.

PubMed

Campbell, W Scott; Pedersen, Jay; McClay, James C; Rao, Praveen; Bastola, Dhundy; Campbell, James R

2015-10-01

SNOMED CT is the international lingua franca of terminologies for human health. Based in Description Logics (DL), the terminology enables data queries that incorporate inferences between data elements, as well as, those relationships that are explicitly stated. However, the ontologic and polyhierarchical nature of the SNOMED CT concept model make it difficult to implement in its entirety within electronic health record systems that largely employ object oriented or relational database architectures. The result is a reduction of data richness, limitations of query capability and increased systems overhead. The hypothesis of this research was that a graph database (graph DB) architecture using SNOMED CT as the basis for the data model and subsequently modeling patient data upon the semantic core of SNOMED CT could exploit the full value of the terminology to enrich and support advanced data querying capability of patient data sets. The hypothesis was tested by instantiating a graph DB with the fully classified SNOMED CT concept model. The graph DB instance was tested for integrity by calculating the transitive closure table for the SNOMED CT hierarchy and comparing the results with transitive closure tables created using current, validated methods. The graph DB was then populated with 461,171 anonymized patient record fragments and over 2.1 million associated SNOMED CT clinical findings. Queries, including concept negation and disjunction, were then run against the graph database and an enterprise Oracle relational database (RDBMS) of the same patient data sets. The graph DB was then populated with laboratory data encoded using LOINC, as well as, medication data encoded with RxNorm and complex queries performed using LOINC, RxNorm and SNOMED CT to identify uniquely described patient populations. A graph database instance was successfully created for two international releases of SNOMED CT and two US SNOMED CT editions. Transitive closure tables and descriptive statistics generated using the graph database were identical to those using validated methods. Patient queries produced identical patient count results to the Oracle RDBMS with comparable times. Database queries involving defining attributes of SNOMED CT concepts were possible with the graph DB. The same queries could not be directly performed with the Oracle RDBMS representation of the patient data and required the creation and use of external terminology services. Further, queries of undefined depth were successful in identifying unknown relationships between patient cohorts. The results of this study supported the hypothesis that a patient database built upon and around the semantic model of SNOMED CT was possible. The model supported queries that leveraged all aspects of the SNOMED CT logical model to produce clinically relevant query results. Logical disjunction and negation queries were possible using the data model, as well as, queries that extended beyond the structural IS_A hierarchy of SNOMED CT to include queries that employed defining attribute-values of SNOMED CT concepts as search parameters. As medical terminologies, such as SNOMED CT, continue to expand, they will become more complex and model consistency will be more difficult to assure. Simultaneously, consumers of data will increasingly demand improvements to query functionality to accommodate additional granularity of clinical concepts without sacrificing speed. This new line of research provides an alternative approach to instantiating and querying patient data represented using advanced computable clinical terminologies. Copyright © 2015 Elsevier Inc. All rights reserved.

Spatial information semantic query based on SPARQL

NASA Astrophysics Data System (ADS)

Xiao, Zhifeng; Huang, Lei; Zhai, Xiaofang

2009-10-01

How can the efficiency of spatial information inquiries be enhanced in today's fast-growing information age? We are rich in geospatial data but poor in up-to-date geospatial information and knowledge that are ready to be accessed by public users. This paper adopts an approach for querying spatial semantic by building an Web Ontology language(OWL) format ontology and introducing SPARQL Protocol and RDF Query Language(SPARQL) to search spatial semantic relations. It is important to establish spatial semantics that support for effective spatial reasoning for performing semantic query. Compared to earlier keyword-based and information retrieval techniques that rely on syntax, we use semantic approaches in our spatial queries system. Semantic approaches need to be developed by ontology, so we use OWL to describe spatial information extracted by the large-scale map of Wuhan. Spatial information expressed by ontology with formal semantics is available to machines for processing and to people for understanding. The approach is illustrated by introducing a case study for using SPARQL to query geo-spatial ontology instances of Wuhan. The paper shows that making use of SPARQL to search OWL ontology instances can ensure the result's accuracy and applicability. The result also indicates constructing a geo-spatial semantic query system has positive efforts on forming spatial query and retrieval.

Meta Search Engines.

ERIC Educational Resources Information Center

Garman, Nancy

1999-01-01

Describes common options and features to consider in evaluating which meta search engine will best meet a searcher's needs. Discusses number and names of engines searched; other sources and specialty engines; search queries; other search options; and results options. (AEF)

An Exponentiation Method for XML Element Retrieval

PubMed Central

2014-01-01

XML document is now widely used for modelling and storing structured documents. The structure is very rich and carries important information about contents and their relationships, for example, e-Commerce. XML data-centric collections require query terms allowing users to specify constraints on the document structure; mapping structure queries and assigning the weight are significant for the set of possibly relevant documents with respect to structural conditions. In this paper, we present an extension to the MEXIR search system that supports the combination of structural and content queries in the form of content-and-structure queries, which we call the Exponentiation function. It has been shown the structural information improve the effectiveness of the search system up to 52.60% over the baseline BM25 at MAP. PMID:24696643

AQBE — QBE Style Queries for Archetyped Data

NASA Astrophysics Data System (ADS)

Sachdeva, Shelly; Yaginuma, Daigo; Chu, Wanming; Bhalla, Subhash

Large-scale adoption of electronic healthcare applications requires semantic interoperability. The new proposals propose an advanced (multi-level) DBMS architecture for repository services for health records of patients. These also require query interfaces at multiple levels and at the level of semi-skilled users. In this regard, a high-level user interface for querying the new form of standardized Electronic Health Records system has been examined in this study. It proposes a step-by-step graphical query interface to allow semi-skilled users to write queries. Its aim is to decrease user effort and communication ambiguities, and increase user friendliness.

Rhinoplasty perioperative database using a personal digital assistant.

PubMed

Kotler, Howard S

2004-01-01

To construct a reliable, accurate, and easy-to-use handheld computer database that facilitates the point-of-care acquisition of perioperative text and image data specific to rhinoplasty. A user-modified database (Pendragon Forms [v.3.2]; Pendragon Software Corporation, Libertyville, Ill) and graphic image program (Tealpaint [v.4.87]; Tealpaint Software, San Rafael, Calif) were used to capture text and image data, respectively, on a Palm OS (v.4.11) handheld operating with 8 megabytes of memory. The handheld and desktop databases were maintained secure using PDASecure (v.2.0) and GoldSecure (v.3.0) (Trust Digital LLC, Fairfax, Va). The handheld data were then uploaded to a desktop database of either FileMaker Pro 5.0 (v.1) (FileMaker Inc, Santa Clara, Calif) or Microsoft Access 2000 (Microsoft Corp, Redmond, Wash). Patient data were collected from 15 patients undergoing rhinoplasty in a private practice outpatient ambulatory setting. Data integrity was assessed after 6 months' disk and hard drive storage. The handheld database was able to facilitate data collection and accurately record, transfer, and reliably maintain perioperative rhinoplasty data. Query capability allowed rapid search using a multitude of keyword search terms specific to the operative maneuvers performed in rhinoplasty. Handheld computer technology provides a method of reliably recording and storing perioperative rhinoplasty information. The handheld computer facilitates the reliable and accurate storage and query of perioperative data, assisting the retrospective review of one's own results and enhancement of surgical skills.

Mirador: A Simple, Fast Search Interface for Remote Sensing Data

NASA Technical Reports Server (NTRS)

Lynnes, Christopher; Strub, Richard; Seiler, Edward; Joshi, Talak; MacHarrie, Peter

2008-01-01

A major challenge for remote sensing science researchers is searching and acquiring relevant data files for their research projects based on content, space and time constraints. Several structured query (SQ) and hierarchical navigation (HN) search interfaces have been develop ed to satisfy this requirement, yet the dominant search engines in th e general domain are based on free-text search. The Goddard Earth Sci ences Data and Information Services Center has developed a free-text search interface named Mirador that supports space-time queries, inc luding a gazetteer and geophysical event gazetteer. In order to compe nsate for a slightly reduced search precision relative to SQ and HN t echniques, Mirador uses several search optimizations to return result s quickly. The quick response enables a more iterative search strateg y than is available with many SQ and HN techniques.

BEAUTY-X: enhanced BLAST searches for DNA queries.

PubMed

Worley, K C; Culpepper, P; Wiese, B A; Smith, R F

1998-01-01

BEAUTY (BLAST Enhanced Alignment Utility) is an enhanced version of the BLAST database search tool that facilitates identification of the functions of matched sequences. Three recent improvements to the BEAUTY program described here make the enhanced output (1) available for DNA queries, (2) available for searches of any protein database, and (3) more up-to-date, with periodic updates of the domain information. BEAUTY searches of the NCBI and EMBL non-redundant protein sequence databases are available from the BCM Search Launcher Web pages (http://gc.bcm.tmc. edu:8088/search-launcher/launcher.html). BEAUTY Post-Processing of submitted search results is available using the BCM Search Launcher Batch Client (version 2.6) (ftp://gc.bcm.tmc. edu/pub/software/search-launcher/). Example figures are available at http://dot.bcm.tmc. edu:9331/papers/beautypp.html (kworley,culpep)@bcm.tmc.edu

A Deep Learning Method to Automatically Identify Reports of Scientifically Rigorous Clinical Research from the Biomedical Literature: Comparative Analytic Study.

PubMed

Del Fiol, Guilherme; Michelson, Matthew; Iorio, Alfonso; Cotoi, Chris; Haynes, R Brian

2018-06-25

A major barrier to the practice of evidence-based medicine is efficiently finding scientifically sound studies on a given clinical topic. To investigate a deep learning approach to retrieve scientifically sound treatment studies from the biomedical literature. We trained a Convolutional Neural Network using a noisy dataset of 403,216 PubMed citations with title and abstract as features. The deep learning model was compared with state-of-the-art search filters, such as PubMed's Clinical Query Broad treatment filter, McMaster's textword search strategy (no Medical Subject Heading, MeSH, terms), and Clinical Query Balanced treatment filter. A previously annotated dataset (Clinical Hedges) was used as the gold standard. The deep learning model obtained significantly lower recall than the Clinical Queries Broad treatment filter (96.9% vs 98.4%; P<.001); and equivalent recall to McMaster's textword search (96.9% vs 97.1%; P=.57) and Clinical Queries Balanced filter (96.9% vs 97.0%; P=.63). Deep learning obtained significantly higher precision than the Clinical Queries Broad filter (34.6% vs 22.4%; P<.001) and McMaster's textword search (34.6% vs 11.8%; P<.001), but was significantly lower than the Clinical Queries Balanced filter (34.6% vs 40.9%; P<.001). Deep learning performed well compared to state-of-the-art search filters, especially when citations were not indexed. Unlike previous machine learning approaches, the proposed deep learning model does not require feature engineering, or time-sensitive or proprietary features, such as MeSH terms and bibliometrics. Deep learning is a promising approach to identifying reports of scientifically rigorous clinical research. Further work is needed to optimize the deep learning model and to assess generalizability to other areas, such as diagnosis, etiology, and prognosis. ©Guilherme Del Fiol, Matthew Michelson, Alfonso Iorio, Chris Cotoi, R Brian Haynes. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 25.06.2018.

Property Graph vs RDF Triple Store: A Comparison on Glycan Substructure Search

PubMed Central

Alocci, Davide; Mariethoz, Julien; Horlacher, Oliver; Bolleman, Jerven T.; Campbell, Matthew P.; Lisacek, Frederique

2015-01-01

Resource description framework (RDF) and Property Graph databases are emerging technologies that are used for storing graph-structured data. We compare these technologies through a molecular biology use case: glycan substructure search. Glycans are branched tree-like molecules composed of building blocks linked together by chemical bonds. The molecular structure of a glycan can be encoded into a direct acyclic graph where each node represents a building block and each edge serves as a chemical linkage between two building blocks. In this context, Graph databases are possible software solutions for storing glycan structures and Graph query languages, such as SPARQL and Cypher, can be used to perform a substructure search. Glycan substructure searching is an important feature for querying structure and experimental glycan databases and retrieving biologically meaningful data. This applies for example to identifying a region of the glycan recognised by a glycan binding protein (GBP). In this study, 19,404 glycan structures were selected from GlycomeDB (www.glycome-db.org) and modelled for being stored into a RDF triple store and a Property Graph. We then performed two different sets of searches and compared the query response times and the results from both technologies to assess performance and accuracy. The two implementations produced the same results, but interestingly we noted a difference in the query response times. Qualitative measures such as portability were also used to define further criteria for choosing the technology adapted to solving glycan substructure search and other comparable issues. PMID:26656740

Query Auto-Completion Based on Word2vec Semantic Similarity

NASA Astrophysics Data System (ADS)

Shao, Taihua; Chen, Honghui; Chen, Wanyu

2018-04-01

Query auto-completion (QAC) is the first step of information retrieval, which helps users formulate the entire query after inputting only a few prefixes. Regarding the models of QAC, the traditional method ignores the contribution from the semantic relevance between queries. However, similar queries always express extremely similar search intention. In this paper, we propose a hybrid model FS-QAC based on query semantic similarity as well as the query frequency. We choose word2vec method to measure the semantic similarity between intended queries and pre-submitted queries. By combining both features, our experiments show that FS-QAC model improves the performance when predicting the user’s query intention and helping formulate the right query. Our experimental results show that the optimal hybrid model contributes to a 7.54% improvement in terms of MRR against a state-of-the-art baseline using the public AOL query logs.

Efficient privacy-preserving string search and an application in genomics.

PubMed

Shimizu, Kana; Nuida, Koji; Rätsch, Gunnar

2016-06-01

Personal genomes carry inherent privacy risks and protecting privacy poses major social and technological challenges. We consider the case where a user searches for genetic information (e.g. an allele) on a server that stores a large genomic database and aims to receive allele-associated information. The user would like to keep the query and result private and the server the database. We propose a novel approach that combines efficient string data structures such as the Burrows-Wheeler transform with cryptographic techniques based on additive homomorphic encryption. We assume that the sequence data is searchable in efficient iterative query operations over a large indexed dictionary, for instance, from large genome collections and employing the (positional) Burrows-Wheeler transform. We use a technique called oblivious transfer that is based on additive homomorphic encryption to conceal the sequence query and the genomic region of interest in positional queries. We designed and implemented an efficient algorithm for searching sequences of SNPs in large genome databases. During search, the user can only identify the longest match while the server does not learn which sequence of SNPs the user queried. In an experiment based on 2184 aligned haploid genomes from the 1000 Genomes Project, our algorithm was able to perform typical queries within [Formula: see text] 4.6 s and [Formula: see text] 10.8 s for client and server side, respectively, on laptop computers. The presented algorithm is at least one order of magnitude faster than an exhaustive baseline algorithm. https://github.com/iskana/PBWT-sec and https://github.com/ratschlab/PBWT-sec shimizu-kana@aist.go.jp or Gunnar.Ratsch@ratschlab.org Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Efficient privacy-preserving string search and an application in genomics

PubMed Central

Shimizu, Kana; Nuida, Koji; Rätsch, Gunnar

2016-01-01

Motivation: Personal genomes carry inherent privacy risks and protecting privacy poses major social and technological challenges. We consider the case where a user searches for genetic information (e.g. an allele) on a server that stores a large genomic database and aims to receive allele-associated information. The user would like to keep the query and result private and the server the database. Approach: We propose a novel approach that combines efficient string data structures such as the Burrows–Wheeler transform with cryptographic techniques based on additive homomorphic encryption. We assume that the sequence data is searchable in efficient iterative query operations over a large indexed dictionary, for instance, from large genome collections and employing the (positional) Burrows–Wheeler transform. We use a technique called oblivious transfer that is based on additive homomorphic encryption to conceal the sequence query and the genomic region of interest in positional queries. Results: We designed and implemented an efficient algorithm for searching sequences of SNPs in large genome databases. During search, the user can only identify the longest match while the server does not learn which sequence of SNPs the user queried. In an experiment based on 2184 aligned haploid genomes from the 1000 Genomes Project, our algorithm was able to perform typical queries within ≈ 4.6 s and ≈ 10.8 s for client and server side, respectively, on laptop computers. The presented algorithm is at least one order of magnitude faster than an exhaustive baseline algorithm. Availability and implementation: https://github.com/iskana/PBWT-sec and https://github.com/ratschlab/PBWT-sec. Contacts: shimizu-kana@aist.go.jp or Gunnar.Ratsch@ratschlab.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27153731

Overview of the TREC 2014 Session Track

DTIC Science & Technology

2014-11-01

except all of them have length mi = 1 and thus they have no current/final query. Participants were to run the 1,021 current queries against their search ... engines under each of the following three conditions separately: RL1 ignoring the session prior to this query RL2 considering all the items (1), (2) and

Distributed Efficient Similarity Search Mechanism in Wireless Sensor Networks

PubMed Central

Ahmed, Khandakar; Gregory, Mark A.

2015-01-01

The Wireless Sensor Network similarity search problem has received considerable research attention due to sensor hardware imprecision and environmental parameter variations. Most of the state-of-the-art distributed data centric storage (DCS) schemes lack optimization for similarity queries of events. In this paper, a DCS scheme with metric based similarity searching (DCSMSS) is proposed. DCSMSS takes motivation from vector distance index, called iDistance, in order to transform the issue of similarity searching into the problem of an interval search in one dimension. In addition, a sector based distance routing algorithm is used to efficiently route messages. Extensive simulation results reveal that DCSMSS is highly efficient and significantly outperforms previous approaches in processing similarity search queries. PMID:25751081

Assessing the Impact of the National Smoking Ban in Indoor Public Places in China: Evidence from Quit Smoking Related Online Searches

PubMed Central

Huang, Jidong; Zheng, Rong; Emery, Sherry

2013-01-01

Background Despite the tremendous economic and health costs imposed on China by tobacco use, China lacks a proactive and systematic tobacco control surveillance and evaluation system, hampering research progress on tobacco-focused surveillance and evaluation studies. Methods This paper uses online search query analyses to investigate changes in online search behavior among Chinese Internet users in response to the adoption of the national indoor public place smoking ban. Baidu Index and Google Trends were used to examine the volume of search queries containing three key search terms “Smoking Ban(s),” “Quit Smoking,” and “Electronic Cigarette(s),” along with the news coverage on the smoking ban, for the period 2009–2011. Findings Our results show that the announcement and adoption of the indoor public place smoking ban in China generated significant increases in news coverage on smoking bans. There was a strong positive correlation between the media coverage of smoking bans and the volume of “Smoking Ban(s)” and “Quit Smoking” related search queries. The volume of search queries related to “Electronic Cigarette(s)” was also correlated with the smoking ban news coverage. Interpretation To the extent it altered smoking-related online searches, our analyses suggest that the smoking ban had a significant effect, at least in the short run, on Chinese Internet users’ smoking-related behaviors. This research introduces a novel analytic tool, which could serve as an alternative tobacco control evaluation and behavior surveillance tool in the absence of timely or comprehensive population surveillance system. This research also highlights the importance of a comprehensive approach to tobacco control in China. PMID:23776504

HBVPathDB: a database of HBV infection-related molecular interaction network.

PubMed

Zhang, Yi; Bo, Xiao-Chen; Yang, Jing; Wang, Sheng-Qi

2005-03-21

To describe molecules or genes interaction between hepatitis B viruses (HBV) and host, for understanding how virus' and host's genes and molecules are networked to form a biological system and for perceiving mechanism of HBV infection. The knowledge of HBV infection-related reactions was organized into various kinds of pathways with carefully drawn graphs in HBVPathDB. Pathway information is stored with relational database management system (DBMS), which is currently the most efficient way to manage large amounts of data and query is implemented with powerful Structured Query Language (SQL). The search engine is written using Personal Home Page (PHP) with SQL embedded and web retrieval interface is developed for searching with Hypertext Markup Language (HTML). We present the first version of HBVPathDB, which is a HBV infection-related molecular interaction network database composed of 306 pathways with 1 050 molecules involved. With carefully drawn graphs, pathway information stored in HBVPathDB can be browsed in an intuitive way. We develop an easy-to-use interface for flexible accesses to the details of database. Convenient software is implemented to query and browse the pathway information of HBVPathDB. Four search page layout options-category search, gene search, description search, unitized search-are supported by the search engine of the database. The database is freely available at http://www.bio-inf.net/HBVPathDB/HBV/. The conventional perspective HBVPathDB have already contained a considerable amount of pathway information with HBV infection related, which is suitable for in-depth analysis of molecular interaction network of virus and host. HBVPathDB integrates pathway data-sets with convenient software for query, browsing, visualization, that provides users more opportunity to identify regulatory key molecules as potential drug targets and to explore the possible mechanism of HBV infection based on gene expression datasets.

Clone DB: an integrated NCBI resource for clone-associated data

PubMed Central

Schneider, Valerie A.; Chen, Hsiu-Chuan; Clausen, Cliff; Meric, Peter A.; Zhou, Zhigang; Bouk, Nathan; Husain, Nora; Maglott, Donna R.; Church, Deanna M.

2013-01-01

The National Center for Biotechnology Information (NCBI) Clone DB (http://www.ncbi.nlm.nih.gov/clone/) is an integrated resource providing information about and facilitating access to clones, which serve as valuable research reagents in many fields, including genome sequencing and variation analysis. Clone DB represents an expansion and replacement of the former NCBI Clone Registry and has records for genomic and cell-based libraries and clones representing more than 100 different eukaryotic taxa. Records provide details of library construction, associated sequences, map positions and information about resource distribution. Clone DB is indexed in the NCBI Entrez system and can be queried by fields that include organism, clone name, gene name and sequence identifier. Whenever possible, genomic clones are mapped to reference assemblies and their map positions provided in clone records. Clones mapping to specific genomic regions can also be searched for using the NCBI Clone Finder tool, which accepts queries based on sequence coordinates or features such as gene or transcript names. Clone DB makes reports of library, clone and placement data on its FTP site available for download. With Clone DB, users now have available to them a centralized resource that provides them with the tools they will need to make use of these important research reagents. PMID:23193260

BOSS: context-enhanced search for biomedical objects

PubMed Central

2012-01-01

Background There exist many academic search solutions and most of them can be put on either ends of spectrum: general-purpose search and domain-specific "deep" search systems. The general-purpose search systems, such as PubMed, offer flexible query interface, but churn out a list of matching documents that users have to go through the results in order to find the answers to their queries. On the other hand, the "deep" search systems, such as PPI Finder and iHOP, return the precompiled results in a structured way. Their results, however, are often found only within some predefined contexts. In order to alleviate these problems, we introduce a new search engine, BOSS, Biomedical Object Search System. Methods Unlike the conventional search systems, BOSS indexes segments, rather than documents. A segment refers to a Maximal Coherent Semantic Unit (MCSU) such as phrase, clause or sentence that is semantically coherent in the given context (e.g., biomedical objects or their relations). For a user query, BOSS finds all matching segments, identifies the objects appearing in those segments, and aggregates the segments for each object. Finally, it returns the ranked list of the objects along with their matching segments. Results The working prototype of BOSS is available at http://boss.korea.ac.kr. The current version of BOSS has indexed abstracts of more than 20 million articles published during last 16 years from 1996 to 2011 across all science disciplines. Conclusion BOSS fills the gap between either ends of the spectrum by allowing users to pose context-free queries and by returning a structured set of results. Furthermore, BOSS exhibits the characteristic of good scalability, just as with conventional document search engines, because it is designed to use a standard document-indexing model with minimal modifications. Considering the features, BOSS notches up the technological level of traditional solutions for search on biomedical information. PMID:22595092

Predicting the hand, foot, and mouth disease incidence using search engine query data and climate variables: an ecological study in Guangdong, China.

PubMed

Du, Zhicheng; Xu, Lin; Zhang, Wangjian; Zhang, Dingmei; Yu, Shicheng; Hao, Yuantao

2017-10-06

Hand, foot, and mouth disease (HFMD) has caused a substantial burden in China, especially in Guangdong Province. Based on the enhanced surveillance system, we aimed to explore whether the addition of temperate and search engine query data improves the risk prediction of HFMD. Ecological study. Information on the confirmed cases of HFMD, climate parameters and search engine query logs was collected. A total of 1.36 million HFMD cases were identified from the surveillance system during 2011-2014. Analyses were conducted at aggregate level and no confidential information was involved. A seasonal autoregressive integrated moving average (ARIMA) model with external variables (ARIMAX) was used to predict the HFMD incidence from 2011 to 2014, taking into account temperature and search engine query data (Baidu Index, BDI). Statistics of goodness-of-fit and precision of prediction were used to compare models (1) based on surveillance data only, and with the addition of (2) temperature, (3) BDI, and (4) both temperature and BDI. A high correlation between HFMD incidence and BDI ( r =0.794, p<0.001) or temperature ( r =0.657, p<0.001) was observed using both time series plot and correlation matrix. A linear effect of BDI (without lag) and non-linear effect of temperature (1 week lag) on HFMD incidence were found in a distributed lag non-linear model. Compared with the model based on surveillance data only, the ARIMAX model including BDI reached the best goodness-of-fit with an Akaike information criterion (AIC) value of -345.332, whereas the model including both BDI and temperature had the most accurate prediction in terms of the mean absolute percentage error (MAPE) of 101.745%. An ARIMAX model incorporating search engine query data significantly improved the prediction of HFMD. Further studies are warranted to examine whether including search engine query data also improves the prediction of other infectious diseases in other settings. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Predicting the hand, foot, and mouth disease incidence using search engine query data and climate variables: an ecological study in Guangdong, China

PubMed Central

Du, Zhicheng; Xu, Lin; Zhang, Wangjian; Zhang, Dingmei; Yu, Shicheng; Hao, Yuantao

2017-01-01

Objectives Hand, foot, and mouth disease (HFMD) has caused a substantial burden in China, especially in Guangdong Province. Based on the enhanced surveillance system, we aimed to explore whether the addition of temperate and search engine query data improves the risk prediction of HFMD. Design Ecological study. Setting and participants Information on the confirmed cases of HFMD, climate parameters and search engine query logs was collected. A total of 1.36 million HFMD cases were identified from the surveillance system during 2011–2014. Analyses were conducted at aggregate level and no confidential information was involved. Outcome measures A seasonal autoregressive integrated moving average (ARIMA) model with external variables (ARIMAX) was used to predict the HFMD incidence from 2011 to 2014, taking into account temperature and search engine query data (Baidu Index, BDI). Statistics of goodness-of-fit and precision of prediction were used to compare models (1) based on surveillance data only, and with the addition of (2) temperature, (3) BDI, and (4) both temperature and BDI. Results A high correlation between HFMD incidence and BDI (r=0.794, p<0.001) or temperature (r=0.657, p<0.001) was observed using both time series plot and correlation matrix. A linear effect of BDI (without lag) and non-linear effect of temperature (1 week lag) on HFMD incidence were found in a distributed lag non-linear model. Compared with the model based on surveillance data only, the ARIMAX model including BDI reached the best goodness-of-fit with an Akaike information criterion (AIC) value of −345.332, whereas the model including both BDI and temperature had the most accurate prediction in terms of the mean absolute percentage error (MAPE) of 101.745%. Conclusions An ARIMAX model incorporating search engine query data significantly improved the prediction of HFMD. Further studies are warranted to examine whether including search engine query data also improves the prediction of other infectious diseases in other settings. PMID:28988169

Query-Adaptive Hash Code Ranking for Large-Scale Multi-View Visual Search.

PubMed

Liu, Xianglong; Huang, Lei; Deng, Cheng; Lang, Bo; Tao, Dacheng

2016-10-01

Hash-based nearest neighbor search has become attractive in many applications. However, the quantization in hashing usually degenerates the discriminative power when using Hamming distance ranking. Besides, for large-scale visual search, existing hashing methods cannot directly support the efficient search over the data with multiple sources, and while the literature has shown that adaptively incorporating complementary information from diverse sources or views can significantly boost the search performance. To address the problems, this paper proposes a novel and generic approach to building multiple hash tables with multiple views and generating fine-grained ranking results at bitwise and tablewise levels. For each hash table, a query-adaptive bitwise weighting is introduced to alleviate the quantization loss by simultaneously exploiting the quality of hash functions and their complement for nearest neighbor search. From the tablewise aspect, multiple hash tables are built for different data views as a joint index, over which a query-specific rank fusion is proposed to rerank all results from the bitwise ranking by diffusing in a graph. Comprehensive experiments on image search over three well-known benchmarks show that the proposed method achieves up to 17.11% and 20.28% performance gains on single and multiple table search over the state-of-the-art methods.

Are cannabis prevalence estimates comparable across countries and regions? A cross-cultural validation using search engine query data.

PubMed

Steppan, Martin; Kraus, Ludwig; Piontek, Daniela; Siciliano, Valeria

2013-01-01

Prevalence estimation of cannabis use is usually based on self-report data. Although there is evidence on the reliability of this data source, its cross-cultural validity is still a major concern. External objective criteria are needed for this purpose. In this study, cannabis-related search engine query data are used as an external criterion. Data on cannabis use were taken from the 2007 European School Survey Project on Alcohol and Other Drugs (ESPAD). Provincial data came from three Italian nation-wide studies using the same methodology (2006-2008; ESPAD-Italia). Information on cannabis-related search engine query data was based on Google search volume indices (GSI). (1) Reliability analysis was conducted for GSI. (2) Latent measurement models of "true" cannabis prevalence were tested using perceived availability, web-based cannabis searches and self-reported prevalence as indicators. (3) Structure models were set up to test the influences of response tendencies and geographical position (latitude, longitude). In order to test the stability of the models, analyses were conducted on country level (Europe, US) and on provincial level in Italy. Cannabis-related GSI were found to be highly reliable and constant over time. The overall measurement model was highly significant in both data sets. On country level, no significant effects of response bias indicators and geographical position on perceived availability, web-based cannabis searches and self-reported prevalence were found. On provincial level, latitude had a significant positive effect on availability indicating that perceived availability of cannabis in northern Italy was higher than expected from the other indicators. Although GSI showed weaker associations with cannabis use than perceived availability, the findings underline the external validity and usefulness of search engine query data as external criteria. The findings suggest an acceptable relative comparability of national (provincial) prevalence estimates of cannabis use that are based on a common survey methodology. Search engine query data are a too weak indicator to base prevalence estimations on this source only, but in combination with other sources (waste water analysis, sales of cigarette paper) they may provide satisfactory estimates. Copyright © 2012. Published by Elsevier B.V.

Spatial Query for Planetary Data

NASA Technical Reports Server (NTRS)

Shams, Khawaja S.; Crockett, Thomas M.; Powell, Mark W.; Joswig, Joseph C.; Fox, Jason M.

2011-01-01

Science investigators need to quickly and effectively assess past observations of specific locations on a planetary surface. This innovation involves a location-based search technology that was adapted and applied to planetary science data to support a spatial query capability for mission operations software. High-performance location-based searching requires the use of spatial data structures for database organization. Spatial data structures are designed to organize datasets based on their coordinates in a way that is optimized for location-based retrieval. The particular spatial data structure that was adapted for planetary data search is the R+ tree.

RadSearch: a RIS/PACS integrated query tool

NASA Astrophysics Data System (ADS)

Tsao, Sinchai; Documet, Jorge; Moin, Paymann; Wang, Kevin; Liu, Brent J.

2008-03-01

Radiology Information Systems (RIS) contain a wealth of information that can be used for research, education, and practice management. However, the sheer amount of information available makes querying specific data difficult and time consuming. Previous work has shown that a clinical RIS database and its RIS text reports can be extracted, duplicated and indexed for searches while complying with HIPAA and IRB requirements. This project's intent is to provide a software tool, the RadSearch Toolkit, to allow intelligent indexing and parsing of RIS reports for easy yet powerful searches. In addition, the project aims to seamlessly query and retrieve associated images from the Picture Archiving and Communication System (PACS) in situations where an integrated RIS/PACS is in place - even subselecting individual series, such as in an MRI study. RadSearch's application of simple text parsing techniques to index text-based radiology reports will allow the search engine to quickly return relevant results. This powerful combination will be useful in both private practice and academic settings; administrators can easily obtain complex practice management information such as referral patterns; researchers can conduct retrospective studies with specific, multiple criteria; teaching institutions can quickly and effectively create thorough teaching files.

Semantics-Based Intelligent Indexing and Retrieval of Digital Images - A Case Study

NASA Astrophysics Data System (ADS)

Osman, Taha; Thakker, Dhavalkumar; Schaefer, Gerald

The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they typically rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this chapter we present a semantically enabled image annotation and retrieval engine that is designed to satisfy the requirements of commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as presenting our initial thoughts on exploiting lexical databases for explicit semantic-based query expansion.

Can internet search queries be used for dengue fever surveillance in China?

PubMed

Guo, Pi; Wang, Li; Zhang, Yanhong; Luo, Ganfeng; Zhang, Yanting; Deng, Changyu; Zhang, Qin; Zhang, Qingying

2017-10-01

China experienced an unprecedented outbreak of dengue fever in 2014, and the number of cases reached the highest level over the past 25 years. Traditional sentinel surveillance systems of dengue fever in China have an obvious drawback that the average delay from receipt to dissemination of dengue case data is roughly 1-2 weeks. In order to exploit internet search queries to timely monitor dengue fever, we analyzed data of dengue incidence and Baidu search query from 31 provinces in mainland China during the period of January 2011 to December 2014. We found that there was a strong correlation between changes in people's online health-seeking behavior and dengue fever incidence. Our study represents the first attempt demonstrating a strong temporal and spatial correlation between internet search trends and dengue epidemics nationwide in China. The findings will help the government to strengthen the capacity of traditional surveillance systems for dengue fever. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

From health search to healthcare: explorations of intention and utilization via query logs and user surveys

PubMed Central

White, Ryen W; Horvitz, Eric

2014-01-01

Objective To better understand the relationship between online health-seeking behaviors and in-world healthcare utilization (HU) by studies of online search and access activities before and after queries that pursue medical professionals and facilities. Materials and methods We analyzed data collected from logs of online searches gathered from consenting users of a browser toolbar from Microsoft (N=9740). We employed a complementary survey (N=489) to seek a deeper understanding of information-gathering, reflection, and action on the pursuit of professional healthcare. Results We provide insights about HU through the survey, breaking out its findings by different respondent marginalizations as appropriate. Observations made from search logs may be explained by trends observed in our survey responses, even though the user populations differ. Discussion The results provide insights about how users decide if and when to utilize healthcare resources, and how online health information seeking transitions to in-world HU. The findings from both the survey and the logs reveal behavioral patterns and suggest a strong relationship between search behavior and HU. Although the diversity of our survey respondents is limited and we cannot be certain that users visited medical facilities, we demonstrate that it may be possible to infer HU from long-term search behavior by the apparent influence that health concerns and professional advice have on search activity. Conclusions Our findings highlight different phases of online activities around queries pursuing professional healthcare facilities and services. We also show that it may be possible to infer HU from logs without tracking people's physical location, based on the effect of HU on pre- and post-HU search behavior. This allows search providers and others to develop more robust models of interests and preferences by modeling utilization rather than simply the intention to utilize that is expressed in search queries. PMID:23666794

Search Query Data to Monitor Interest in Behavior Change: Application for Public Health

PubMed Central

Carr, Lucas J.; Dunsiger, Shira I.

2012-01-01

There is a need for effective interventions and policies that target the leading preventable causes of death in the U.S. (e.g., smoking, overweight/obesity, physical inactivity). Such efforts could be aided by the use of publicly available, real-time search query data that illustrate times and locations of high and low public interest in behaviors related to preventable causes of death. Objectives This study explored patterns of search query activity for the terms ‘weight’, ‘diet’, ‘fitness’, and ‘smoking’ using Google Insights for Search. Methods Search activity for ‘weight’, ‘diet’, ‘fitness’, and ‘smoking’ conducted within the United States via Google between January 4th, 2004 (first date data was available) and November 28th, 2011 (date of data download and analysis) were analyzed. Using a generalized linear model, we explored the effects of time (month) on mean relative search volume for all four terms. Results Models suggest a significant effect of month on mean search volume for all four terms. Search activity for all four terms was highest in January with observable declines throughout the remainder of the year. Conclusions These findings demonstrate discernable temporal patterns of search activity for four areas of behavior change. These findings could be used to inform the timing, location and messaging of interventions, campaigns and policies targeting these behaviors. PMID:23110198

How popular is waterpipe tobacco smoking? Findings from internet search queries.

PubMed

Salloum, Ramzi G; Osman, Amira; Maziak, Wasim; Thrasher, James F

2015-09-01

Waterpipe tobacco smoking (WTS), a traditional tobacco consumption practice in the Middle East, is gaining popularity worldwide. Estimates of population-level interest in WTS over time are not documented. We assessed the popularity of WTS using World Wide Web search query results across four English-speaking countries. We analysed trends in Google search queries related to WTS, comparing these trends with those for electronic cigarettes between 2004 and 2013 in Australia, Canada, the UK and the USA. Weekly search volumes were reported as percentages relative to the week with the highest volume of searches. Web-based searches for WTS have increased steadily since 2004 in all four countries. Search volume for WTS was higher than for e-cigarettes in three of the four nations, with the highest volume in the USA. Online searches were primarily targeted at WTS products for home use, followed by searches for WTS cafés/lounges. Online demand for information on WTS-related products and venues is large and increasing. Given the rise in WTS popularity, increasing evidence of exposure-related harms, and relatively lax government regulation, WTS is a serious public health concern and could reach epidemic levels in Western societies. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Query Classification and Study of University Students' Search Trends

ERIC Educational Resources Information Center

Maabreh, Majdi A.; Al-Kabi, Mohammed N.; Alsmadi, Izzat M.

2012-01-01

Purpose: This study is an attempt to develop an automatic identification method for Arabic web queries and divide them into several query types using data mining. In addition, it seeks to evaluate the impact of the academic environment on using the internet. Design/methodology/approach: The web log files were collected from one of the higher…

SkyQuery - A Prototype Distributed Query and Cross-Matching Web Service for the Virtual Observatory

NASA Astrophysics Data System (ADS)

Thakar, A. R.; Budavari, T.; Malik, T.; Szalay, A. S.; Fekete, G.; Nieto-Santisteban, M.; Haridas, V.; Gray, J.

2002-12-01

We have developed a prototype distributed query and cross-matching service for the VO community, called SkyQuery, which is implemented with hierarchichal Web Services. SkyQuery enables astronomers to run combined queries on existing distributed heterogeneous astronomy archives. SkyQuery provides a simple, user-friendly interface to run distributed queries over the federation of registered astronomical archives in the VO. The SkyQuery client connects to the portal Web Service, which farms the query out to the individual archives, which are also Web Services called SkyNodes. The cross-matching algorithm is run recursively on each SkyNode. Each archive is a relational DBMS with a HTM index for fast spatial lookups. The results of the distributed query are returned as an XML DataSet that is automatically rendered by the client. SkyQuery also returns the image cutout corresponding to the query result. SkyQuery finds not only matches between the various catalogs, but also dropouts - objects that exist in some of the catalogs but not in others. This is often as important as finding matches. We demonstrate the utility of SkyQuery with a brown-dwarf search between SDSS and 2MASS, and a search for radio-quiet quasars in SDSS, 2MASS and FIRST. The importance of a service like SkyQuery for the worldwide astronomical community cannot be overstated: data on the same objects in various archives is mapped in different wavelength ranges and looks very different due to different errors, instrument sensitivities and other peculiarities of each archive. Our cross-matching algorithm preforms a fuzzy spatial join across multiple catalogs. This type of cross-matching is currently often done by eye, one object at a time. A static cross-identification table for a set of archives would become obsolete by the time it was built - the exponential growth of astronomical data means that a dynamic cross-identification mechanism like SkyQuery is the only viable option. SkyQuery was funded by a grant from the NASA AISR program.

Query-Adaptive Reciprocal Hash Tables for Nearest Neighbor Search.

PubMed

Liu, Xianglong; Deng, Cheng; Lang, Bo; Tao, Dacheng; Li, Xuelong

2016-02-01

Recent years have witnessed the success of binary hashing techniques in approximate nearest neighbor search. In practice, multiple hash tables are usually built using hashing to cover more desired results in the hit buckets of each table. However, rare work studies the unified approach to constructing multiple informative hash tables using any type of hashing algorithms. Meanwhile, for multiple table search, it also lacks of a generic query-adaptive and fine-grained ranking scheme that can alleviate the binary quantization loss suffered in the standard hashing techniques. To solve the above problems, in this paper, we first regard the table construction as a selection problem over a set of candidate hash functions. With the graph representation of the function set, we propose an efficient solution that sequentially applies normalized dominant set to finding the most informative and independent hash functions for each table. To further reduce the redundancy between tables, we explore the reciprocal hash tables in a boosting manner, where the hash function graph is updated with high weights emphasized on the misclassified neighbor pairs of previous hash tables. To refine the ranking of the retrieved buckets within a certain Hamming radius from the query, we propose a query-adaptive bitwise weighting scheme to enable fine-grained bucket ranking in each hash table, exploiting the discriminative power of its hash functions and their complement for nearest neighbor search. Moreover, we integrate such scheme into the multiple table search using a fast, yet reciprocal table lookup algorithm within the adaptive weighted Hamming radius. In this paper, both the construction method and the query-adaptive search method are general and compatible with different types of hashing algorithms using different feature spaces and/or parameter settings. Our extensive experiments on several large-scale benchmarks demonstrate that the proposed techniques can significantly outperform both the naive construction methods and the state-of-the-art hashing algorithms.

Blind Seer: A Scalable Private DBMS

DTIC Science & Technology

2014-05-01

searchable index terms per DB row, in time comparable to (insecure) MySQL (many practical queries can be privately executed with work 1.2-3 times slower...than MySQL , although some queries are costlier). We support a rich query set, including searching on arbitrary boolean formulas on keywords and ranges...index terms per DB row, in time comparable to (insecure) MySQL (many practical queries can be privately executed with work 1.2-3 times slower than MySQL

Efficient Queries of Stand-off Annotations for Natural Language Processing on Electronic Medical Records.

PubMed

Luo, Yuan; Szolovits, Peter

2016-01-01

In natural language processing, stand-off annotation uses the starting and ending positions of an annotation to anchor it to the text and stores the annotation content separately from the text. We address the fundamental problem of efficiently storing stand-off annotations when applying natural language processing on narrative clinical notes in electronic medical records (EMRs) and efficiently retrieving such annotations that satisfy position constraints. Efficient storage and retrieval of stand-off annotations can facilitate tasks such as mapping unstructured text to electronic medical record ontologies. We first formulate this problem into the interval query problem, for which optimal query/update time is in general logarithm. We next perform a tight time complexity analysis on the basic interval tree query algorithm and show its nonoptimality when being applied to a collection of 13 query types from Allen's interval algebra. We then study two closely related state-of-the-art interval query algorithms, proposed query reformulations, and augmentations to the second algorithm. Our proposed algorithm achieves logarithmic time stabbing-max query time complexity and solves the stabbing-interval query tasks on all of Allen's relations in logarithmic time, attaining the theoretic lower bound. Updating time is kept logarithmic and the space requirement is kept linear at the same time. We also discuss interval management in external memory models and higher dimensions.

Efficient Queries of Stand-off Annotations for Natural Language Processing on Electronic Medical Records

PubMed Central

Luo, Yuan; Szolovits, Peter

2016-01-01

In natural language processing, stand-off annotation uses the starting and ending positions of an annotation to anchor it to the text and stores the annotation content separately from the text. We address the fundamental problem of efficiently storing stand-off annotations when applying natural language processing on narrative clinical notes in electronic medical records (EMRs) and efficiently retrieving such annotations that satisfy position constraints. Efficient storage and retrieval of stand-off annotations can facilitate tasks such as mapping unstructured text to electronic medical record ontologies. We first formulate this problem into the interval query problem, for which optimal query/update time is in general logarithm. We next perform a tight time complexity analysis on the basic interval tree query algorithm and show its nonoptimality when being applied to a collection of 13 query types from Allen’s interval algebra. We then study two closely related state-of-the-art interval query algorithms, proposed query reformulations, and augmentations to the second algorithm. Our proposed algorithm achieves logarithmic time stabbing-max query time complexity and solves the stabbing-interval query tasks on all of Allen’s relations in logarithmic time, attaining the theoretic lower bound. Updating time is kept logarithmic and the space requirement is kept linear at the same time. We also discuss interval management in external memory models and higher dimensions. PMID:27478379

An index-based algorithm for fast on-line query processing of latent semantic analysis

PubMed Central

Li, Pohan; Wang, Wei

2017-01-01

Latent Semantic Analysis (LSA) is widely used for finding the documents whose semantic is similar to the query of keywords. Although LSA yield promising similar results, the existing LSA algorithms involve lots of unnecessary operations in similarity computation and candidate check during on-line query processing, which is expensive in terms of time cost and cannot efficiently response the query request especially when the dataset becomes large. In this paper, we study the efficiency problem of on-line query processing for LSA towards efficiently searching the similar documents to a given query. We rewrite the similarity equation of LSA combined with an intermediate value called partial similarity that is stored in a designed index called partial index. For reducing the searching space, we give an approximate form of similarity equation, and then develop an efficient algorithm for building partial index, which skips the partial similarities lower than a given threshold θ. Based on partial index, we develop an efficient algorithm called ILSA for supporting fast on-line query processing. The given query is transformed into a pseudo document vector, and the similarities between query and candidate documents are computed by accumulating the partial similarities obtained from the index nodes corresponds to non-zero entries in the pseudo document vector. Compared to the LSA algorithm, ILSA reduces the time cost of on-line query processing by pruning the candidate documents that are not promising and skipping the operations that make little contribution to similarity scores. Extensive experiments through comparison with LSA have been done, which demonstrate the efficiency and effectiveness of our proposed algorithm. PMID:28520747

An index-based algorithm for fast on-line query processing of latent semantic analysis.

PubMed

Zhang, Mingxi; Li, Pohan; Wang, Wei

2017-01-01

Latent Semantic Analysis (LSA) is widely used for finding the documents whose semantic is similar to the query of keywords. Although LSA yield promising similar results, the existing LSA algorithms involve lots of unnecessary operations in similarity computation and candidate check during on-line query processing, which is expensive in terms of time cost and cannot efficiently response the query request especially when the dataset becomes large. In this paper, we study the efficiency problem of on-line query processing for LSA towards efficiently searching the similar documents to a given query. We rewrite the similarity equation of LSA combined with an intermediate value called partial similarity that is stored in a designed index called partial index. For reducing the searching space, we give an approximate form of similarity equation, and then develop an efficient algorithm for building partial index, which skips the partial similarities lower than a given threshold θ. Based on partial index, we develop an efficient algorithm called ILSA for supporting fast on-line query processing. The given query is transformed into a pseudo document vector, and the similarities between query and candidate documents are computed by accumulating the partial similarities obtained from the index nodes corresponds to non-zero entries in the pseudo document vector. Compared to the LSA algorithm, ILSA reduces the time cost of on-line query processing by pruning the candidate documents that are not promising and skipping the operations that make little contribution to similarity scores. Extensive experiments through comparison with LSA have been done, which demonstrate the efficiency and effectiveness of our proposed algorithm.

Automatically finding relevant citations for clinical guideline development.

PubMed

Bui, Duy Duc An; Jonnalagadda, Siddhartha; Del Fiol, Guilherme

2015-10-01

Literature database search is a crucial step in the development of clinical practice guidelines and systematic reviews. In the age of information technology, the process of literature search is still conducted manually, therefore it is costly, slow and subject to human errors. In this research, we sought to improve the traditional search approach using innovative query expansion and citation ranking approaches. We developed a citation retrieval system composed of query expansion and citation ranking methods. The methods are unsupervised and easily integrated over the PubMed search engine. To validate the system, we developed a gold standard consisting of citations that were systematically searched and screened to support the development of cardiovascular clinical practice guidelines. The expansion and ranking methods were evaluated separately and compared with baseline approaches. Compared with the baseline PubMed expansion, the query expansion algorithm improved recall (80.2% vs. 51.5%) with small loss on precision (0.4% vs. 0.6%). The algorithm could find all citations used to support a larger number of guideline recommendations than the baseline approach (64.5% vs. 37.2%, p<0.001). In addition, the citation ranking approach performed better than PubMed's "most recent" ranking (average precision +6.5%, recall@k +21.1%, p<0.001), PubMed's rank by "relevance" (average precision +6.1%, recall@k +14.8%, p<0.001), and the machine learning classifier that identifies scientifically sound studies from MEDLINE citations (average precision +4.9%, recall@k +4.2%, p<0.001). Our unsupervised query expansion and ranking techniques are more flexible and effective than PubMed's default search engine behavior and the machine learning classifier. Automated citation finding is promising to augment the traditional literature search. Copyright © 2015 Elsevier Inc. All rights reserved.

Digital detection for tobacco control: online reactions to the 2009 U.S. cigarette excise tax increase.

PubMed

Ayers, John W; Althouse, Benjamin M; Ribisl, Kurt M; Emery, Sherry

2014-05-01

The Internet is revolutionizing tobacco control, but few have harnessed the Web for surveillance. We demonstrate for the first time an approach for analyzing aggregate Internet search queries that captures precise changes in population considerations about tobacco. We compared tobacco-related Google queries originating in the United States during the week of the State Children's Health Insurance Program (SCHIP) 2009 cigarette excise tax increase with a historic baseline. Specific queries were then ranked according to their relative increases while also considering approximations of changes in absolute search volume. Individual queries with the largest relative increases the week of the SCHIP tax were "cigarettes Indian reservations" 640% (95% CI, 472-918), "free cigarettes online" 557% (95% CI, 432-756), and "Indian reservations cigarettes" 542% (95% CI, 414-733), amounting to about 7,500 excess searches. By themes, the largest relative increases were tribal cigarettes 246% (95% CI, 228-265), "free" cigarettes 215% (95% CI, 191-242), and cigarette stores 176% (95% CI, 160-193), accounting for 21,000, 27,000, and 90,000 excess queries. All avoidance queries, including those aforementioned themes, relatively increased 150% (95% CI, 144-155) or 550,000 from their baseline. All cessation queries increased 46% (95% CI, 44-48), or 175,000, around SCHIP; including themes for "cold turkey" 19% (95% CI, 11-27) or 2,600, cessation products 47% (95% CI, 44-50) or 78,000, and dubious cessation approaches (e.g., hypnosis) 40% (95% CI, 33-47) or 2,300. The SCHIP tax motivated specific changes in population considerations. Our strategy can support evaluations that temporally link tobacco control measures with instantaneous population reactions, as well as serve as a springboard for traditional studies, for example, including survey questionnaire design.

OS2: Oblivious similarity based searching for encrypted data outsourced to an untrusted domain

PubMed Central

Pervez, Zeeshan; Ahmad, Mahmood; Khattak, Asad Masood; Ramzan, Naeem

2017-01-01

Public cloud storage services are becoming prevalent and myriad data sharing, archiving and collaborative services have emerged which harness the pay-as-you-go business model of public cloud. To ensure privacy and confidentiality often encrypted data is outsourced to such services, which further complicates the process of accessing relevant data by using search queries. Search over encrypted data schemes solve this problem by exploiting cryptographic primitives and secure indexing to identify outsourced data that satisfy the search criteria. Almost all of these schemes rely on exact matching between the encrypted data and search criteria. A few schemes which extend the notion of exact matching to similarity based search, lack realism as those schemes rely on trusted third parties or due to increase storage and computational complexity. In this paper we propose Oblivious Similarity based Search (OS2) for encrypted data. It enables authorized users to model their own encrypted search queries which are resilient to typographical errors. Unlike conventional methodologies, OS2 ranks the search results by using similarity measure offering a better search experience than exact matching. It utilizes encrypted bloom filter and probabilistic homomorphic encryption to enable authorized users to access relevant data without revealing results of search query evaluation process to the untrusted cloud service provider. Encrypted bloom filter based search enables OS2 to reduce search space to potentially relevant encrypted data avoiding unnecessary computation on public cloud. The efficacy of OS2 is evaluated on Google App Engine for various bloom filter lengths on different cloud configurations. PMID:28692697

[Formula: see text]: Oblivious similarity based searching for encrypted data outsourced to an untrusted domain.

PubMed

Pervez, Zeeshan; Ahmad, Mahmood; Khattak, Asad Masood; Ramzan, Naeem; Khan, Wajahat Ali

2017-01-01

Public cloud storage services are becoming prevalent and myriad data sharing, archiving and collaborative services have emerged which harness the pay-as-you-go business model of public cloud. To ensure privacy and confidentiality often encrypted data is outsourced to such services, which further complicates the process of accessing relevant data by using search queries. Search over encrypted data schemes solve this problem by exploiting cryptographic primitives and secure indexing to identify outsourced data that satisfy the search criteria. Almost all of these schemes rely on exact matching between the encrypted data and search criteria. A few schemes which extend the notion of exact matching to similarity based search, lack realism as those schemes rely on trusted third parties or due to increase storage and computational complexity. In this paper we propose Oblivious Similarity based Search ([Formula: see text]) for encrypted data. It enables authorized users to model their own encrypted search queries which are resilient to typographical errors. Unlike conventional methodologies, [Formula: see text] ranks the search results by using similarity measure offering a better search experience than exact matching. It utilizes encrypted bloom filter and probabilistic homomorphic encryption to enable authorized users to access relevant data without revealing results of search query evaluation process to the untrusted cloud service provider. Encrypted bloom filter based search enables [Formula: see text] to reduce search space to potentially relevant encrypted data avoiding unnecessary computation on public cloud. The efficacy of [Formula: see text] is evaluated on Google App Engine for various bloom filter lengths on different cloud configurations.

On Relevance Weight Estimation and Query Expansion.

ERIC Educational Resources Information Center

Robertson, S. E.

1986-01-01

A Bayesian argument is used to suggest modifications to the Robertson and Jones relevance weighting formula to accommodate the addition to the query of terms taken from the relevant documents identified during the search. (Author)

Constructing Topic Models of Internet of Things for Information Processing

PubMed Central

Xin, Jie; Cui, Zhiming; Zhang, Shukui; He, Tianxu; Li, Chunhua; Huang, Haojing

2014-01-01

Internet of Things (IoT) is regarded as a remarkable development of the modern information technology. There is abundant digital products data on the IoT, linking with multiple types of objects/entities. Those associated entities carry rich information and usually in the form of query records. Therefore, constructing high quality topic hierarchies that can capture the term distribution of each product record enables us to better understand users' search intent and benefits tasks such as taxonomy construction, recommendation systems, and other communications solutions for the future IoT. In this paper, we propose a novel record entity topic model (RETM) for IoT environment that is associated with a set of entities and records and a Gibbs sampling-based algorithm is proposed to learn the model. We conduct extensive experiments on real-world datasets and compare our approach with existing methods to demonstrate the advantage of our approach. PMID:25110737

Constructing topic models of Internet of Things for information processing.

PubMed

Xin, Jie; Cui, Zhiming; Zhang, Shukui; He, Tianxu; Li, Chunhua; Huang, Haojing

2014-01-01

Internet of Things (IoT) is regarded as a remarkable development of the modern information technology. There is abundant digital products data on the IoT, linking with multiple types of objects/entities. Those associated entities carry rich information and usually in the form of query records. Therefore, constructing high quality topic hierarchies that can capture the term distribution of each product record enables us to better understand users' search intent and benefits tasks such as taxonomy construction, recommendation systems, and other communications solutions for the future IoT. In this paper, we propose a novel record entity topic model (RETM) for IoT environment that is associated with a set of entities and records and a Gibbs sampling-based algorithm is proposed to learn the model. We conduct extensive experiments on real-world datasets and compare our approach with existing methods to demonstrate the advantage of our approach.

An advanced search engine for patent analytics in medicinal chemistry.

PubMed

Pasche, Emilie; Gobeill, Julien; Teodoro, Douglas; Gaudinat, Arnaud; Vishnykova, Dina; Lovis, Christian; Ruch, Patrick

2012-01-01

Patent collections contain an important amount of medical-related knowledge, but existing tools were reported to lack of useful functionalities. We present here the development of TWINC, an advanced search engine dedicated to patent retrieval in the domain of health and life sciences. Our tool embeds two search modes: an ad hoc search to retrieve relevant patents given a short query and a related patent search to retrieve similar patents given a patent. Both search modes rely on tuning experiments performed during several patent retrieval competitions. Moreover, TWINC is enhanced with interactive modules, such as chemical query expansion, which is of prior importance to cope with various ways of naming biomedical entities. While the related patent search showed promising performances, the ad-hoc search resulted in fairly contrasted results. Nonetheless, TWINC performed well during the Chemathlon task of the PatOlympics competition and experts appreciated its usability.

Web search queries can predict stock market volumes.

PubMed

Bordino, Ilaria; Battiston, Stefano; Caldarelli, Guido; Cristelli, Matthieu; Ukkonen, Antti; Weber, Ingmar

2012-01-01

We live in a computerized and networked society where many of our actions leave a digital trace and affect other people's actions. This has lead to the emergence of a new data-driven research field: mathematical methods of computer science, statistical physics and sociometry provide insights on a wide range of disciplines ranging from social science to human mobility. A recent important discovery is that search engine traffic (i.e., the number of requests submitted by users to search engines on the www) can be used to track and, in some cases, to anticipate the dynamics of social phenomena. Successful examples include unemployment levels, car and home sales, and epidemics spreading. Few recent works applied this approach to stock prices and market sentiment. However, it remains unclear if trends in financial markets can be anticipated by the collective wisdom of on-line users on the web. Here we show that daily trading volumes of stocks traded in NASDAQ-100 are correlated with daily volumes of queries related to the same stocks. In particular, query volumes anticipate in many cases peaks of trading by one day or more. Our analysis is carried out on a unique dataset of queries, submitted to an important web search engine, which enable us to investigate also the user behavior. We show that the query volume dynamics emerges from the collective but seemingly uncoordinated activity of many users. These findings contribute to the debate on the identification of early warnings of financial systemic risk, based on the activity of users of the www.

Web Search Queries Can Predict Stock Market Volumes

PubMed Central

Bordino, Ilaria; Battiston, Stefano; Caldarelli, Guido; Cristelli, Matthieu; Ukkonen, Antti; Weber, Ingmar

2012-01-01

We live in a computerized and networked society where many of our actions leave a digital trace and affect other people’s actions. This has lead to the emergence of a new data-driven research field: mathematical methods of computer science, statistical physics and sociometry provide insights on a wide range of disciplines ranging from social science to human mobility. A recent important discovery is that search engine traffic (i.e., the number of requests submitted by users to search engines on the www) can be used to track and, in some cases, to anticipate the dynamics of social phenomena. Successful examples include unemployment levels, car and home sales, and epidemics spreading. Few recent works applied this approach to stock prices and market sentiment. However, it remains unclear if trends in financial markets can be anticipated by the collective wisdom of on-line users on the web. Here we show that daily trading volumes of stocks traded in NASDAQ-100 are correlated with daily volumes of queries related to the same stocks. In particular, query volumes anticipate in many cases peaks of trading by one day or more. Our analysis is carried out on a unique dataset of queries, submitted to an important web search engine, which enable us to investigate also the user behavior. We show that the query volume dynamics emerges from the collective but seemingly uncoordinated activity of many users. These findings contribute to the debate on the identification of early warnings of financial systemic risk, based on the activity of users of the www. PMID:22829871

Automatic Concept-Based Query Expansion Using Term Relational Pathways Built from a Collection-Specific Association Thesaurus

ERIC Educational Resources Information Center

Lyall-Wilson, Jennifer Rae

2013-01-01

The dissertation research explores an approach to automatic concept-based query expansion to improve search engine performance. It uses a network-based approach for identifying the concept represented by the user's query and is founded on the idea that a collection-specific association thesaurus can be used to create a reasonable representation of…

Advances in nowcasting influenza-like illness rates using search query logs

NASA Astrophysics Data System (ADS)

Lampos, Vasileios; Miller, Andrew C.; Crossan, Steve; Stefansen, Christian

2015-08-01

User-generated content can assist epidemiological surveillance in the early detection and prevalence estimation of infectious diseases, such as influenza. Google Flu Trends embodies the first public platform for transforming search queries to indications about the current state of flu in various places all over the world. However, the original model significantly mispredicted influenza-like illness rates in the US during the 2012-13 flu season. In this work, we build on the previous modeling attempt, proposing substantial improvements. Firstly, we investigate the performance of a widely used linear regularized regression solver, known as the Elastic Net. Then, we expand on this model by incorporating the queries selected by the Elastic Net into a nonlinear regression framework, based on a composite Gaussian Process. Finally, we augment the query-only predictions with an autoregressive model, injecting prior knowledge about the disease. We assess predictive performance using five consecutive flu seasons spanning from 2008 to 2013 and qualitatively explain certain shortcomings of the previous approach. Our results indicate that a nonlinear query modeling approach delivers the lowest cumulative nowcasting error, and also suggest that query information significantly improves autoregressive inferences, obtaining state-of-the-art performance.

Advances in nowcasting influenza-like illness rates using search query logs.

PubMed

Lampos, Vasileios; Miller, Andrew C; Crossan, Steve; Stefansen, Christian

2015-08-03

User-generated content can assist epidemiological surveillance in the early detection and prevalence estimation of infectious diseases, such as influenza. Google Flu Trends embodies the first public platform for transforming search queries to indications about the current state of flu in various places all over the world. However, the original model significantly mispredicted influenza-like illness rates in the US during the 2012-13 flu season. In this work, we build on the previous modeling attempt, proposing substantial improvements. Firstly, we investigate the performance of a widely used linear regularized regression solver, known as the Elastic Net. Then, we expand on this model by incorporating the queries selected by the Elastic Net into a nonlinear regression framework, based on a composite Gaussian Process. Finally, we augment the query-only predictions with an autoregressive model, injecting prior knowledge about the disease. We assess predictive performance using five consecutive flu seasons spanning from 2008 to 2013 and qualitatively explain certain shortcomings of the previous approach. Our results indicate that a nonlinear query modeling approach delivers the lowest cumulative nowcasting error, and also suggest that query information significantly improves autoregressive inferences, obtaining state-of-the-art performance.

A spatial data handling system for retrieval of images by unrestricted regions of user interest

NASA Technical Reports Server (NTRS)

Dorfman, Erik; Cromp, Robert F.

1992-01-01

The Intelligent Data Management (IDM) project at NASA/Goddard Space Flight Center has prototyped an Intelligent Information Fusion System (IIFS), which automatically ingests metadata from remote sensor observations into a large catalog which is directly queryable by end-users. The greatest challenge in the implementation of this catalog was supporting spatially-driven searches, where the user has a possible complex region of interest and wishes to recover those images that overlap all or simply a part of that region. A spatial data management system is described, which is capable of storing and retrieving records of image data regardless of their source. This system was designed and implemented as part of the IIFS catalog. A new data structure, called a hypercylinder, is central to the design. The hypercylinder is specifically tailored for data distributed over the surface of a sphere, such as satellite observations of the Earth or space. Operations on the hypercylinder are regulated by two expert systems. The first governs the ingest of new metadata records, and maintains the efficiency of the data structure as it grows. The second translates, plans, and executes users' spatial queries, performing incremental optimization as partial query results are returned.

Intelligent web image retrieval system

NASA Astrophysics Data System (ADS)

Hong, Sungyong; Lee, Chungwoo; Nah, Yunmook

2001-07-01

Recently, the web sites such as e-business sites and shopping mall sites deal with lots of image information. To find a specific image from these image sources, we usually use web search engines or image database engines which rely on keyword only retrievals or color based retrievals with limited search capabilities. This paper presents an intelligent web image retrieval system. We propose the system architecture, the texture and color based image classification and indexing techniques, and representation schemes of user usage patterns. The query can be given by providing keywords, by selecting one or more sample texture patterns, by assigning color values within positional color blocks, or by combining some or all of these factors. The system keeps track of user's preferences by generating user query logs and automatically add more search information to subsequent user queries. To show the usefulness of the proposed system, some experimental results showing recall and precision are also explained.

Do economic equality and generalized trust inhibit academic dishonesty? Evidence from state-level search-engine queries.

PubMed

Neville, Lukas

2012-04-01

What effect does economic inequality have on academic integrity? Using data from search-engine queries made between 2003 and 2011 on Google and state-level measures of income inequality and generalized trust, I found that academically dishonest searches (queries seeking term-paper mills and help with cheating) were more likely to come from states with higher income inequality and lower levels of generalized trust. These relations persisted even when controlling for contextual variables, such as average income and the number of colleges per capita. The relation between income inequality and academic dishonesty was fully mediated by generalized trust. When there is higher economic inequality, people are less likely to view one another as trustworthy. This lower generalized trust, in turn, is associated with a greater prevalence of academic dishonesty. These results might explain previous findings on the effectiveness of honor codes.

WORDGRAPH: Keyword-in-Context Visualization for NETSPEAK's Wildcard Search.

PubMed

Riehmann, Patrick; Gruendl, Henning; Potthast, Martin; Trenkmann, Martin; Stein, Benno; Froehlich, Benno

2012-09-01

The WORDGRAPH helps writers in visually choosing phrases while writing a text. It checks for the commonness of phrases and allows for the retrieval of alternatives by means of wildcard queries. To support such queries, we implement a scalable retrieval engine, which returns high-quality results within milliseconds using a probabilistic retrieval strategy. The results are displayed as WORDGRAPH visualization or as a textual list. The graphical interface provides an effective means for interactive exploration of search results using filter techniques, query expansion, and navigation. Our observations indicate that, of three investigated retrieval tasks, the textual interface is sufficient for the phrase verification task, wherein both interfaces support context-sensitive word choice, and the WORDGRAPH best supports the exploration of a phrase's context or the underlying corpus. Our user study confirms these observations and shows that WORDGRAPH is generally the preferred interface over the textual result list for queries containing multiple wildcards.

Experiments with Cross-Language Information Retrieval on a Health Portal for Psychology and Psychotherapy.

PubMed

Andrenucci, Andrea

2016-01-01

Few studies have been performed within cross-language information retrieval (CLIR) in the field of psychology and psychotherapy. The aim of this paper is to to analyze and assess the quality of available query translation methods for CLIR on a health portal for psychology. A test base of 100 user queries, 50 Multi Word Units (WUs) and 50 Single WUs, was used. Swedish was the source language and English the target language. Query translation methods based on machine translation (MT) and dictionary look-up were utilized in order to submit query translations to two search engines: Google Site Search and Quick Ask. Standard IR evaluation measures and a qualitative analysis were utilized to assess the results. The lexicon extracted with word alignment of the portal's parallel corpus provided better statistical results among dictionary look-ups. Google Translate provided more linguistically correct translations overall and also delivered better retrieval results in MT.

Thesaurus-Enhanced Search Interfaces.

ERIC Educational Resources Information Center

Shiri, Ali Asghar; Revie, Crawford; Chowdhury, Gobinda

2002-01-01

Discussion of user interfaces to information retrieval systems focuses on interfaces that incorporate thesauri as part of their searching and browsing facilities. Discusses research literature related to information searching behavior, information retrieval interface evaluation, search term selection, and query expansion; and compares thesaurus…

A Survey in Indexing and Searching XML Documents.

ERIC Educational Resources Information Center

Luk, Robert W. P.; Leong, H. V.; Dillon, Tharam S.; Chan, Alvin T. S.; Croft, W. Bruce; Allan, James

2002-01-01

Discussion of XML focuses on indexing techniques for XML documents, grouping them into flat-file, semistructured, and structured indexing paradigms. Highlights include searching techniques, including full text search and multistage search; search result presentations; database and information retrieval system integration; XML query languages; and…

FTree query construction for virtual screening: a statistical analysis.

PubMed

Gerlach, Christof; Broughton, Howard; Zaliani, Andrea

2008-02-01

FTrees (FT) is a known chemoinformatic tool able to condense molecular descriptions into a graph object and to search for actives in large databases using graph similarity. The query graph is classically derived from a known active molecule, or a set of actives, for which a similar compound has to be found. Recently, FT similarity has been extended to fragment space, widening its capabilities. If a user were able to build a knowledge-based FT query from information other than a known active structure, the similarity search could be combined with other, normally separate, fields like de-novo design or pharmacophore searches. With this aim in mind, we performed a comprehensive analysis of several databases in terms of FT description and provide a basic statistical analysis of the FT spaces so far at hand. Vendors' catalogue collections and MDDR as a source of potential or known "actives", respectively, have been used. With the results reported herein, a set of ranges, mean values and standard deviations for several query parameters are presented in order to set a reference guide for the users. Applications on how to use this information in FT query building are also provided, using a newly built 3D-pharmacophore from 57 5HT-1F agonists and a published one which was used for virtual screening for tRNA-guanine transglycosylase (TGT) inhibitors.

FTree query construction for virtual screening: a statistical analysis

NASA Astrophysics Data System (ADS)

Gerlach, Christof; Broughton, Howard; Zaliani, Andrea

2008-02-01

FTrees (FT) is a known chemoinformatic tool able to condense molecular descriptions into a graph object and to search for actives in large databases using graph similarity. The query graph is classically derived from a known active molecule, or a set of actives, for which a similar compound has to be found. Recently, FT similarity has been extended to fragment space, widening its capabilities. If a user were able to build a knowledge-based FT query from information other than a known active structure, the similarity search could be combined with other, normally separate, fields like de-novo design or pharmacophore searches. With this aim in mind, we performed a comprehensive analysis of several databases in terms of FT description and provide a basic statistical analysis of the FT spaces so far at hand. Vendors' catalogue collections and MDDR as a source of potential or known "actives", respectively, have been used. With the results reported herein, a set of ranges, mean values and standard deviations for several query parameters are presented in order to set a reference guide for the users. Applications on how to use this information in FT query building are also provided, using a newly built 3D-pharmacophore from 57 5HT-1F agonists and a published one which was used for virtual screening for tRNA-guanine transglycosylase (TGT) inhibitors.

CIM explorer--intelligent tool for exploring the ICD Romanian version.

PubMed

Filip, F; Haras, C

2000-01-01

The CIM Explorer software provides us with an intelligent interface for exploring the Romanian version of the International Classification of Diseases (in Romanian Clasificarea Internationala a Maladiilor-CIM). The ICD was transposed from its initial appearance as a printed document into a database. The classification can be accessed in two modes: "Navigation" and "Code" and queried in the "Key words" mode. In the last mode CIM Explorer program searches for the right content of the ICD records starting from naturally written expressions which it "understands". As a results it returns all the records containing the key words regardless their grammatical form. This program implements the specificity of the Romanian language where the words are made up from a root and a flexional termination.

Combining structured and unstructured data to identify a cohort of ICU patients who received dialysis

PubMed Central

Abhyankar, Swapna; Demner-Fushman, Dina; Callaghan, Fiona M; McDonald, Clement J

2014-01-01

Objective To develop a generalizable method for identifying patient cohorts from electronic health record (EHR) data—in this case, patients having dialysis—that uses simple information retrieval (IR) tools. Methods We used the coded data and clinical notes from the 24 506 adult patients in the Multiparameter Intelligent Monitoring in Intensive Care database to identify patients who had dialysis. We used SQL queries to search the procedure, diagnosis, and coded nursing observations tables based on ICD-9 and local codes. We used a domain-specific search engine to find clinical notes containing terms related to dialysis. We manually validated the available records for a 10% random sample of patients who potentially had dialysis and a random sample of 200 patients who were not identified as having dialysis based on any of the sources. Results We identified 1844 patients that potentially had dialysis: 1481 from the three coded sources and 1624 from the clinical notes. Precision for identifying dialysis patients based on available data was estimated to be 78.4% (95% CI 71.9% to 84.2%) and recall was 100% (95% CI 86% to 100%). Conclusions Combining structured EHR data with information from clinical notes using simple queries increases the utility of both types of data for cohort identification. Patients identified by more than one source are more likely to meet the inclusion criteria; however, including patients found in any of the sources increases recall. This method is attractive because it is available to researchers with access to EHR data and off-the-shelf IR tools. PMID:24384230

Omokage search: shape similarity search service for biomolecular structures in both the PDB and EMDB.

PubMed

Suzuki, Hirofumi; Kawabata, Takeshi; Nakamura, Haruki

2016-02-15

Omokage search is a service to search the global shape similarity of biological macromolecules and their assemblies, in both the Protein Data Bank (PDB) and Electron Microscopy Data Bank (EMDB). The server compares global shapes of assemblies independent of sequence order and number of subunits. As a search query, the user inputs a structure ID (PDB ID or EMDB ID) or uploads an atomic model or 3D density map to the server. The search is performed usually within 1 min, using one-dimensional profiles (incremental distance rank profiles) to characterize the shapes. Using the gmfit (Gaussian mixture model fitting) program, the found structures are fitted onto the query structure and their superimposed structures are displayed on the Web browser. Our service provides new structural perspectives to life science researchers. Omokage search is freely accessible at http://pdbj.org/omokage/. © The Author 2015. Published by Oxford University Press.

Understanding PubMed user search behavior through log analysis.

PubMed

Islamaj Dogan, Rezarta; Murray, G Craig; Névéol, Aurélie; Lu, Zhiyong

2009-01-01

This article reports on a detailed investigation of PubMed users' needs and behavior as a step toward improving biomedical information retrieval. PubMed is providing free service to researchers with access to more than 19 million citations for biomedical articles from MEDLINE and life science journals. It is accessed by millions of users each day. Efficient search tools are crucial for biomedical researchers to keep abreast of the biomedical literature relating to their own research. This study provides insight into PubMed users' needs and their behavior. This investigation was conducted through the analysis of one month of log data, consisting of more than 23 million user sessions and more than 58 million user queries. Multiple aspects of users' interactions with PubMed are characterized in detail with evidence from these logs. Despite having many features in common with general Web searches, biomedical information searches have unique characteristics that are made evident in this study. PubMed users are more persistent in seeking information and they reformulate queries often. The three most frequent types of search are search by author name, search by gene/protein, and search by disease. Use of abbreviation in queries is very frequent. Factors such as result set size influence users' decisions. Analysis of characteristics such as these plays a critical role in identifying users' information needs and their search habits. In turn, such an analysis also provides useful insight for improving biomedical information retrieval.Database URL:http://www.ncbi.nlm.nih.gov/PubMed.

Multitasking Web Searching and Implications for Design.

ERIC Educational Resources Information Center

Ozmutlu, Seda; Ozmutlu, H. C.; Spink, Amanda

2003-01-01

Findings from a study of users' multitasking searches on Web search engines include: multitasking searches are a noticeable user behavior; multitasking search sessions are longer than regular search sessions in terms of queries per session and duration; both Excite and AlltheWeb.com users search for about three topics per multitasking session and…

A Method for Search Engine Selection using Thesaurus for Selective Meta-Search Engine

NASA Astrophysics Data System (ADS)

Goto, Shoji; Ozono, Tadachika; Shintani, Toramatsu

In this paper, we propose a new method for selecting search engines on WWW for selective meta-search engine. In selective meta-search engine, a method is needed that would enable selecting appropriate search engines for users' queries. Most existing methods use statistical data such as document frequency. These methods may select inappropriate search engines if a query contains polysemous words. In this paper, we describe an search engine selection method based on thesaurus. In our method, a thesaurus is constructed from documents in a search engine and is used as a source description of the search engine. The form of a particular thesaurus depends on the documents used for its construction. Our method enables search engine selection by considering relationship between terms and overcomes the problems caused by polysemous words. Further, our method does not have a centralized broker maintaining data, such as document frequency for all search engines. As a result, it is easy to add a new search engine, and meta-search engines become more scalable with our method compared to other existing methods.

A comparison of Boolean-based retrieval to the WAIS system for retrieval of aeronautical information

NASA Technical Reports Server (NTRS)

Marchionini, Gary; Barlow, Diane

1994-01-01

An evaluation of an information retrieval system using a Boolean-based retrieval engine and inverted file architecture and WAIS, which uses a vector-based engine, was conducted. Four research questions in aeronautical engineering were used to retrieve sets of citations from the NASA Aerospace Database which was mounted on a WAIS server and available through Dialog File 108 which served as the Boolean-based system (BBS). High recall and high precision searches were done in the BBS and terse and verbose queries were used in the WAIS condition. Precision values for the WAIS searches were consistently above the precision values for high recall BBS searches and consistently below the precision values for high precision BBS searches. Terse WAIS queries gave somewhat better precision performance than verbose WAIS queries. In every case, a small number of relevant documents retrieved by one system were not retrieved by the other, indicating the incomplete nature of the results from either retrieval system. Relevant documents in the WAIS searches were found to be randomly distributed in the retrieved sets rather than distributed by ranks. Advantages and limitations of both types of systems are discussed.

Utility of Web search query data in testing theoretical assumptions about mephedrone.

PubMed

Kapitány-Fövény, Máté; Demetrovics, Zsolt

2017-05-01

With growing access to the Internet, people who use drugs and traffickers started to obtain information about novel psychoactive substances (NPS) via online platforms. This paper aims to analyze whether a decreasing Web interest in formerly banned substances-cocaine, heroin, and MDMA-and the legislative status of mephedrone predict Web interest about this NPS. Google Trends was used to measure changes of Web interest on cocaine, heroin, MDMA, and mephedrone. Google search results for mephedrone within the same time frame were analyzed and categorized. Web interest about classic drugs found to be more persistent. Regarding geographical distribution, location of Web searches for heroin and cocaine was less centralized. Illicit status of mephedrone was a negative predictor of its Web search query rates. The connection between mephedrone-related Web search rates and legislative status of this substance was significantly mediated by ecstasy-related Web search queries, the number of documentaries, and forum/blog entries about mephedrone. The results might provide support for the hypothesis that mephedrone's popularity was highly correlated with its legal status as well as it functioned as a potential substitute for MDMA. Google Trends was found to be a useful tool for testing theoretical assumptions about NPS. Copyright © 2017 John Wiley & Sons, Ltd.

Improved nearest codeword search scheme using a tighter kick-out condition

NASA Astrophysics Data System (ADS)

Hwang, Kuo-Feng; Chang, Chin-Chen

2001-09-01

Using a tighter kick-out condition as a faster approach to nearest codeword searches is proposed. The proposed scheme finds the nearest codeword that is identical to the one found using a full search. However, using our scheme, the search time is much shorter. Our scheme first establishes a tighter kick-out condition. Then, the temporal nearest codeword can be obtained from the codewords that survive the tighter condition. Finally, the temporal nearest codeword cooperatives with the query vector to constitute a better kick-out condition. In other words, more codewords can be excluded without actually computing the distances between the bypassed codewords and the query vector. Comparison to previous work are included to present the benefits of the proposed scheme in relation to search time.

Internet search query analysis can be used to demonstrate the rapidly increasing public awareness of palliative care in the USA.

PubMed

McLean, Sarah; Lennon, Paul; Glare, Paul

2017-01-27

A lack of public awareness of palliative care (PC) has been identified as one of the main barriers to appropriate PC access. Internet search query analysis is a novel methodology, which has been effectively used in surveillance of infectious diseases, and can be used to monitor public awareness of health-related topics. We aimed to demonstrate the utility of internet search query analysis to evaluate changes in public awareness of PC in the USA between 2005 and 2015. Google Trends provides a referenced score for the popularity of a search term, for defined regions over defined time periods. The popularity of the search term 'palliative care' was measured monthly between 1/1/2005 and 31/12/2015 in the USA and in the UK. Results were analysed using independent t-tests and joinpoint analysis. The mean monthly popularity of the search term increased between 2008-2009 (p<0.001), 2011-2012 (p<0.001), 2013-2014 (p=0.004) and 2014-2015 (p=0.002) in the USA. Joinpoint analysis was used to evaluate the monthly percentage change (MPC) in the popularity of the search term. In the USA, the MPC increase was 0.6%/month (p<0.05); in the UK the MPC of 0.05% was non-significant. Although internet search query surveillance is a novel methodology, it is freely accessible and has significant potential to monitor health-seeking behaviour among the public. PC is rapidly growing in the USA, and the rapidly increasing public awareness of PC as demonstrated in this study, in comparison with the UK, where PC is relatively well established is encouraging in increasingly ensuring appropriate PC access for all. 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/.

The Word Outside and the Pictures in Our Heads: Contingent Framing Effects of Labels on Health Policy Preferences by Political Ideology.

PubMed

Roh, Sungjong; Niederdeppe, Jeff

2016-09-01

This study uses data from systematic Web image search results and two randomized survey experiments to analyze how frames commonly used in public debates about health issues, operationalized here as alternative word choices, influence public support for health policy reforms. In Study 1, analyses of Bing (N = 1,719), Google (N = 1,872), and Yahoo Images (N = 1,657) search results suggest that the images returned from the search query "sugar-sweetened beverage" are more likely to evoke health-related concepts than images returned from a search query about "soda." In contrast, "soda" search queries were more likely to incorporate brand-related concepts than "sugar-sweetened beverage" search queries. In Study 2, participants (N = 206) in a controlled Web experiment rated their support for policies to reduce consumption of these drinks. As expected, strong liberals had more support for policies designed to reduce the consumption of these drinks when the policies referenced "soda" compared to "sugar-sweetened beverage." To the contrary, items describing these drinks as "soda" produced lower policy support than items describing them as "sugar-sweetened beverage" among strong conservatives. In Study 3, participants (N = 1,000) in a national telephone survey experiment rated their support for a similar set of policies. Results conceptually replicated the previous Web-based experiment, such that strong liberals reported greater support for a penny-per-ounce taxation when labeled "soda" versus "sugar-sweetened beverages." In both Studies 2 and 3, more respondents referred to brand-related concepts in response to questions about "sugar-sweetened beverages" compared to "soda." We conclude with a discussion of theoretical and methodological implications for studying framing effects of labels.

KBGIS-II: A knowledge-based geographic information system

NASA Technical Reports Server (NTRS)

Smith, Terence; Peuquet, Donna; Menon, Sudhakar; Agarwal, Pankaj

1986-01-01

The architecture and working of a recently implemented Knowledge-Based Geographic Information System (KBGIS-II), designed to satisfy several general criteria for the GIS, is described. The system has four major functions including query-answering, learning and editing. The main query finds constrained locations for spatial objects that are describable in a predicate-calculus based spatial object language. The main search procedures include a family of constraint-satisfaction procedures that use a spatial object knowledge base to search efficiently for complex spatial objects in large, multilayered spatial data bases. These data bases are represented in quadtree form. The search strategy is designed to reduce the computational cost of search in the average case. The learning capabilities of the system include the addition of new locations of complex spatial objects to the knowledge base as queries are answered, and the ability to learn inductively definitions of new spatial objects from examples. The new definitions are added to the knowledge base by the system. The system is performing all its designated tasks successfully. Future reports will relate performance characteristics of the system.

CDAPubMed: a browser extension to retrieve EHR-based biomedical literature.

PubMed

Perez-Rey, David; Jimenez-Castellanos, Ana; Garcia-Remesal, Miguel; Crespo, Jose; Maojo, Victor

2012-04-05

Over the last few decades, the ever-increasing output of scientific publications has led to new challenges to keep up to date with the literature. In the biomedical area, this growth has introduced new requirements for professionals, e.g., physicians, who have to locate the exact papers that they need for their clinical and research work amongst a huge number of publications. Against this backdrop, novel information retrieval methods are even more necessary. While web search engines are widespread in many areas, facilitating access to all kinds of information, additional tools are required to automatically link information retrieved from these engines to specific biomedical applications. In the case of clinical environments, this also means considering aspects such as patient data security and confidentiality or structured contents, e.g., electronic health records (EHRs). In this scenario, we have developed a new tool to facilitate query building to retrieve scientific literature related to EHRs. We have developed CDAPubMed, an open-source web browser extension to integrate EHR features in biomedical literature retrieval approaches. Clinical users can use CDAPubMed to: (i) load patient clinical documents, i.e., EHRs based on the Health Level 7-Clinical Document Architecture Standard (HL7-CDA), (ii) identify relevant terms for scientific literature search in these documents, i.e., Medical Subject Headings (MeSH), automatically driven by the CDAPubMed configuration, which advanced users can optimize to adapt to each specific situation, and (iii) generate and launch literature search queries to a major search engine, i.e., PubMed, to retrieve citations related to the EHR under examination. CDAPubMed is a platform-independent tool designed to facilitate literature searching using keywords contained in specific EHRs. CDAPubMed is visually integrated, as an extension of a widespread web browser, within the standard PubMed interface. It has been tested on a public dataset of HL7-CDA documents, returning significantly fewer citations since queries are focused on characteristics identified within the EHR. For instance, compared with more than 200,000 citations retrieved by breast neoplasm, fewer than ten citations were retrieved when ten patient features were added using CDAPubMed. This is an open source tool that can be freely used for non-profit purposes and integrated with other existing systems.

CDAPubMed: a browser extension to retrieve EHR-based biomedical literature

PubMed Central

2012-01-01

Background Over the last few decades, the ever-increasing output of scientific publications has led to new challenges to keep up to date with the literature. In the biomedical area, this growth has introduced new requirements for professionals, e.g., physicians, who have to locate the exact papers that they need for their clinical and research work amongst a huge number of publications. Against this backdrop, novel information retrieval methods are even more necessary. While web search engines are widespread in many areas, facilitating access to all kinds of information, additional tools are required to automatically link information retrieved from these engines to specific biomedical applications. In the case of clinical environments, this also means considering aspects such as patient data security and confidentiality or structured contents, e.g., electronic health records (EHRs). In this scenario, we have developed a new tool to facilitate query building to retrieve scientific literature related to EHRs. Results We have developed CDAPubMed, an open-source web browser extension to integrate EHR features in biomedical literature retrieval approaches. Clinical users can use CDAPubMed to: (i) load patient clinical documents, i.e., EHRs based on the Health Level 7-Clinical Document Architecture Standard (HL7-CDA), (ii) identify relevant terms for scientific literature search in these documents, i.e., Medical Subject Headings (MeSH), automatically driven by the CDAPubMed configuration, which advanced users can optimize to adapt to each specific situation, and (iii) generate and launch literature search queries to a major search engine, i.e., PubMed, to retrieve citations related to the EHR under examination. Conclusions CDAPubMed is a platform-independent tool designed to facilitate literature searching using keywords contained in specific EHRs. CDAPubMed is visually integrated, as an extension of a widespread web browser, within the standard PubMed interface. It has been tested on a public dataset of HL7-CDA documents, returning significantly fewer citations since queries are focused on characteristics identified within the EHR. For instance, compared with more than 200,000 citations retrieved by breast neoplasm, fewer than ten citations were retrieved when ten patient features were added using CDAPubMed. This is an open source tool that can be freely used for non-profit purposes and integrated with other existing systems. PMID:22480327

Evolution of Query Optimization Methods

NASA Astrophysics Data System (ADS)

Hameurlain, Abdelkader; Morvan, Franck

Query optimization is the most critical phase in query processing. In this paper, we try to describe synthetically the evolution of query optimization methods from uniprocessor relational database systems to data Grid systems through parallel, distributed and data integration systems. We point out a set of parameters to characterize and compare query optimization methods, mainly: (i) size of the search space, (ii) type of method (static or dynamic), (iii) modification types of execution plans (re-optimization or re-scheduling), (iv) level of modification (intra-operator and/or inter-operator), (v) type of event (estimation errors, delay, user preferences), and (vi) nature of decision-making (centralized or decentralized control).

Using internet searches for influenza surveillance.

PubMed

Polgreen, Philip M; Chen, Yiling; Pennock, David M; Nelson, Forrest D

2008-12-01

The Internet is an important source of health information. Thus, the frequency of Internet searches may provide information regarding infectious disease activity. As an example, we examined the relationship between searches for influenza and actual influenza occurrence. Using search queries from the Yahoo! search engine ( http://search.yahoo.com ) from March 2004 through May 2008, we counted daily unique queries originating in the United States that contained influenza-related search terms. Counts were divided by the total number of searches, and the resulting daily fraction of searches was averaged over the week. We estimated linear models, using searches with 1-10-week lead times as explanatory variables to predict the percentage of cultures positive for influenza and deaths attributable to pneumonia and influenza in the United States. With use of the frequency of searches, our models predicted an increase in cultures positive for influenza 1-3 weeks in advance of when they occurred (P < .001), and similar models predicted an increase in mortality attributable to pneumonia and influenza up to 5 weeks in advance (P < .001). Search-term surveillance may provide an additional tool for disease surveillance.

SW#db: GPU-Accelerated Exact Sequence Similarity Database Search.

PubMed

Korpar, Matija; Šošić, Martin; Blažeka, Dino; Šikić, Mile

2015-01-01

In recent years we have witnessed a growth in sequencing yield, the number of samples sequenced, and as a result-the growth of publicly maintained sequence databases. The increase of data present all around has put high requirements on protein similarity search algorithms with two ever-opposite goals: how to keep the running times acceptable while maintaining a high-enough level of sensitivity. The most time consuming step of similarity search are the local alignments between query and database sequences. This step is usually performed using exact local alignment algorithms such as Smith-Waterman. Due to its quadratic time complexity, alignments of a query to the whole database are usually too slow. Therefore, the majority of the protein similarity search methods prior to doing the exact local alignment apply heuristics to reduce the number of possible candidate sequences in the database. However, there is still a need for the alignment of a query sequence to a reduced database. In this paper we present the SW#db tool and a library for fast exact similarity search. Although its running times, as a standalone tool, are comparable to the running times of BLAST, it is primarily intended to be used for exact local alignment phase in which the database of sequences has already been reduced. It uses both GPU and CPU parallelization and was 4-5 times faster than SSEARCH, 6-25 times faster than CUDASW++ and more than 20 times faster than SSW at the time of writing, using multiple queries on Swiss-prot and Uniref90 databases.

Effective Filtering of Query Results on Updated User Behavioral Profiles in Web Mining

PubMed Central

Sadesh, S.; Suganthe, R. C.

2015-01-01

Web with tremendous volume of information retrieves result for user related queries. With the rapid growth of web page recommendation, results retrieved based on data mining techniques did not offer higher performance filtering rate because relationships between user profile and queries were not analyzed in an extensive manner. At the same time, existing user profile based prediction in web data mining is not exhaustive in producing personalized result rate. To improve the query result rate on dynamics of user behavior over time, Hamilton Filtered Regime Switching User Query Probability (HFRS-UQP) framework is proposed. HFRS-UQP framework is split into two processes, where filtering and switching are carried out. The data mining based filtering in our research work uses the Hamilton Filtering framework to filter user result based on personalized information on automatic updated profiles through search engine. Maximized result is fetched, that is, filtered out with respect to user behavior profiles. The switching performs accurate filtering updated profiles using regime switching. The updating in profile change (i.e., switches) regime in HFRS-UQP framework identifies the second- and higher-order association of query result on the updated profiles. Experiment is conducted on factors such as personalized information search retrieval rate, filtering efficiency, and precision ratio. PMID:26221626

MedlinePlus Connect: Web Application

MedlinePlus

... will result in a query to the MedlinePlus search engine. If you specify a code and the name/ ... system or problem code, will use the MedlinePlus search engine (English only): https://connect.medlineplus.gov/application?mainSearchCriteria. ...

Keeping Dublin Core Simple: Cross-Domain Discovery or Resource Description?; First Steps in an Information Commerce Economy: Digital Rights Management in the Emerging E-Book Environment; Interoperability: Digital Rights Management and the Emerging EBook Environment; Searching the Deep Web: Direct Query Engine Applications at the Department of Energy.

ERIC Educational Resources Information Center

Lagoze, Carl; Neylon, Eamonn; Mooney, Stephen; Warnick, Walter L.; Scott, R. L.; Spence, Karen J.; Johnson, Lorrie A.; Allen, Valerie S.; Lederman, Abe

2001-01-01

Includes four articles that discuss Dublin Core metadata, digital rights management and electronic books, including interoperability; and directed query engines, a type of search engine designed to access resources on the deep Web that is being used at the Department of Energy. (LRW)

An assessment of the visibility of MeSH-indexed medical web catalogs through search engines.

PubMed

Zweigenbaum, P; Darmoni, S J; Grabar, N; Douyère, M; Benichou, J

2002-01-01

Manually indexed Internet health catalogs such as CliniWeb or CISMeF provide resources for retrieving high-quality health information. Users of these quality-controlled subject gateways are most often referred to them by general search engines such as Google, AltaVista, etc. This raises several questions, among which the following: what is the relative visibility of medical Internet catalogs through search engines? This study addresses this issue by measuring and comparing the visibility of six major, MeSH-indexed health catalogs through four different search engines (AltaVista, Google, Lycos, Northern Light) in two languages (English and French). Over half a million queries were sent to the search engines; for most of these search engines, according to our measures at the time the queries were sent, the most visible catalog for English MeSH terms was CliniWeb and the most visible one for French MeSH terms was CISMeF.

Evaluating Open-Source Full-Text Search Engines for Matching ICD-10 Codes.

PubMed

Jurcău, Daniel-Alexandru; Stoicu-Tivadar, Vasile

2016-01-01

This research presents the results of evaluating multiple free, open-source engines on matching ICD-10 diagnostic codes via full-text searches. The study investigates what it takes to get an accurate match when searching for a specific diagnostic code. For each code the evaluation starts by extracting the words that make up its text and continues with building full-text search queries from the combinations of these words. The queries are then run against all the ICD-10 codes until a match indicates the code in question as a match with the highest relative score. This method identifies the minimum number of words that must be provided in order for the search engines choose the desired entry. The engines analyzed include a popular Java-based full-text search engine, a lightweight engine written in JavaScript which can even execute on the user's browser, and two popular open-source relational database management systems.

CITE NLM: Natural-Language Searching in an Online Catalog.

ERIC Educational Resources Information Center

Doszkocs, Tamas E.

1983-01-01

The National Library of Medicine's Current Information Transfer in English public access online catalog offers unique subject search capabilities--natural-language query input, automatic medical subject headings display, closest match search strategy, ranked document output, dynamic end user feedback for search refinement. References, description…

Peeling the Onion: Okapi System Architecture and Software Design Issues.

ERIC Educational Resources Information Center

Jones, S.; And Others

1997-01-01

Discusses software design issues for Okapi, an information retrieval system that incorporates both search engine and user interface and supports weighted searching, relevance feedback, and query expansion. The basic search system, adjacency searching, and moving toward a distributed system are discussed. (Author/LRW)

NASA Taxonomy 2.0 Project Overview

NASA Technical Reports Server (NTRS)

Dutra, Jayne; Busch, Joseph

2004-01-01

This viewgraph presentation reviews the project to develop a Taxonomy for NASA. The benefits of this project are: Make it easy for various audiences to find relevant information from NASA programs quickly, specifically (1) Provide easy access for NASA Web resources (2) Information integration for unified queries and management reporting ve search results targeted to user interests the ability to move content through the enterprise to where it is needed most (3) Facilitate Records Management and Retention Requirements. In addition the project will assist NASA in complying with E-Government Act of 2002 and prepare NASA to participate in federal projects.

Measuring Up: Implementing a Dental Quality Measure in the Electronic Health Record Context

PubMed Central

Bhardwaj, Aarti; Ramoni, Rachel; Kalenderian, Elsbeth; Neumann, Ana; Hebballi, Nutan B; White, Joel M; McClellan, Lyle; Walji, Muhammad F

2015-01-01

Background Quality improvement requires quality measures that are validly implementable. In this work, we assessed the feasibility and performance of an automated electronic Meaningful Use dental clinical quality measure (percentage of children who received fluoride varnish). Methods We defined how to implement the automated measure queries in a dental electronic health record (EHR). Within records identified through automated query, we manually reviewed a subsample to assess the performance of the query. Results The automated query found 71.0% of patients to have had fluoride varnish compared to 77.6% found using the manual chart review. The automated quality measure performance was 90.5% sensitivity, 90.8% specificity, 96.9% positive predictive value, and 75.2% negative predictive value. Conclusions Our findings support the feasibility of automated dental quality measure queries in the context of sufficient structured data. Information noted only in the free text rather than in structured data would require natural language processing approaches to effectively query. Practical Implications To participate in self-directed quality improvement, dental clinicians must embrace the accountability era. Commitment to quality will require enhanced documentation in order to support near-term automated calculation of quality measures. PMID:26562736

A Fast Exact k-Nearest Neighbors Algorithm for High Dimensional Search Using k-Means Clustering and Triangle Inequality.

PubMed

Wang, Xueyi

2012-02-08

The k-nearest neighbors (k-NN) algorithm is a widely used machine learning method that finds nearest neighbors of a test object in a feature space. We present a new exact k-NN algorithm called kMkNN (k-Means for k-Nearest Neighbors) that uses the k-means clustering and the triangle inequality to accelerate the searching for nearest neighbors in a high dimensional space. The kMkNN algorithm has two stages. In the buildup stage, instead of using complex tree structures such as metric trees, kd-trees, or ball-tree, kMkNN uses a simple k-means clustering method to preprocess the training dataset. In the searching stage, given a query object, kMkNN finds nearest training objects starting from the nearest cluster to the query object and uses the triangle inequality to reduce the distance calculations. Experiments show that the performance of kMkNN is surprisingly good compared to the traditional k-NN algorithm and tree-based k-NN algorithms such as kd-trees and ball-trees. On a collection of 20 datasets with up to 10(6) records and 10(4) dimensions, kMkNN shows a 2-to 80-fold reduction of distance calculations and a 2- to 60-fold speedup over the traditional k-NN algorithm for 16 datasets. Furthermore, kMkNN performs significant better than a kd-tree based k-NN algorithm for all datasets and performs better than a ball-tree based k-NN algorithm for most datasets. The results show that kMkNN is effective for searching nearest neighbors in high dimensional spaces.

Query Language for Location-Based Services: A Model Checking Approach

NASA Astrophysics Data System (ADS)

Hoareau, Christian; Satoh, Ichiro

We present a model checking approach to the rationale, implementation, and applications of a query language for location-based services. Such query mechanisms are necessary so that users, objects, and/or services can effectively benefit from the location-awareness of their surrounding environment. The underlying data model is founded on a symbolic model of space organized in a tree structure. Once extended to a semantic model for modal logic, we regard location query processing as a model checking problem, and thus define location queries as hybrid logicbased formulas. Our approach is unique to existing research because it explores the connection between location models and query processing in ubiquitous computing systems, relies on a sound theoretical basis, and provides modal logic-based query mechanisms for expressive searches over a decentralized data structure. A prototype implementation is also presented and will be discussed.

Automation and integration of components for generalized semantic markup of electronic medical texts.

PubMed

Dugan, J M; Berrios, D C; Liu, X; Kim, D K; Kaizer, H; Fagan, L M

1999-01-01

Our group has built an information retrieval system based on a complex semantic markup of medical textbooks. We describe the construction of a set of web-based knowledge-acquisition tools that expedites the collection and maintenance of the concepts required for text markup and the search interface required for information retrieval from the marked text. In the text markup system, domain experts (DEs) identify sections of text that contain one or more elements from a finite set of concepts. End users can then query the text using a predefined set of questions, each of which identifies a subset of complementary concepts. The search process matches that subset of concepts to relevant points in the text. The current process requires that the DE invest significant time to generate the required concepts and questions. We propose a new system--called ACQUIRE (Acquisition of Concepts and Queries in an Integrated Retrieval Environment)--that assists a DE in two essential tasks in the text-markup process. First, it helps her to develop, edit, and maintain the concept model: the set of concepts with which she marks the text. Second, ACQUIRE helps her to develop a query model: the set of specific questions that end users can later use to search the marked text. The DE incorporates concepts from the concept model when she creates the questions in the query model. The major benefit of the ACQUIRE system is a reduction in the time and effort required for the text-markup process. We compared the process of concept- and query-model creation using ACQUIRE to the process used in previous work by rebuilding two existing models that we previously constructed manually. We observed a significant decrease in the time required to build and maintain the concept and query models.

Analysis of PubMed User Sessions Using a Full-Day PubMed Query Log: A Comparison of Experienced and Nonexperienced PubMed Users

PubMed Central

2015-01-01

Background PubMed is the largest biomedical bibliographic information source on the Internet. PubMed has been considered one of the most important and reliable sources of up-to-date health care evidence. Previous studies examined the effects of domain expertise/knowledge on search performance using PubMed. However, very little is known about PubMed users’ knowledge of information retrieval (IR) functions and their usage in query formulation. Objective The purpose of this study was to shed light on how experienced/nonexperienced PubMed users perform their search queries by analyzing a full-day query log. Our hypotheses were that (1) experienced PubMed users who use system functions quickly retrieve relevant documents and (2) nonexperienced PubMed users who do not use them have longer search sessions than experienced users. Methods To test these hypotheses, we analyzed PubMed query log data containing nearly 3 million queries. User sessions were divided into two categories: experienced and nonexperienced. We compared experienced and nonexperienced users per number of sessions, and experienced and nonexperienced user sessions per session length, with a focus on how fast they completed their sessions. Results To test our hypotheses, we measured how successful information retrieval was (at retrieving relevant documents), represented as the decrease rates of experienced and nonexperienced users from a session length of 1 to 2, 3, 4, and 5. The decrease rate (from a session length of 1 to 2) of the experienced users was significantly larger than that of the nonexperienced groups. Conclusions Experienced PubMed users retrieve relevant documents more quickly than nonexperienced PubMed users in terms of session length. PMID:26139516

Google Search Queries About Neurosurgical Topics: Are They a Suitable Guide for Neurosurgeons?

PubMed

Lawson McLean, Anna C; Lawson McLean, Aaron; Kalff, Rolf; Walter, Jan

2016-06-01

Google is the most popular search engine, with about 100 billion searches per month. Google Trends is an integrated tool that allows users to obtain Google's search popularity statistics from the last decade. Our aim was to evaluate whether Google Trends is a useful tool to assess the public's interest in specific neurosurgical topics. We evaluated Google Trends statistics for the neurosurgical search topic areas "hydrocephalus," "spinal stenosis," "concussion," "vestibular schwannoma," and "cerebral arteriovenous malformation." We compared these with bibliometric data from PubMed and epidemiologic data from the German Federal Monitoring Agency. In addition, we assessed Google users' search behavior for the search terms "glioblastoma" and "meningioma." Over the last 10 years, there has been an increasing interest in the topic "concussion" from Internet users in general and scientists. "Spinal stenosis," "concussion," and "vestibular schwannoma" are topics that are of special interest in high-income countries (eg, Germany), whereas "hydrocephalus" is a popular topic in low- and middle-income countries. The Google-defined top searches within these topic areas revealed more detail about people's interests (eg, "normal pressure hydrocephalus" or "football concussion" ranked among the most popular search queries within the corresponding topics). There was a similar volume of queries for "glioblastoma" and "meningioma." Google Trends is a useful source to elicit information about general trends in peoples' health interests and the role of different diseases across the world. The Internet presence of neurosurgical units and surgeons can be guided by online users' interests to achieve high-quality, professional-endorsed patient education. Copyright © 2016 Elsevier Inc. All rights reserved.

What, where and when? Using Google Trends and Google to investigate patient needs and inform pharmacy practice.

PubMed

Hanna, Alan; Hanna, Lezley-Anne

2018-03-30

The aim was to provide a comprehensive overview (using pertinent examples) of the various ways that Google Trends and Google data could inform pharmacy practice. The objectives were to: examine what type of information people search for in relation to a common class of medicines; ascertain where people are directed to (websites) following an initial search for a medicine or medical condition; and establish information about when they search. The methodology differed depending on whether Google Trends or Google was being interrogated, but the search domain was always limited to the United Kingdom. Google Trends was queried, typically for a 5-year time frame, and data downloaded for many search inputs relating to medical conditions (self-treatable and non-self-treatable) and medicines (bought over-the-counter and prescribed). Google was queried and data collected for searches related to 'antibiotics'. Google Trends revealed a previously unknown seasonality pattern for irritable bowel syndrome. Related searches for 'antibiotics' revealed a high level of interest in the appropriateness of concomitant alcohol consumption and queries about what antibiotics are. Largely, people were being directed to reputable websites following their initial search input about a prescription-only medicine. However, searches for over-the-counter medicines were more likely to lead to commercial domains. This is one of the first studies to investigate use of Google Trends and Google in a pharmacy-specific context. It is relevant for practice as it could inform marketing strategies, public health policy and help tailor patient advice and counselling. © 2018 Royal Pharmaceutical Society.

A novel methodology for querying web images

NASA Astrophysics Data System (ADS)

Prabhakara, Rashmi; Lee, Ching Cheng

2005-01-01

Ever since the advent of Internet, there has been an immense growth in the amount of image data that is available on the World Wide Web. With such a magnitude of image availability, an efficient and effective image retrieval system is required to make use of this information. This research presents an effective image matching and indexing technique that improvises on existing integrated image retrieval methods. The proposed technique follows a two-phase approach, integrating query by topic and query by example specification methods. The first phase consists of topic-based image retrieval using an improved text information retrieval (IR) technique that makes use of the structured format of HTML documents. It consists of a focused crawler that not only provides for the user to enter the keyword for the topic-based search but also, the scope in which the user wants to find the images. The second phase uses the query by example specification to perform a low-level content-based image match for the retrieval of smaller and relatively closer results of the example image. Information related to the image feature is automatically extracted from the query image by the image processing system. A technique that is not computationally intensive based on color feature is used to perform content-based matching of images. The main goal is to develop a functional image search and indexing system and to demonstrate that better retrieval results can be achieved with this proposed hybrid search technique.

A novel methodology for querying web images

NASA Astrophysics Data System (ADS)

Prabhakara, Rashmi; Lee, Ching Cheng

2004-12-01

Ever since the advent of Internet, there has been an immense growth in the amount of image data that is available on the World Wide Web. With such a magnitude of image availability, an efficient and effective image retrieval system is required to make use of this information. This research presents an effective image matching and indexing technique that improvises on existing integrated image retrieval methods. The proposed technique follows a two-phase approach, integrating query by topic and query by example specification methods. The first phase consists of topic-based image retrieval using an improved text information retrieval (IR) technique that makes use of the structured format of HTML documents. It consists of a focused crawler that not only provides for the user to enter the keyword for the topic-based search but also, the scope in which the user wants to find the images. The second phase uses the query by example specification to perform a low-level content-based image match for the retrieval of smaller and relatively closer results of the example image. Information related to the image feature is automatically extracted from the query image by the image processing system. A technique that is not computationally intensive based on color feature is used to perform content-based matching of images. The main goal is to develop a functional image search and indexing system and to demonstrate that better retrieval results can be achieved with this proposed hybrid search technique.

A stochastic evolutionary model generating a mixture of exponential distributions

NASA Astrophysics Data System (ADS)

Fenner, Trevor; Levene, Mark; Loizou, George

2016-02-01

Recent interest in human dynamics has stimulated the investigation of the stochastic processes that explain human behaviour in various contexts, such as mobile phone networks and social media. In this paper, we extend the stochastic urn-based model proposed in [T. Fenner, M. Levene, G. Loizou, J. Stat. Mech. 2015, P08015 (2015)] so that it can generate mixture models, in particular, a mixture of exponential distributions. The model is designed to capture the dynamics of survival analysis, traditionally employed in clinical trials, reliability analysis in engineering, and more recently in the analysis of large data sets recording human dynamics. The mixture modelling approach, which is relatively simple and well understood, is very effective in capturing heterogeneity in data. We provide empirical evidence for the validity of the model, using a data set of popular search engine queries collected over a period of 114 months. We show that the survival function of these queries is closely matched by the exponential mixture solution for our model.

On search guide phrase compilation for recommending home medical products.

PubMed

Luo, Gang

2010-01-01

To help people find desired home medical products (HMPs), we developed an intelligent personal health record (iPHR) system that can automatically recommend HMPs based on users' health issues. Using nursing knowledge, we pre-compile a set of "search guide" phrases that provides semantic translation from words describing health issues to their underlying medical meanings. Then iPHR automatically generates queries from those phrases and uses them and a search engine to retrieve HMPs. To avoid missing relevant HMPs during retrieval, the compiled search guide phrases need to be comprehensive. Such compilation is a challenging task because nursing knowledge updates frequently and contains numerous details scattered in many sources. This paper presents a semi-automatic tool facilitating such compilation. Our idea is to formulate the phrase compilation task as a multi-label classification problem. For each newly obtained search guide phrase, we first use nursing knowledge and information retrieval techniques to identify a small set of potentially relevant classes with corresponding hints. Then a nurse makes the final decision on assigning this phrase to proper classes based on those hints. We demonstrate the effectiveness of our techniques by compiling search guide phrases from an occupational therapy textbook.

Adverse-drug-event data provided by pharmaceutical companies.

PubMed

Cudny, Magdalena E; Graham, Angie S

2008-06-01

Pharmaceutical company drug information center (PCDIC) responses to queries about adverse drug events (ADEs) were studied to determine whether PCDICs search sources other than the prescribing information on the package insert (PI) and whether the PCDICs' approach differs according to whether an ADE is listed in the PI (labeled) or not (unlabeled). Companies were selected from a list of PCDICs in the Physicians' Desk Reference. One oral or injectable prescription drug from each company was selected. For each drug, a labeled ADE and an unlabeled ADE about which to query the PCDICs were randomly selected from the index of an annual publication on ADEs. The investigators telephoned the PCDICs with an open-ended inquiry about the incidence, timing, and management of the ADE as reported in the literature and the company's internal data; they clarified that the request did not concern a specific patient. Whether or not information was provided, the source searched was recorded (PI, literature, internal database), and the percentages of PCDICs that used each source for labeled and for unlabeled ADEs were analyzed. Results were obtained from 100 companies to questions about 100 drugs (200 ADEs). For ADEs overall, 80% used the PI, 50% the medical literature, and 38% internal data. For labeled versus unlabeled ADEs, respectively, the PI was used by 84% and 76%; literature, both 50%; and internal data, 35% and 41%. The PCDIC specialists referencing the PI did not always provide accurate or up-to-date information. Some specialists, when asked to query internal databases, said that was not an option. For both labeled and unlabeled ADEs, the PI was the primary source used by PCDICs to answer safety questions about their products, and internal data were the least-used source. Most resources used by PCDICs are readily available to practicing pharmacists.

Digital Detection for Tobacco Control: Online Reactions to the 2009 U.S. Cigarette Excise Tax Increase

PubMed Central

2014-01-01

Introduction: The Internet is revolutionizing tobacco control, but few have harnessed the Web for surveillance. We demonstrate for the first time an approach for analyzing aggregate Internet search queries that captures precise changes in population considerations about tobacco. Methods: We compared tobacco-related Google queries originating in the United States during the week of the State Children’s Health Insurance Program (SCHIP) 2009 cigarette excise tax increase with a historic baseline. Specific queries were then ranked according to their relative increases while also considering approximations of changes in absolute search volume. Results: Individual queries with the largest relative increases the week of the SCHIP tax were “cigarettes Indian reservations” 640% (95% CI, 472–918), “free cigarettes online” 557% (95% CI, 432–756), and “Indian reservations cigarettes” 542% (95% CI, 414–733), amounting to about 7,500 excess searches. By themes, the largest relative increases were tribal cigarettes 246% (95% CI, 228–265), “free” cigarettes 215% (95% CI, 191–242), and cigarette stores 176% (95% CI, 160–193), accounting for 21,000, 27,000, and 90,000 excess queries. All avoidance queries, including those aforementioned themes, relatively increased 150% (95% CI, 144–155) or 550,000 from their baseline. All cessation queries increased 46% (95% CI, 44–48), or 175,000, around SCHIP; including themes for “cold turkey” 19% (95% CI, 11–27) or 2,600, cessation products 47% (95% CI, 44–50) or 78,000, and dubious cessation approaches (e.g., hypnosis) 40% (95% CI, 33–47) or 2,300. Conclusions: The SCHIP tax motivated specific changes in population considerations. Our strategy can support evaluations that temporally link tobacco control measures with instantaneous population reactions, as well as serve as a springboard for traditional studies, for example, including survey questionnaire design. PMID:24323570

STARS 2.0: 2nd-generation open-source archiving and query software

NASA Astrophysics Data System (ADS)

Winegar, Tom

2008-07-01

The Subaru Telescope is in process of developing an open-source alternative to the 1st-generation software and databases (STARS 1) used for archiving and query. For STARS 2, we have chosen PHP and Python for scripting and MySQL as the database software. We have collected feedback from staff and observers, and used this feedback to significantly improve the design and functionality of our future archiving and query software. Archiving - We identified two weaknesses in 1st-generation STARS archiving software: a complex and inflexible table structure and uncoordinated system administration for our business model: taking pictures from the summit and archiving them in both Hawaii and Japan. We adopted a simplified and normalized table structure with passive keyword collection, and we are designing an archive-to-archive file transfer system that automatically reports real-time status and error conditions and permits error recovery. Query - We identified several weaknesses in 1st-generation STARS query software: inflexible query tools, poor sharing of calibration data, and no automatic file transfer mechanisms to observers. We are developing improved query tools and sharing of calibration data, and multi-protocol unassisted file transfer mechanisms for observers. In the process, we have redefined a 'query': from an invisible search result that can only transfer once in-house right now, with little status and error reporting and no error recovery - to a stored search result that can be monitored, transferred to different locations with multiple protocols, reporting status and error conditions and permitting recovery from errors.

MetaSEEk: a content-based metasearch engine for images

NASA Astrophysics Data System (ADS)

Beigi, Mandis; Benitez, Ana B.; Chang, Shih-Fu

1997-12-01

Search engines are the most powerful resources for finding information on the rapidly expanding World Wide Web (WWW). Finding the desired search engines and learning how to use them, however, can be very time consuming. The integration of such search tools enables the users to access information across the world in a transparent and efficient manner. These systems are called meta-search engines. The recent emergence of visual information retrieval (VIR) search engines on the web is leading to the same efficiency problem. This paper describes and evaluates MetaSEEk, a content-based meta-search engine used for finding images on the Web based on their visual information. MetaSEEk is designed to intelligently select and interface with multiple on-line image search engines by ranking their performance for different classes of user queries. User feedback is also integrated in the ranking refinement. We compare MetaSEEk with a base line version of meta-search engine, which does not use the past performance of the different search engines in recommending target search engines for future queries.

Infodemiology of status epilepticus: A systematic validation of the Google Trends-based search queries.

PubMed

Bragazzi, Nicola Luigi; Bacigaluppi, Susanna; Robba, Chiara; Nardone, Raffaele; Trinka, Eugen; Brigo, Francesco

2016-02-01

People increasingly use Google looking for health-related information. We previously demonstrated that in English-speaking countries most people use this search engine to obtain information on status epilepticus (SE) definition, types/subtypes, and treatment. Now, we aimed at providing a quantitative analysis of SE-related web queries. This analysis represents an advancement, with respect to what was already previously discussed, in that the Google Trends (GT) algorithm has been further refined and correlational analyses have been carried out to validate the GT-based query volumes. Google Trends-based SE-related query volumes were well correlated with information concerning causes and pharmacological and nonpharmacological treatments. Google Trends can provide both researchers and clinicians with data on realities and contexts that are generally overlooked and underexplored by classic epidemiology. In this way, GT can foster new epidemiological studies in the field and can complement traditional epidemiological tools. Copyright © 2015 Elsevier Inc. All rights reserved.

GenoQuery: a new querying module for functional annotation in a genomic warehouse

PubMed Central

Lemoine, Frédéric; Labedan, Bernard; Froidevaux, Christine

2008-01-01

Motivation: We have to cope with both a deluge of new genome sequences and a huge amount of data produced by high-throughput approaches used to exploit these genomic features. Crossing and comparing such heterogeneous and disparate data will help improving functional annotation of genomes. This requires designing elaborate integration systems such as warehouses for storing and querying these data. Results: We have designed a relational genomic warehouse with an original multi-layer architecture made of a databases layer and an entities layer. We describe a new querying module, GenoQuery, which is based on this architecture. We use the entities layer to define mixed queries. These mixed queries allow searching for instances of biological entities and their properties in the different databases, without specifying in which database they should be found. Accordingly, we further introduce the central notion of alternative queries. Such queries have the same meaning as the original mixed queries, while exploiting complementarities yielded by the various integrated databases of the warehouse. We explain how GenoQuery computes all the alternative queries of a given mixed query. We illustrate how useful this querying module is by means of a thorough example. Availability: http://www.lri.fr/~lemoine/GenoQuery/ Contact: chris@lri.fr, lemoine@lri.fr PMID:18586731

BioTCM-SE: a semantic search engine for the information retrieval of modern biology and traditional Chinese medicine.

PubMed

Chen, Xi; Chen, Huajun; Bi, Xuan; Gu, Peiqin; Chen, Jiaoyan; Wu, Zhaohui

2014-01-01

Understanding the functional mechanisms of the complex biological system as a whole is drawing more and more attention in global health care management. Traditional Chinese Medicine (TCM), essentially different from Western Medicine (WM), is gaining increasing attention due to its emphasis on individual wellness and natural herbal medicine, which satisfies the goal of integrative medicine. However, with the explosive growth of biomedical data on the Web, biomedical researchers are now confronted with the problem of large-scale data analysis and data query. Besides that, biomedical data also has a wide coverage which usually comes from multiple heterogeneous data sources and has different taxonomies, making it hard to integrate and query the big biomedical data. Embedded with domain knowledge from different disciplines all regarding human biological systems, the heterogeneous data repositories are implicitly connected by human expert knowledge. Traditional search engines cannot provide accurate and comprehensive search results for the semantically associated knowledge since they only support keywords-based searches. In this paper, we present BioTCM-SE, a semantic search engine for the information retrieval of modern biology and TCM, which provides biologists with a comprehensive and accurate associated knowledge query platform to greatly facilitate the implicit knowledge discovery between WM and TCM.

BioTCM-SE: A Semantic Search Engine for the Information Retrieval of Modern Biology and Traditional Chinese Medicine

PubMed Central

Chen, Xi; Chen, Huajun; Bi, Xuan; Gu, Peiqin; Chen, Jiaoyan; Wu, Zhaohui

2014-01-01

Understanding the functional mechanisms of the complex biological system as a whole is drawing more and more attention in global health care management. Traditional Chinese Medicine (TCM), essentially different from Western Medicine (WM), is gaining increasing attention due to its emphasis on individual wellness and natural herbal medicine, which satisfies the goal of integrative medicine. However, with the explosive growth of biomedical data on the Web, biomedical researchers are now confronted with the problem of large-scale data analysis and data query. Besides that, biomedical data also has a wide coverage which usually comes from multiple heterogeneous data sources and has different taxonomies, making it hard to integrate and query the big biomedical data. Embedded with domain knowledge from different disciplines all regarding human biological systems, the heterogeneous data repositories are implicitly connected by human expert knowledge. Traditional search engines cannot provide accurate and comprehensive search results for the semantically associated knowledge since they only support keywords-based searches. In this paper, we present BioTCM-SE, a semantic search engine for the information retrieval of modern biology and TCM, which provides biologists with a comprehensive and accurate associated knowledge query platform to greatly facilitate the implicit knowledge discovery between WM and TCM. PMID:24772189

Method for indexing and retrieving manufacturing-specific digital imagery based on image content

DOEpatents

Ferrell, Regina K.; Karnowski, Thomas P.; Tobin, Jr., Kenneth W.

2004-06-15

A method for indexing and retrieving manufacturing-specific digital images based on image content comprises three steps. First, at least one feature vector can be extracted from a manufacturing-specific digital image stored in an image database. In particular, each extracted feature vector corresponds to a particular characteristic of the manufacturing-specific digital image, for instance, a digital image modality and overall characteristic, a substrate/background characteristic, and an anomaly/defect characteristic. Notably, the extracting step includes generating a defect mask using a detection process. Second, using an unsupervised clustering method, each extracted feature vector can be indexed in a hierarchical search tree. Third, a manufacturing-specific digital image associated with a feature vector stored in the hierarchicial search tree can be retrieved, wherein the manufacturing-specific digital image has image content comparably related to the image content of the query image. More particularly, can include two data reductions, the first performed based upon a query vector extracted from a query image. Subsequently, a user can select relevant images resulting from the first data reduction. From the selection, a prototype vector can be calculated, from which a second-level data reduction can be performed. The second-level data reduction can result in a subset of feature vectors comparable to the prototype vector, and further comparable to the query vector. An additional fourth step can include managing the hierarchical search tree by substituting a vector average for several redundant feature vectors encapsulated by nodes in the hierarchical search tree.

Cognitive issues in searching images with visual queries

NASA Astrophysics Data System (ADS)

Yu, ByungGu; Evens, Martha W.

1999-01-01

In this paper, we propose our image indexing technique and visual query processing technique. Our mental images are different from the actual retinal images and many things, such as personal interests, personal experiences, perceptual context, the characteristics of spatial objects, and so on, affect our spatial perception. These private differences are propagated into our mental images and so our visual queries become different from the real images that we want to find. This is a hard problem and few people have tried to work on it. In this paper, we survey the human mental imagery system, the human spatial perception, and discuss several kinds of visual queries. Also, we propose our own approach to visual query interpretation and processing.

A Scalable Data Access Layer to Manage Structured Heterogeneous Biomedical Data.

PubMed

Delussu, Giovanni; Lianas, Luca; Frexia, Francesca; Zanetti, Gianluigi

2016-01-01

This work presents a scalable data access layer, called PyEHR, designed to support the implementation of data management systems for secondary use of structured heterogeneous biomedical and clinical data. PyEHR adopts the openEHR's formalisms to guarantee the decoupling of data descriptions from implementation details and exploits structure indexing to accelerate searches. Data persistence is guaranteed by a driver layer with a common driver interface. Interfaces for two NoSQL Database Management Systems are already implemented: MongoDB and Elasticsearch. We evaluated the scalability of PyEHR experimentally through two types of tests, called "Constant Load" and "Constant Number of Records", with queries of increasing complexity on synthetic datasets of ten million records each, containing very complex openEHR archetype structures, distributed on up to ten computing nodes.

What Can Pictures Tell Us About Web Pages? Improving Document Search Using Images.

PubMed

Rodriguez-Vaamonde, Sergio; Torresani, Lorenzo; Fitzgibbon, Andrew W

2015-06-01

Traditional Web search engines do not use the images in the HTML pages to find relevant documents for a given query. Instead, they typically operate by computing a measure of agreement between the keywords provided by the user and only the text portion of each page. In this paper we study whether the content of the pictures appearing in a Web page can be used to enrich the semantic description of an HTML document and consequently boost the performance of a keyword-based search engine. We present a Web-scalable system that exploits a pure text-based search engine to find an initial set of candidate documents for a given query. Then, the candidate set is reranked using visual information extracted from the images contained in the pages. The resulting system retains the computational efficiency of traditional text-based search engines with only a small additional storage cost needed to encode the visual information. We test our approach on one of the TREC Million Query Track benchmarks where we show that the exploitation of visual content yields improvement in accuracies for two distinct text-based search engines, including the system with the best reported performance on this benchmark. We further validate our approach by collecting document relevance judgements on our search results using Amazon Mechanical Turk. The results of this experiment confirm the improvement in accuracy produced by our image-based reranker over a pure text-based system.

Tracking the rise in popularity of electronic nicotine delivery systems (electronic cigarettes) using search query surveillance.

PubMed

Ayers, John W; Ribisl, Kurt M; Brownstein, John S

2011-04-01

Public interest in electronic nicotine delivery systems (ENDS) is undocumented. By monitoring search queries, ENDS popularity and correlates of their popularity were assessed in Australia, Canada, the United Kingdom (UK), and the U.S. English-language Google searches conducted from January 2008 through September 2010 were compared to snus, nicotine replacement therapy (NRT), and Chantix® or Champix®. Searches for each week were scaled to the highest weekly search proportion (100), with lower values indicating the relative search proportion compared to the highest-proportion week (e.g., 50=50% of the highest observed proportion). Analyses were performed in 2010. From July 2008 through February 2010, ENDS searches increased in all nations studied except Australia, there an increase occurred more recently. By September 2010, ENDS searches were several-hundred-fold greater than searches for smoking alternatives in the UK and U.S., and were rivaling alternatives in Australia and Canada. Across nations, ENDS searches were highest in the U.S., followed by similar search intensity in Canada and the UK, with Australia having the fewest ENDS searches. Stronger tobacco control, created by clean indoor air laws, cigarette taxes, and anti-smoking populations, were associated with consistently higher levels of ENDS searches. The online popularity of ENDS has surpassed that of snus and NRTs, which have been on the market for far longer, and is quickly outpacing Chantix or Champix. In part, the association between ENDS's popularity and stronger tobacco control suggests ENDS are used to bypass, or quit in response to, smoking restrictions. Search query surveillance is a valuable, real-time, free, and public method to evaluate the diffusion of new health products. This method may be generalized to other behavioral, biological, informational, or psychological outcomes manifested on search engines. Copyright © 2011 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Association between Search Behaviors and Disease Prevalence Rates at 18 U.S. Children's Hospitals.

PubMed

Daniel, Dennis; Wolbrink, Traci; Logvinenko, Tanya; Harper, Marvin; Burns, Jeffrey

2017-10-01

Background Usage of online resources by clinicians in training and practice can provide insight into knowledge gaps and inform development of decision support tools. Although online information seeking is often driven by encountered patient problems, the relationship between disease prevalence and search rate has not been previously characterized. Objective This article aimed to (1) identify topics frequently searched by pediatric clinicians using UpToDate (http://www.uptodate.com) and (2) explore the association between disease prevalence rate and search rate using data from the Pediatric Health Information System. Methods We identified the most common search queries and resources most frequently accessed on UpToDate for a cohort of 18 children's hospitals during calendar year 2012. We selected 64 of the most frequently searched diseases and matched ICD-9 data from the PHIS database during the same time period. Using linear regression, we explored the relationship between clinician query rate and disease prevalence rate. Results The hospital cohort submitted 1,228,138 search queries across 592,454 sessions. The majority of search sessions focused on a single search topic. We identified no consistent overall association between disease prevalence and search rates. Diseases where search rate was substantially higher than prevalence rate were often infectious or immune/rheumatologic conditions, involved potentially complex diagnosis or management, and carried risk of significant morbidity or mortality. None of the examined diseases showed a decrease in search rate associated with increased disease prevalence rates. Conclusion This is one of the first medical learning needs assessments to use large-scale, multisite data to identify topics of interest to pediatric clinicians, and to examine the relationship between disease prevalence and search rate for a set of pediatric diseases. Overall, disease search rate did not appear to be associated with hospital disease prevalence rates based on ICD-9 codes. However, some diseases were consistently searched at a higher rate than their prevalence rate; many of these diseases shared common features.

Advanced SPARQL querying in small molecule databases.

PubMed

Galgonek, Jakub; Hurt, Tomáš; Michlíková, Vendula; Onderka, Petr; Schwarz, Jan; Vondrášek, Jiří

2016-01-01

In recent years, the Resource Description Framework (RDF) and the SPARQL query language have become more widely used in the area of cheminformatics and bioinformatics databases. These technologies allow better interoperability of various data sources and powerful searching facilities. However, we identified several deficiencies that make usage of such RDF databases restrictive or challenging for common users. We extended a SPARQL engine to be able to use special procedures inside SPARQL queries. This allows the user to work with data that cannot be simply precomputed and thus cannot be directly stored in the database. We designed an algorithm that checks a query against data ontology to identify possible user errors. This greatly improves query debugging. We also introduced an approach to visualize retrieved data in a user-friendly way, based on templates describing visualizations of resource classes. To integrate all of our approaches, we developed a simple web application. Our system was implemented successfully, and we demonstrated its usability on the ChEBI database transformed into RDF form. To demonstrate procedure call functions, we employed compound similarity searching based on OrChem. The application is publicly available at https://bioinfo.uochb.cas.cz/projects/chemRDF.

Microscopy as a statistical, Rényi-Ulam, half-lie game: a new heuristic search strategy to accelerate imaging.

PubMed

Drumm, Daniel W; Greentree, Andrew D

2017-11-07

Finding a fluorescent target in a biological environment is a common and pressing microscopy problem. This task is formally analogous to the canonical search problem. In ideal (noise-free, truthful) search problems, the well-known binary search is optimal. The case of half-lies, where one of two responses to a search query may be deceptive, introduces a richer, Rényi-Ulam problem and is particularly relevant to practical microscopy. We analyse microscopy in the contexts of Rényi-Ulam games and half-lies, developing a new family of heuristics. We show the cost of insisting on verification by positive result in search algorithms; for the zero-half-lie case bisectioning with verification incurs a 50% penalty in the average number of queries required. The optimal partitioning of search spaces directly following verification in the presence of random half-lies is determined. Trisectioning with verification is shown to be the most efficient heuristic of the family in a majority of cases.

Omicseq: a web-based search engine for exploring omics datasets

PubMed Central

Sun, Xiaobo; Pittard, William S.; Xu, Tianlei; Chen, Li; Zwick, Michael E.; Jiang, Xiaoqian; Wang, Fusheng

2017-01-01

Abstract The development and application of high-throughput genomics technologies has resulted in massive quantities of diverse omics data that continue to accumulate rapidly. These rich datasets offer unprecedented and exciting opportunities to address long standing questions in biomedical research. However, our ability to explore and query the content of diverse omics data is very limited. Existing dataset search tools rely almost exclusively on the metadata. A text-based query for gene name(s) does not work well on datasets wherein the vast majority of their content is numeric. To overcome this barrier, we have developed Omicseq, a novel web-based platform that facilitates the easy interrogation of omics datasets holistically to improve ‘findability’ of relevant data. The core component of Omicseq is trackRank, a novel algorithm for ranking omics datasets that fully uses the numerical content of the dataset to determine relevance to the query entity. The Omicseq system is supported by a scalable and elastic, NoSQL database that hosts a large collection of processed omics datasets. In the front end, a simple, web-based interface allows users to enter queries and instantly receive search results as a list of ranked datasets deemed to be the most relevant. Omicseq is freely available at http://www.omicseq.org. PMID:28402462

Analyzing Document Retrievability in Patent Retrieval Settings

NASA Astrophysics Data System (ADS)

Bashir, Shariq; Rauber, Andreas

Most information retrieval settings, such as web search, are typically precision-oriented, i.e. they focus on retrieving a small number of highly relevant documents. However, in specific domains, such as patent retrieval or law, recall becomes more relevant than precision: in these cases the goal is to find all relevant documents, requiring algorithms to be tuned more towards recall at the cost of precision. This raises important questions with respect to retrievability and search engine bias: depending on how the similarity between a query and documents is measured, certain documents may be more or less retrievable in certain systems, up to some documents not being retrievable at all within common threshold settings. Biases may be oriented towards popularity of documents (increasing weight of references), towards length of documents, favour the use of rare or common words; rely on structural information such as metadata or headings, etc. Existing accessibility measurement techniques are limited as they measure retrievability with respect to all possible queries. In this paper, we improve accessibility measurement by considering sets of relevant and irrelevant queries for each document. This simulates how recall oriented users create their queries when searching for relevant information. We evaluate retrievability scores using a corpus of patents from US Patent and Trademark Office.

Research on multi-user encrypted search scheme in cloud environment

NASA Astrophysics Data System (ADS)

Yu, Zonghua; Lin, Sui

2017-05-01

Aiming at the existing problems of multi-user encrypted search scheme in cloud computing environment, a basic multi-user encrypted scheme is proposed firstly, and then the basic scheme is extended to an anonymous hierarchical management authority. Compared with most of the existing schemes, the scheme not only to achieve the protection of keyword information, but also to achieve the protection of user identity privacy; the same time, data owners can directly control the user query permissions, rather than the cloud server. In addition, through the use of a special query key generation rules, to achieve the hierarchical management of the user's query permissions. The safety analysis shows that the scheme is safe and that the performance analysis and experimental data show that the scheme is practicable.

Associative memory model for searching an image database by image snippet

NASA Astrophysics Data System (ADS)

Khan, Javed I.; Yun, David Y.

1994-09-01

This paper presents an associative memory called an multidimensional holographic associative computing (MHAC), which can be potentially used to perform feature based image database query using image snippet. MHAC has the unique capability to selectively focus on specific segments of a query frame during associative retrieval. As a result, this model can perform search on the basis of featural significance described by a subset of the snippet pixels. This capability is critical for visual query in image database because quite often the cognitive index features in the snippet are statistically weak. Unlike, the conventional artificial associative memories, MHAC uses a two level representation and incorporates additional meta-knowledge about the reliability status of segments of information it receives and forwards. In this paper we present the analysis of focus characteristics of MHAC.

View-Based Searching Systems--Progress Towards Effective Disintermediation.

ERIC Educational Resources Information Center

Pollitt, A. Steven; Smith, Martin P.; Treglown, Mark; Braekevelt, Patrick

This paper presents the background and then reports progress made in the development of two view-based searching systems--HIBROWSE for EMBASE, searching Europe's most important biomedical bibliographic database, and HIBROWSE for EPOQUE, improving access to the European Parliament's Online Query System. The HIBROWSE approach to searching promises…

Modeling Group Interactions via Open Data Sources

DTIC Science & Technology

2011-08-30

data. The state-of-art search engines are designed to help general query-specific search and not suitable for finding disconnected online groups. The...groups, (2) developing innovative mathematical and statistical models and efficient algorithms that leverage existing search engines and employ

Embedding strategies for effective use of information from multiple sequence alignments.

PubMed Central

Henikoff, S.; Henikoff, J. G.

1997-01-01

We describe a new strategy for utilizing multiple sequence alignment information to detect distant relationships in searches of sequence databases. A single sequence representing a protein family is enriched by replacing conserved regions with position-specific scoring matrices (PSSMs) or consensus residues derived from multiple alignments of family members. In comprehensive tests of these and other family representations, PSSM-embedded queries produced the best results overall when used with a special version of the Smith-Waterman searching algorithm. Moreover, embedding consensus residues instead of PSSMs improved performance with readily available single sequence query searching programs, such as BLAST and FASTA. Embedding PSSMs or consensus residues into a representative sequence improves searching performance by extracting multiple alignment information from motif regions while retaining single sequence information where alignment is uncertain. PMID:9070452

An Extensible "SCHEMA-LESS" Database Framework for Managing High-Throughput Semi-Structured Documents

NASA Technical Reports Server (NTRS)

Maluf, David A.; Tran, Peter B.

2003-01-01

Object-Relational database management system is an integrated hybrid cooperative approach to combine the best practices of both the relational model utilizing SQL queries and the object-oriented, semantic paradigm for supporting complex data creation. In this paper, a highly scalable, information on demand database framework, called NETMARK, is introduced. NETMARK takes advantages of the Oracle 8i object-relational database using physical addresses data types for very efficient keyword search of records spanning across both context and content. NETMARK was originally developed in early 2000 as a research and development prototype to solve the vast amounts of unstructured and semistructured documents existing within NASA enterprises. Today, NETMARK is a flexible, high-throughput open database framework for managing, storing, and searching unstructured or semi-structured arbitrary hierarchal models, such as XML and HTML.

An Extensible Schema-less Database Framework for Managing High-throughput Semi-Structured Documents

NASA Technical Reports Server (NTRS)

Maluf, David A.; Tran, Peter B.; La, Tracy; Clancy, Daniel (Technical Monitor)

2002-01-01

Object-Relational database management system is an integrated hybrid cooperative approach to combine the best practices of both the relational model utilizing SQL queries and the object oriented, semantic paradigm for supporting complex data creation. In this paper, a highly scalable, information on demand database framework, called NETMARK is introduced. NETMARK takes advantages of the Oracle 8i object-relational database using physical addresses data types for very efficient keyword searches of records for both context and content. NETMARK was originally developed in early 2000 as a research and development prototype to solve the vast amounts of unstructured and semi-structured documents existing within NASA enterprises. Today, NETMARK is a flexible, high throughput open database framework for managing, storing, and searching unstructured or semi structured arbitrary hierarchal models, XML and HTML.

NETMARK: A Schema-less Extension for Relational Databases for Managing Semi-structured Data Dynamically

NASA Technical Reports Server (NTRS)

Maluf, David A.; Tran, Peter B.

2003-01-01

Object-Relational database management system is an integrated hybrid cooperative approach to combine the best practices of both the relational model utilizing SQL queries and the object-oriented, semantic paradigm for supporting complex data creation. In this paper, a highly scalable, information on demand database framework, called NETMARK, is introduced. NETMARK takes advantages of the Oracle 8i object-relational database using physical addresses data types for very efficient keyword search of records spanning across both context and content. NETMARK was originally developed in early 2000 as a research and development prototype to solve the vast amounts of unstructured and semi-structured documents existing within NASA enterprises. Today, NETMARK is a flexible, high-throughput open database framework for managing, storing, and searching unstructured or semi-structured arbitrary hierarchal models, such as XML and HTML.

A Modular Framework for Transforming Structured Data into HTML with Machine-Readable Annotations

NASA Astrophysics Data System (ADS)

Patton, E. W.; West, P.; Rozell, E.; Zheng, J.

2010-12-01

There is a plethora of web-based Content Management Systems (CMS) available for maintaining projects and data, i.a. However, each system varies in its capabilities and often content is stored separately and accessed via non-uniform web interfaces. Moving from one CMS to another (e.g., MediaWiki to Drupal) can be cumbersome, especially if a large quantity of data must be adapted to the new system. To standardize the creation, display, management, and sharing of project information, we have assembled a framework that uses existing web technologies to transform data provided by any service that supports the SPARQL Protocol and RDF Query Language (SPARQL) queries into HTML fragments, allowing it to be embedded in any existing website. The framework utilizes a two-tier XML Stylesheet Transformation (XSLT) that uses existing ontologies (e.g., Friend-of-a-Friend, Dublin Core) to interpret query results and render them as HTML documents. These ontologies can be used in conjunction with custom ontologies suited to individual needs (e.g., domain-specific ontologies for describing data records). Furthermore, this transformation process encodes machine-readable annotations, namely, the Resource Description Framework in attributes (RDFa), into the resulting HTML, so that capable parsers and search engines can extract the relationships between entities (e.g, people, organizations, datasets). To facilitate editing of content, the framework provides a web-based form system, mapping each query to a dynamically generated form that can be used to modify and create entities, while keeping the native data store up-to-date. This open framework makes it easy to duplicate data across many different sites, allowing researchers to distribute their data in many different online forums. In this presentation we will outline the structure of queries and the stylesheets used to transform them, followed by a brief walkthrough that follows the data from storage to human- and machine-accessible web page. We conclude with a discussion on content caching and steps toward performing queries across multiple domains.

Query-Based Outlier Detection in Heterogeneous Information Networks.

PubMed

Kuck, Jonathan; Zhuang, Honglei; Yan, Xifeng; Cam, Hasan; Han, Jiawei

2015-03-01

Outlier or anomaly detection in large data sets is a fundamental task in data science, with broad applications. However, in real data sets with high-dimensional space, most outliers are hidden in certain dimensional combinations and are relative to a user's search space and interest. It is often more effective to give power to users and allow them to specify outlier queries flexibly, and the system will then process such mining queries efficiently. In this study, we introduce the concept of query-based outlier in heterogeneous information networks, design a query language to facilitate users to specify such queries flexibly, define a good outlier measure in heterogeneous networks, and study how to process outlier queries efficiently in large data sets. Our experiments on real data sets show that following such a methodology, interesting outliers can be defined and uncovered flexibly and effectively in large heterogeneous networks.

Query-Based Outlier Detection in Heterogeneous Information Networks

PubMed Central

Kuck, Jonathan; Zhuang, Honglei; Yan, Xifeng; Cam, Hasan; Han, Jiawei

2015-01-01

Outlier or anomaly detection in large data sets is a fundamental task in data science, with broad applications. However, in real data sets with high-dimensional space, most outliers are hidden in certain dimensional combinations and are relative to a user’s search space and interest. It is often more effective to give power to users and allow them to specify outlier queries flexibly, and the system will then process such mining queries efficiently. In this study, we introduce the concept of query-based outlier in heterogeneous information networks, design a query language to facilitate users to specify such queries flexibly, define a good outlier measure in heterogeneous networks, and study how to process outlier queries efficiently in large data sets. Our experiments on real data sets show that following such a methodology, interesting outliers can be defined and uncovered flexibly and effectively in large heterogeneous networks. PMID:27064397

Google it: obtaining information about local STD/HIV testing services online.

PubMed

Habel, Melissa A; Hood, Julia; Desai, Sheila; Kachur, Rachel; Buhi, Eric R; Liddon, Nicole

2011-04-01

Although the Internet is one of the most commonly accessed resources for health information, finding information on local sexual health services, such as sexually transmitted disease (STD) testing, can be challenging. Recognizing that most quests for online health information begin with search engines, the purpose of this exploratory study was to examine the extent to which online information about local STD/HIV testing services can be found using Google. Queries on STD and HIV testing services were executed in Google for 6 geographically unique locations across the United States. The first 3 websites that resulted from each query were coded for the following characteristics: (1) relevancy to the search topic, (2) domain and purpose, (3) rank in Google results, and (4) content. Websites hosted at .com (57.3%), .org (25.7%), and .gov (10.5%) domains were retrieved most frequently. Roughly half of all websites (n = 376) provided information relevant to the query, and about three-quarters (77.0%) of all queries yielded at least 1 relevant website within the first 3 results. Searches for larger cities were more likely to yield relevant results compared with smaller cities (odds ratio [OR] = 10.0, 95% confidence interval [CI] = 5.6, 17.9). On comparison with .com domains, .gov (OR = 2.9, 95% CI = 1.4, 5.6) and .org domains (OR = 2.9, 95% CI = 1.7, 4.8) were more likely to provide information of the location to get tested. Ease of online access to information about sexual health services varies by search topic and locale. Sexual health service providers must optimize their website placement so as to reach a greater proportion of the sexually active population who use web search engines.

Analysis of Technique to Extract Data from the Web for Improved Performance

NASA Astrophysics Data System (ADS)

Gupta, Neena; Singh, Manish

2010-11-01

The World Wide Web rapidly guides the world into a newly amazing electronic world, where everyone can publish anything in electronic form and extract almost all the information. Extraction of information from semi structured or unstructured documents, such as web pages, is a useful yet complex task. Data extraction, which is important for many applications, extracts the records from the HTML files automatically. Ontologies can achieve a high degree of accuracy in data extraction. We analyze method for data extraction OBDE (Ontology-Based Data Extraction), which automatically extracts the query result records from the web with the help of agents. OBDE first constructs an ontology for a domain according to information matching between the query interfaces and query result pages from different web sites within the same domain. Then, the constructed domain ontology is used during data extraction to identify the query result section in a query result page and to align and label the data values in the extracted records. The ontology-assisted data extraction method is fully automatic and overcomes many of the deficiencies of current automatic data extraction methods.

The BioPrompt-box: an ontology-based clustering tool for searching in biological databases.

PubMed

Corsi, Claudio; Ferragina, Paolo; Marangoni, Roberto

2007-03-08

High-throughput molecular biology provides new data at an incredible rate, so that the increase in the size of biological databanks is enormous and very rapid. This scenario generates severe problems not only at indexing time, where suitable algorithmic techniques for data indexing and retrieval are required, but also at query time, since a user query may produce such a large set of results that their browsing and "understanding" becomes humanly impractical. This problem is well known to the Web community, where a new generation of Web search engines is being developed, like Vivisimo. These tools organize on-the-fly the results of a user query in a hierarchy of labeled folders that ease their browsing and knowledge extraction. We investigate this approach on biological data, and propose the so called The BioPrompt-boxsoftware system which deploys ontology-driven clustering strategies for making the searching process of biologists more efficient and effective. The BioPrompt-box (Bpb) defines a document as a biological sequence plus its associated meta-data taken from the underneath databank--like references to ontologies or to external databanks, and plain texts as comments of researchers and (title, abstracts or even body of) papers. Bpboffers several tools to customize the search and the clustering process over its indexed documents. The user can search a set of keywords within a specific field of the document schema, or can execute Blastto find documents relative to homologue sequences. In both cases the search task returns a set of documents (hits) which constitute the answer to the user query. Since the number of hits may be large, Bpbclusters them into groups of homogenous content, organized as a hierarchy of labeled clusters. The user can actually choose among several ontology-based hierarchical clustering strategies, each offering a different "view" of the returned hits. Bpbcomputes these views by exploiting the meta-data present within the retrieved documents such as the references to Gene Ontology, the taxonomy lineage, the organism and the keywords. Of course, the approach is flexible enough to leave room for future additions of other meta-information. The ultimate goal of the clustering process is to provide the user with several different readings of the (maybe numerous) query results and show possible hidden correlations among them, thus improving their browsing and understanding. Bpb is a powerful search engine that makes it very easy to perform complex queries over the indexed databanks (currently only UNIPROT is considered). The ontology-based clustering approach is efficient and effective, and could thus be applied successfully to larger databanks, like GenBank or EMBL.

The BioPrompt-box: an ontology-based clustering tool for searching in biological databases

PubMed Central

Corsi, Claudio; Ferragina, Paolo; Marangoni, Roberto

2007-01-01

Background High-throughput molecular biology provides new data at an incredible rate, so that the increase in the size of biological databanks is enormous and very rapid. This scenario generates severe problems not only at indexing time, where suitable algorithmic techniques for data indexing and retrieval are required, but also at query time, since a user query may produce such a large set of results that their browsing and "understanding" becomes humanly impractical. This problem is well known to the Web community, where a new generation of Web search engines is being developed, like Vivisimo. These tools organize on-the-fly the results of a user query in a hierarchy of labeled folders that ease their browsing and knowledge extraction. We investigate this approach on biological data, and propose the so called The BioPrompt-boxsoftware system which deploys ontology-driven clustering strategies for making the searching process of biologists more efficient and effective. Results The BioPrompt-box (Bpb) defines a document as a biological sequence plus its associated meta-data taken from the underneath databank – like references to ontologies or to external databanks, and plain texts as comments of researchers and (title, abstracts or even body of) papers. Bpboffers several tools to customize the search and the clustering process over its indexed documents. The user can search a set of keywords within a specific field of the document schema, or can execute Blastto find documents relative to homologue sequences. In both cases the search task returns a set of documents (hits) which constitute the answer to the user query. Since the number of hits may be large, Bpbclusters them into groups of homogenous content, organized as a hierarchy of labeled clusters. The user can actually choose among several ontology-based hierarchical clustering strategies, each offering a different "view" of the returned hits. Bpbcomputes these views by exploiting the meta-data present within the retrieved documents such as the references to Gene Ontology, the taxonomy lineage, the organism and the keywords. Of course, the approach is flexible enough to leave room for future additions of other meta-information. The ultimate goal of the clustering process is to provide the user with several different readings of the (maybe numerous) query results and show possible hidden correlations among them, thus improving their browsing and understanding. Conclusion Bpb is a powerful search engine that makes it very easy to perform complex queries over the indexed databanks (currently only UNIPROT is considered). The ontology-based clustering approach is efficient and effective, and could thus be applied successfully to larger databanks, like GenBank or EMBL. PMID:17430575

Preliminary Results on Uncertainty Quantification for Pattern Analytics

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

Stracuzzi, David John; Brost, Randolph; Chen, Maximillian Gene

2015-09-01

This report summarizes preliminary research into uncertainty quantification for pattern ana- lytics within the context of the Pattern Analytics to Support High-Performance Exploitation and Reasoning (PANTHER) project. The primary focus of PANTHER was to make large quantities of remote sensing data searchable by analysts. The work described in this re- port adds nuance to both the initial data preparation steps and the search process. Search queries are transformed from does the specified pattern exist in the data? to how certain is the system that the returned results match the query? We show example results for both data processing and search,more » and discuss a number of possible improvements for each.« less

How To Do Field Searching in Web Search Engines: A Field Trip.

ERIC Educational Resources Information Center

Hock, Ran

1998-01-01

Describes the field search capabilities of selected Web search engines (AltaVista, HotBot, Infoseek, Lycos, Yahoo!) and includes a chart outlining what fields (date, title, URL, images, audio, video, links, page depth) are searchable, where to go on the page to search them, the syntax required (if any), and how field search queries are entered.…

Adding Hierarchical Objects to Relational Database General-Purpose XML-Based Information Managements

NASA Technical Reports Server (NTRS)

Lin, Shu-Chun; Knight, Chris; La, Tracy; Maluf, David; Bell, David; Tran, Khai Peter; Gawdiak, Yuri

2006-01-01

NETMARK is a flexible, high-throughput software system for managing, storing, and rapid searching of unstructured and semi-structured documents. NETMARK transforms such documents from their original highly complex, constantly changing, heterogeneous data formats into well-structured, common data formats in using Hypertext Markup Language (HTML) and/or Extensible Markup Language (XML). The software implements an object-relational database system that combines the best practices of the relational model utilizing Structured Query Language (SQL) with those of the object-oriented, semantic database model for creating complex data. In particular, NETMARK takes advantage of the Oracle 8i object-relational database model using physical-address data types for very efficient keyword searches of records across both context and content. NETMARK also supports multiple international standards such as WEBDAV for drag-and-drop file management and SOAP for integrated information management using Web services. The document-organization and -searching capabilities afforded by NETMARK are likely to make this software attractive for use in disciplines as diverse as science, auditing, and law enforcement.

Measuring up: Implementing a dental quality measure in the electronic health record context.

PubMed

Bhardwaj, Aarti; Ramoni, Rachel; Kalenderian, Elsbeth; Neumann, Ana; Hebballi, Nutan B; White, Joel M; McClellan, Lyle; Walji, Muhammad F

2016-01-01

Quality improvement requires using quality measures that can be implemented in a valid manner. Using guidelines set forth by the Meaningful Use portion of the Health Information Technology for Economic and Clinical Health Act, the authors assessed the feasibility and performance of an automated electronic Meaningful Use dental clinical quality measure to determine the percentage of children who received fluoride varnish. The authors defined how to implement the automated measure queries in a dental electronic health record. Within records identified through automated query, the authors manually reviewed a subsample to assess the performance of the query. The automated query results revealed that 71.0% of patients had fluoride varnish compared with the manual chart review results that indicated 77.6% of patients had fluoride varnish. The automated quality measure performance results indicated 90.5% sensitivity, 90.8% specificity, 96.9% positive predictive value, and 75.2% negative predictive value. The authors' findings support the feasibility of using automated dental quality measure queries in the context of sufficient structured data. Information noted only in free text rather than in structured data would require using natural language processing approaches to effectively query electronic health records. To participate in self-directed quality improvement, dental clinicians must embrace the accountability era. Commitment to quality will require enhanced documentation to support near-term automated calculation of quality measures. Copyright © 2016 American Dental Association. Published by Elsevier Inc. All rights reserved.

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Science.gov Websites

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End User Information Searching on the Internet: How Do Users Search and What Do They Search For? (SIG USE)

ERIC Educational Resources Information Center

Saracevic, Tefko

2000-01-01

Summarizes a presentation that discussed findings and implications of research projects using an Internet search service and Internet-accessible vendor databases, representing the two sides of public database searching: query formulation and resource utilization. Presenters included: Tefko Saracevic, Amanda Spink, Dietmar Wolfram and Hong Xie.…

Query Results Clustering by Extending SPARQL with CLUSTER BY

NASA Astrophysics Data System (ADS)

Ławrynowicz, Agnieszka

The task of dynamic clustering of the search results proved to be useful in the Web context, where the user often does not know the granularity of the search results in advance. The goal of this paper is to provide a declarative way for invoking dynamic clustering of the results of queries submitted over Semantic Web data. To achieve this goal the paper proposes an approach that extends SPARQL by clustering abilities. The approach introduces a new statement, CLUSTER BY, into the SPARQL grammar and proposes semantics for such extension.

Treatment Options

MedlinePlus

... of America Search Query (required) Search Understanding Lupus Diagnosing Lupus Living with Lupus Research on Lupus View All ... I want to sort by: Sort all collections Diagnosing Lupus Living with Lupus I Might Have Lupus I ...

Mercury: Reusable software application for Metadata Management, Data Discovery and Access

NASA Astrophysics Data System (ADS)

Devarakonda, Ranjeet; Palanisamy, Giri; Green, James; Wilson, Bruce E.

2009-12-01

Mercury is a federated metadata harvesting, data discovery and access tool based on both open source packages and custom developed software. It was originally developed for NASA, and the Mercury development consortium now includes funding from NASA, USGS, and DOE. Mercury is itself a reusable toolset for metadata, with current use in 12 different projects. Mercury also supports the reuse of metadata by enabling searching across a range of metadata specification and standards including XML, Z39.50, FGDC, Dublin-Core, Darwin-Core, EML, and ISO-19115. Mercury provides a single portal to information contained in distributed data management systems. It collects metadata and key data from contributing project servers distributed around the world and builds a centralized index. The Mercury search interfaces then allow the users to perform simple, fielded, spatial and temporal searches across these metadata sources. One of the major goals of the recent redesign of Mercury was to improve the software reusability across the projects which currently fund the continuing development of Mercury. These projects span a range of land, atmosphere, and ocean ecological communities and have a number of common needs for metadata searches, but they also have a number of needs specific to one or a few projects To balance these common and project-specific needs, Mercury’s architecture includes three major reusable components; a harvester engine, an indexing system and a user interface component. The harvester engine is responsible for harvesting metadata records from various distributed servers around the USA and around the world. The harvester software was packaged in such a way that all the Mercury projects will use the same harvester scripts but each project will be driven by a set of configuration files. The harvested files are then passed to the Indexing system, where each of the fields in these structured metadata records are indexed properly, so that the query engine can perform simple, keyword, spatial and temporal searches across these metadata sources. The search user interface software has two API categories; a common core API which is used by all the Mercury user interfaces for querying the index and a customized API for project specific user interfaces. For our work in producing a reusable, portable, robust, feature-rich application, Mercury received a 2008 NASA Earth Science Data Systems Software Reuse Working Group Peer-Recognition Software Reuse Award. The new Mercury system is based on a Service Oriented Architecture and effectively reuses components for various services such as Thesaurus Service, Gazetteer Web Service and UDDI Directory Services. The software also provides various search services including: RSS, Geo-RSS, OpenSearch, Web Services and Portlets, integrated shopping cart to order datasets from various data centers (ORNL DAAC, NSIDC) and integrated visualization tools. Other features include: Filtering and dynamic sorting of search results, book-markable search results, save, retrieve, and modify search criteria.

Examining the themes of STD-related Internet searches to increase specificity of disease forecasting using Internet search terms.

PubMed

Johnson, Amy K; Mikati, Tarek; Mehta, Supriya D

2016-11-09

US surveillance of sexually transmitted diseases (STDs) is often delayed and incomplete which creates missed opportunities to identify and respond to trends in disease. Internet search engine data has the potential to be an efficient, economical and representative enhancement to the established surveillance system. Google Trends allows the download of de-identified search engine data, which has been used to demonstrate the positive and statistically significant association between STD-related search terms and STD rates. In this study, search engine user content was identified by surveying specific exposure groups of individuals (STD clinic patients and university students) aged 18-35. Participants were asked to list the terms they use to search for STD-related information. Google Correlate was used to validate search term content. On average STD clinic participant queries were longer compared to student queries. STD clinic participants were more likely to report using search terms that were related to symptomatology such as describing symptoms of STDs, while students were more likely to report searching for general information. These differences in search terms by subpopulation have implications for STD surveillance in populations at most risk for disease acquisition.

Secure searching of biomarkers through hybrid homomorphic encryption scheme.

PubMed

Kim, Miran; Song, Yongsoo; Cheon, Jung Hee

2017-07-26

As genome sequencing technology develops rapidly, there has lately been an increasing need to keep genomic data secure even when stored in the cloud and still used for research. We are interested in designing a protocol for the secure outsourcing matching problem on encrypted data. We propose an efficient method to securely search a matching position with the query data and extract some information at the position. After decryption, only a small amount of comparisons with the query information should be performed in plaintext state. We apply this method to find a set of biomarkers in encrypted genomes. The important feature of our method is to encode a genomic database as a single element of polynomial ring. Since our method requires a single homomorphic multiplication of hybrid scheme for query computation, it has the advantage over the previous methods in parameter size, computation complexity, and communication cost. In particular, the extraction procedure not only prevents leakage of database information that has not been queried by user but also reduces the communication cost by half. We evaluate the performance of our method and verify that the computation on large-scale personal data can be securely and practically outsourced to a cloud environment during data analysis. It takes about 3.9 s to search-and-extract the reference and alternate sequences at the queried position in a database of size 4M. Our solution for finding a set of biomarkers in DNA sequences shows the progress of cryptographic techniques in terms of their capability can support real-world genome data analysis in a cloud environment.

Modeling Rich Interactions for Web Search Intent Inference, Ranking and Evaluation

ERIC Educational Resources Information Center

Guo, Qi

2012-01-01

Billions of people interact with Web search engines daily and their interactions provide valuable clues about their interests and preferences. While modeling search behavior, such as queries and clicks on results, has been found to be effective for various Web search applications, the effectiveness of the existing approaches are limited by…

Automated Ontology Alignment with Fuselets for Community of Interest (COI) Integration

DTIC Science & Technology

2008-09-01

Search Example ............................................................................... 22 Figure 8 - Federated Search Example Revisited...integrating information from various sources through a single query. This is the traditional federated search problem, where the sources don’t...Figure 7 - Federated Search Example For the data sources in the graphic above, the ontologies align in a fairly straightforward manner

The Weaknesses of Full-Text Searching

ERIC Educational Resources Information Center

Beall, Jeffrey

2008-01-01

This paper provides a theoretical critique of the deficiencies of full-text searching in academic library databases. Because full-text searching relies on matching words in a search query with words in online resources, it is an inefficient method of finding information in a database. This matching fails to retrieve synonyms, and it also retrieves…

Just-in-Time Web Searches for Trainers & Adult Educators.

ERIC Educational Resources Information Center

Kirk, James J.

Trainers and adult educators often need to quickly locate quality information on the World Wide Web (WWW) and need assistance in searching for such information. A "search engine" is an application used to query existing information on the WWW. The three types of search engines are computer-generated indexes, directories, and meta search…

A Real-Time All-Atom Structural Search Engine for Proteins

PubMed Central

Gonzalez, Gabriel; Hannigan, Brett; DeGrado, William F.

2014-01-01

Protein designers use a wide variety of software tools for de novo design, yet their repertoire still lacks a fast and interactive all-atom search engine. To solve this, we have built the Suns program: a real-time, atomic search engine integrated into the PyMOL molecular visualization system. Users build atomic-level structural search queries within PyMOL and receive a stream of search results aligned to their query within a few seconds. This instant feedback cycle enables a new “designability”-inspired approach to protein design where the designer searches for and interactively incorporates native-like fragments from proven protein structures. We demonstrate the use of Suns to interactively build protein motifs, tertiary interactions, and to identify scaffolds compatible with hot-spot residues. The official web site and installer are located at http://www.degradolab.org/suns/ and the source code is hosted at https://github.com/godotgildor/Suns (PyMOL plugin, BSD license), https://github.com/Gabriel439/suns-cmd (command line client, BSD license), and https://github.com/Gabriel439/suns-search (search engine server, GPLv2 license). PMID:25079944

BioSearch: a semantic search engine for Bio2RDF

PubMed Central

Qiu, Honglei; Huang, Jiacheng

2017-01-01

Abstract Biomedical data are growing at an incredible pace and require substantial expertise to organize data in a manner that makes them easily findable, accessible, interoperable and reusable. Massive effort has been devoted to using Semantic Web standards and technologies to create a network of Linked Data for the life sciences, among others. However, while these data are accessible through programmatic means, effective user interfaces for non-experts to SPARQL endpoints are few and far between. Contributing to user frustrations is that data are not necessarily described using common vocabularies, thereby making it difficult to aggregate results, especially when distributed across multiple SPARQL endpoints. We propose BioSearch — a semantic search engine that uses ontologies to enhance federated query construction and organize search results. BioSearch also features a simplified query interface that allows users to optionally filter their keywords according to classes, properties and datasets. User evaluation demonstrated that BioSearch is more effective and usable than two state of the art search and browsing solutions. Database URL: http://ws.nju.edu.cn/biosearch/ PMID:29220451

A real-time all-atom structural search engine for proteins.

PubMed

Gonzalez, Gabriel; Hannigan, Brett; DeGrado, William F

2014-07-01

Protein designers use a wide variety of software tools for de novo design, yet their repertoire still lacks a fast and interactive all-atom search engine. To solve this, we have built the Suns program: a real-time, atomic search engine integrated into the PyMOL molecular visualization system. Users build atomic-level structural search queries within PyMOL and receive a stream of search results aligned to their query within a few seconds. This instant feedback cycle enables a new "designability"-inspired approach to protein design where the designer searches for and interactively incorporates native-like fragments from proven protein structures. We demonstrate the use of Suns to interactively build protein motifs, tertiary interactions, and to identify scaffolds compatible with hot-spot residues. The official web site and installer are located at http://www.degradolab.org/suns/ and the source code is hosted at https://github.com/godotgildor/Suns (PyMOL plugin, BSD license), https://github.com/Gabriel439/suns-cmd (command line client, BSD license), and https://github.com/Gabriel439/suns-search (search engine server, GPLv2 license).

An assessment of the visibility of MeSH-indexed medical web catalogs through search engines.

PubMed Central

Zweigenbaum, P.; Darmoni, S. J.; Grabar, N.; Douyère, M.; Benichou, J.

2002-01-01

Manually indexed Internet health catalogs such as CliniWeb or CISMeF provide resources for retrieving high-quality health information. Users of these quality-controlled subject gateways are most often referred to them by general search engines such as Google, AltaVista, etc. This raises several questions, among which the following: what is the relative visibility of medical Internet catalogs through search engines? This study addresses this issue by measuring and comparing the visibility of six major, MeSH-indexed health catalogs through four different search engines (AltaVista, Google, Lycos, Northern Light) in two languages (English and French). Over half a million queries were sent to the search engines; for most of these search engines, according to our measures at the time the queries were sent, the most visible catalog for English MeSH terms was CliniWeb and the most visible one for French MeSH terms was CISMeF. PMID:12463965

Using discordance to improve classification in narrative clinical databases: an application to community-acquired pneumonia.

PubMed

Hripcsak, George; Knirsch, Charles; Zhou, Li; Wilcox, Adam; Melton, Genevieve B

2007-03-01

Data mining in electronic medical records may facilitate clinical research, but much of the structured data may be miscoded, incomplete, or non-specific. The exploitation of narrative data using natural language processing may help, although nesting, varying granularity, and repetition remain challenges. In a study of community-acquired pneumonia using electronic records, these issues led to poor classification. Limiting queries to accurate, complete records led to vastly reduced, possibly biased samples. We exploited knowledge latent in the electronic records to improve classification. A similarity metric was used to cluster cases. We defined discordance as the degree to which cases within a cluster give different answers for some query that addresses a classification task of interest. Cases with higher discordance are more likely to be incorrectly classified, and can be reviewed manually to adjust the classification, improve the query, or estimate the likely accuracy of the query. In a study of pneumonia--in which the ICD9-CM coding was found to be very poor--the discordance measure was statistically significantly correlated with classification correctness (.45; 95% CI .15-.62).

Classifying Clinical Trial Eligibility Criteria to Facilitate Phased Cohort Identification Using Clinical Data Repositories.

PubMed

Wang, Amy Y; Lancaster, William J; Wyatt, Matthew C; Rasmussen, Luke V; Fort, Daniel G; Cimino, James J

2017-01-01

A major challenge in using electronic health record repositories for research is the difficulty matching subject eligibility criteria to query capabilities of the repositories. We propose categories for study criteria corresponding to the effort needed for querying those criteria: "easy" (supporting automated queries), mixed (initial automated querying with manual review), "hard" (fully manual record review), and "impossible" or "point of enrollment" (not typically in health repositories). We obtained a sample of 292 criteria from 20 studies from ClinicalTrials.gov. Six independent reviewers, three each from two academic research institutions, rated criteria according to our four types. We observed high interrater reliability both within and between institutions. The analysis demonstrated typical features of criteria that map with varying levels of difficulty to repositories. We propose using these features to improve enrollment workflow through more standardized study criteria, self-service repository queries, and analyst-mediated retrievals.

Classifying Clinical Trial Eligibility Criteria to Facilitate Phased Cohort Identification Using Clinical Data Repositories

PubMed Central

Wang, Amy Y.; Lancaster, William J.; Wyatt, Matthew C.; Rasmussen, Luke V.; Fort, Daniel G.; Cimino, James J.

2017-01-01

A major challenge in using electronic health record repositories for research is the difficulty matching subject eligibility criteria to query capabilities of the repositories. We propose categories for study criteria corresponding to the effort needed for querying those criteria: “easy” (supporting automated queries), mixed (initial automated querying with manual review), “hard” (fully manual record review), and “impossible” or “point of enrollment” (not typically in health repositories). We obtained a sample of 292 criteria from 20 studies from ClinicalTrials.gov. Six independent reviewers, three each from two academic research institutions, rated criteria according to our four types. We observed high interrater reliability both within and between institutions. The analysis demonstrated typical features of criteria that map with varying levels of difficulty to repositories. We propose using these features to improve enrollment workflow through more standardized study criteria, self-service repository queries, and analyst-mediated retrievals. PMID:29854246

Virtual Solar Observatory Distributed Query Construction

NASA Technical Reports Server (NTRS)

Gurman, J. B.; Dimitoglou, G.; Bogart, R.; Davey, A.; Hill, F.; Martens, P.

2003-01-01

Through a prototype implementation (Tian et al., this meeting) the VSO has already demonstrated the capability of unifying geographically distributed data sources following the Web Services paradigm and utilizing mechanisms such as the Simple Object Access Protocol (SOAP). So far, four participating sites (Stanford, Montana State University, National Solar Observatory and the Solar Data Analysis Center) permit Web-accessible, time-based searches that allow browse access to a number of diverse data sets. Our latest work includes the extension of the simple, time-based queries to include numerous other searchable observation parameters. For VSO users, this extended functionality enables more refined searches. For the VSO, it is a proof of concept that more complex, distributed queries can be effectively constructed and that results from heterogeneous, remote sources can be synthesized and presented to users as a single, virtual data product.

Enhanced Approximate Nearest Neighbor via Local Area Focused Search.

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

Gonzales, Antonio; Blazier, Nicholas Paul

Approximate Nearest Neighbor (ANN) algorithms are increasingly important in machine learning, data mining, and image processing applications. There is a large family of space- partitioning ANN algorithms, such as randomized KD-Trees, that work well in practice but are limited by an exponential increase in similarity comparisons required to optimize recall. Additionally, they only support a small set of similarity metrics. We present Local Area Fo- cused Search (LAFS), a method that enhances the way queries are performed using an existing ANN index. Instead of a single query, LAFS performs a number of smaller (fewer similarity comparisons) queries and focuses onmore » a local neighborhood which is refined as candidates are identified. We show that our technique improves performance on several well known datasets and is easily extended to general similarity metrics using kernel projection techniques.« less

Children & Teens (with Lupus)

MedlinePlus

... of America Search Query (required) Search Understanding Lupus Diagnosing Lupus Living with Lupus Research on Lupus View All ... article Medications for treating lupus in children article Diagnosing lupus in children article Types of health care providers ...

Exploring personalized searches using tag-based user profiles and resource profiles in folksonomy.

PubMed

Cai, Yi; Li, Qing; Xie, Haoran; Min, Huaqin

2014-10-01

With the increase in resource-sharing websites such as YouTube and Flickr, many shared resources have arisen on the Web. Personalized searches have become more important and challenging since users demand higher retrieval quality. To achieve this goal, personalized searches need to take users' personalized profiles and information needs into consideration. Collaborative tagging (also known as folksonomy) systems allow users to annotate resources with their own tags, which provides a simple but powerful way for organizing, retrieving and sharing different types of social resources. In this article, we examine the limitations of previous tag-based personalized searches. To handle these limitations, we propose a new method to model user profiles and resource profiles in collaborative tagging systems. We use a normalized term frequency to indicate the preference degree of a user on a tag. A novel search method using such profiles of users and resources is proposed to facilitate the desired personalization in resource searches. In our framework, instead of the keyword matching or similarity measurement used in previous works, the relevance measurement between a resource and a user query (termed the query relevance) is treated as a fuzzy satisfaction problem of a user's query requirements. We implement a prototype system called the Folksonomy-based Multimedia Retrieval System (FMRS). Experiments using the FMRS data set and the MovieLens data set show that our proposed method outperforms baseline methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

76 FR 9295 - Privacy Act; Exempt Record System

Federal Register 2010, 2011, 2012, 2013, 2014

2011-02-17

... entities can self-query. One of the primary purposes of these data will be use of this information by a... information on all other queries to the data bank, disclosure of law enforcement queries could compromise ongoing investigation activities. The premature disclosure of the existence of a law enforcement activity...

Using search query surveillance to monitor tax avoidance and smoking cessation following the United States' 2009 "SCHIP" cigarette tax increase.

PubMed

Ayers, John W; Ribisl, Kurt; Brownstein, John S

2011-03-16

Smokers can use the web to continue or quit their habit. Online vendors sell reduced or tax-free cigarettes lowering smoking costs, while health advocates use the web to promote cessation. We examined how smokers' tax avoidance and smoking cessation Internet search queries were motivated by the United States' (US) 2009 State Children's Health Insurance Program (SCHIP) federal cigarette excise tax increase and two other state specific tax increases. Google keyword searches among residents in a taxed geography (US or US state) were compared to an untaxed geography (Canada) for two years around each tax increase. Search data were normalized to a relative search volume (RSV) scale, where the highest search proportion was labeled 100 with lesser proportions scaled by how they relatively compared to the highest proportion. Changes in RSV were estimated by comparing means during and after the tax increase to means before the tax increase, across taxed and untaxed geographies. The SCHIP tax was associated with an 11.8% (95% confidence interval [95%CI], 5.7 to 17.9; p<.001) immediate increase in cessation searches; however, searches quickly abated and approximated differences from pre-tax levels in Canada during the months after the tax. Tax avoidance searches increased 27.9% (95%CI, 15.9 to 39.9; p<.001) and 5.3% (95%CI, 3.6 to 7.1; p<.001) during and in the months after the tax compared to Canada, respectively, suggesting avoidance is the more pronounced and durable response. Trends were similar for state-specific tax increases but suggest strong interactive processes across taxes. When the SCHIP tax followed Florida's tax, versus not, it promoted more cessation and avoidance searches. Efforts to combat tax avoidance and increase cessation may be enhanced by using interventions targeted and tailored to smokers' searches. Search query surveillance is a valuable real-time, free and public method, that may be generalized to other behavioral, biological, informational or psychological outcomes manifested online.

Development and evaluation of a biomedical search engine using a predicate-based vector space model.

PubMed

Kwak, Myungjae; Leroy, Gondy; Martinez, Jesse D; Harwell, Jeffrey

2013-10-01

Although biomedical information available in articles and patents is increasing exponentially, we continue to rely on the same information retrieval methods and use very few keywords to search millions of documents. We are developing a fundamentally different approach for finding much more precise and complete information with a single query using predicates instead of keywords for both query and document representation. Predicates are triples that are more complex datastructures than keywords and contain more structured information. To make optimal use of them, we developed a new predicate-based vector space model and query-document similarity function with adjusted tf-idf and boost function. Using a test bed of 107,367 PubMed abstracts, we evaluated the first essential function: retrieving information. Cancer researchers provided 20 realistic queries, for which the top 15 abstracts were retrieved using a predicate-based (new) and keyword-based (baseline) approach. Each abstract was evaluated, double-blind, by cancer researchers on a 0-5 point scale to calculate precision (0 versus higher) and relevance (0-5 score). Precision was significantly higher (p<.001) for the predicate-based (80%) than for the keyword-based (71%) approach. Relevance was almost doubled with the predicate-based approach-2.1 versus 1.6 without rank order adjustment (p<.001) and 1.34 versus 0.98 with rank order adjustment (p<.001) for predicate--versus keyword-based approach respectively. Predicates can support more precise searching than keywords, laying the foundation for rich and sophisticated information search. Copyright © 2013 Elsevier Inc. All rights reserved.

Assessing Ebola-related web search behaviour: insights and implications from an analytical study of Google Trends-based query volumes.

PubMed

Alicino, Cristiano; Bragazzi, Nicola Luigi; Faccio, Valeria; Amicizia, Daniela; Panatto, Donatella; Gasparini, Roberto; Icardi, Giancarlo; Orsi, Andrea

2015-12-10

The 2014 Ebola epidemic in West Africa has attracted public interest worldwide, leading to millions of Ebola-related Internet searches being performed during the period of the epidemic. This study aimed to evaluate and interpret Google search queries for terms related to the Ebola outbreak both at the global level and in all countries where primary cases of Ebola occurred. The study also endeavoured to look at the correlation between the number of overall and weekly web searches and the number of overall and weekly new cases of Ebola. Google Trends (GT) was used to explore Internet activity related to Ebola. The study period was from 29 December 2013 to 14 June 2015. Pearson's correlation was performed to correlate Ebola-related relative search volumes (RSVs) with the number of weekly and overall Ebola cases. Multivariate regression was performed using Ebola-related RSV as a dependent variable, and the overall number of Ebola cases and the Human Development Index were used as predictor variables. The greatest RSV was registered in the three West African countries mainly affected by the Ebola epidemic. The queries varied in the different countries. Both quantitative and qualitative differences between the affected African countries and other Western countries with primary cases were noted, in relation to the different flux volumes and different time courses. In the affected African countries, web query search volumes were mostly concentrated in the capital areas. However, in Western countries, web queries were uniformly distributed over the national territory. In terms of the three countries mainly affected by the Ebola epidemic, the correlation between the number of new weekly cases of Ebola and the weekly GT index varied from weak to moderate. The correlation between the number of Ebola cases registered in all countries during the study period and the GT index was very high. Google Trends showed a coarse-grained nature, strongly correlating with global epidemiological data, but was weaker at country level, as it was prone to distortions induced by unbalanced media coverage and the digital divide. Global and local health agencies could usefully exploit GT data to identify disease-related information needs and plan proper communication strategies, particularly in the case of health-threatening events.

Design and implementation of web-based discharge summary note based on service-oriented architecture.

PubMed

Chen, Chi-Huang; Hsieh, Sung-Huai; Su, Yu-Shuan; Hsu, Kai-Ping; Lee, Hsiu-Hui; Lai, Feipei

2012-02-01

Discharge summary note is one of the essential clinical data in medical records, and it concisely capsules a patient's status during hospitalization. In the article, we adopt web-based architecture in developing a new discharge summary system for the Healthcare Information System of National Taiwan University Hospital, to improve the traditional client/sever architecture. The article elaborates the design approaches and implementation illustrations in detail, including patients' summary query and searching, model and phrase quoted, summary check list, major editing blocks as well as other functionalities. The system has been on-line and achieves successfully since October 2009.

Guiding Students to Answers: Query Recommendation

ERIC Educational Resources Information Center

Yilmazel, Ozgur

2011-01-01

This paper reports on a guided navigation system built on the textbook search engine developed at Anadolu University to support distance education students. The search engine uses Turkish Language specific language processing modules to enable searches over course material presented in Open Education Faculty textbooks. We implemented a guided…

Framing Electronic Medical Records as Polylingual Documents in Query Expansion

PubMed Central

Huang, Edward W; Wang, Sheng; Lee, Doris Jung-Lin; Zhang, Runshun; Liu, Baoyan; Zhou, Xuezhong; Zhai, ChengXiang

2017-01-01

We present a study of electronic medical record (EMR) retrieval that emulates situations in which a doctor treats a new patient. Given a query consisting of a new patient’s symptoms, the retrieval system returns the set of most relevant records of previously treated patients. However, due to semantic, functional, and treatment synonyms in medical terminology, queries are often incomplete and thus require enhancement. In this paper, we present a topic model that frames symptoms and treatments as separate languages. Our experimental results show that this method improves retrieval performance over several baselines with statistical significance. These baselines include methods used in prior studies as well as state-of-the-art embedding techniques. Finally, we show that our proposed topic model discovers all three types of synonyms to improve medical record retrieval. PMID:29854161

Can Google Trends search queries contribute to risk diversification?

PubMed

Kristoufek, Ladislav

2013-01-01

Portfolio diversification and active risk management are essential parts of financial analysis which became even more crucial (and questioned) during and after the years of the Global Financial Crisis. We propose a novel approach to portfolio diversification using the information of searched items on Google Trends. The diversification is based on an idea that popularity of a stock measured by search queries is correlated with the stock riskiness. We penalize the popular stocks by assigning them lower portfolio weights and we bring forward the less popular, or peripheral, stocks to decrease the total riskiness of the portfolio. Our results indicate that such strategy dominates both the benchmark index and the uniformly weighted portfolio both in-sample and out-of-sample.

Can Google Trends search queries contribute to risk diversification?

PubMed Central

Kristoufek, Ladislav

2013-01-01

Portfolio diversification and active risk management are essential parts of financial analysis which became even more crucial (and questioned) during and after the years of the Global Financial Crisis. We propose a novel approach to portfolio diversification using the information of searched items on Google Trends. The diversification is based on an idea that popularity of a stock measured by search queries is correlated with the stock riskiness. We penalize the popular stocks by assigning them lower portfolio weights and we bring forward the less popular, or peripheral, stocks to decrease the total riskiness of the portfolio. Our results indicate that such strategy dominates both the benchmark index and the uniformly weighted portfolio both in-sample and out-of-sample. PMID:24048448

Application of kernel functions for accurate similarity search in large chemical databases.

PubMed

Wang, Xiaohong; Huan, Jun; Smalter, Aaron; Lushington, Gerald H

2010-04-29

Similarity search in chemical structure databases is an important problem with many applications in chemical genomics, drug design, and efficient chemical probe screening among others. It is widely believed that structure based methods provide an efficient way to do the query. Recently various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models, graph kernel functions can not be applied to large chemical compound database due to the high computational complexity and the difficulties in indexing similarity search for large databases. To bridge graph kernel function and similarity search in chemical databases, we applied a novel kernel-based similarity measurement, developed in our team, to measure similarity of graph represented chemicals. In our method, we utilize a hash table to support new graph kernel function definition, efficient storage and fast search. We have applied our method, named G-hash, to large chemical databases. Our results show that the G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Moreover, the similarity measurement and the index structure is scalable to large chemical databases with smaller indexing size, and faster query processing time as compared to state-of-the-art indexing methods such as Daylight fingerprints, C-tree and GraphGrep. Efficient similarity query processing method for large chemical databases is challenging since we need to balance running time efficiency and similarity search accuracy. Our previous similarity search method, G-hash, provides a new way to perform similarity search in chemical databases. Experimental study validates the utility of G-hash in chemical databases.

A topic clustering approach to finding similar questions from large question and answer archives.

PubMed

Zhang, Wei-Nan; Liu, Ting; Yang, Yang; Cao, Liujuan; Zhang, Yu; Ji, Rongrong

2014-01-01

With the blooming of Web 2.0, Community Question Answering (CQA) services such as Yahoo! Answers (http://answers.yahoo.com), WikiAnswer (http://wiki.answers.com), and Baidu Zhidao (http://zhidao.baidu.com), etc., have emerged as alternatives for knowledge and information acquisition. Over time, a large number of question and answer (Q&A) pairs with high quality devoted by human intelligence have been accumulated as a comprehensive knowledge base. Unlike the search engines, which return long lists of results, searching in the CQA services can obtain the correct answers to the question queries by automatically finding similar questions that have already been answered by other users. Hence, it greatly improves the efficiency of the online information retrieval. However, given a question query, finding the similar and well-answered questions is a non-trivial task. The main challenge is the word mismatch between question query (query) and candidate question for retrieval (question). To investigate this problem, in this study, we capture the word semantic similarity between query and question by introducing the topic modeling approach. We then propose an unsupervised machine-learning approach to finding similar questions on CQA Q&A archives. The experimental results show that our proposed approach significantly outperforms the state-of-the-art methods.

Omicseq: a web-based search engine for exploring omics datasets.

PubMed

Sun, Xiaobo; Pittard, William S; Xu, Tianlei; Chen, Li; Zwick, Michael E; Jiang, Xiaoqian; Wang, Fusheng; Qin, Zhaohui S

2017-07-03

The development and application of high-throughput genomics technologies has resulted in massive quantities of diverse omics data that continue to accumulate rapidly. These rich datasets offer unprecedented and exciting opportunities to address long standing questions in biomedical research. However, our ability to explore and query the content of diverse omics data is very limited. Existing dataset search tools rely almost exclusively on the metadata. A text-based query for gene name(s) does not work well on datasets wherein the vast majority of their content is numeric. To overcome this barrier, we have developed Omicseq, a novel web-based platform that facilitates the easy interrogation of omics datasets holistically to improve 'findability' of relevant data. The core component of Omicseq is trackRank, a novel algorithm for ranking omics datasets that fully uses the numerical content of the dataset to determine relevance to the query entity. The Omicseq system is supported by a scalable and elastic, NoSQL database that hosts a large collection of processed omics datasets. In the front end, a simple, web-based interface allows users to enter queries and instantly receive search results as a list of ranked datasets deemed to be the most relevant. Omicseq is freely available at http://www.omicseq.org. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

A Statistical Ontology-Based Approach to Ranking for Multiword Search

ERIC Educational Resources Information Center

Kim, Jinwoo

2013-01-01

Keyword search is a prominent data retrieval method for the Web, largely because the simple and efficient nature of keyword processing allows a large amount of information to be searched with fast response. However, keyword search approaches do not formally capture the clear meaning of a keyword query and fail to address the semantic relationships…

Designing a Syntax-Based Retrieval System for Supporting Language Learning

ERIC Educational Resources Information Center

Tsao, Nai-Lung; Kuo, Chin-Hwa; Wible, David; Hung, Tsung-Fu

2009-01-01

In this paper, we propose a syntax-based text retrieval system for on-line language learning and use a fast regular expression search engine as its main component. Regular expression searches provide more scalable querying and search results than keyword-based searches. However, without a well-designed index scheme, the execution time of regular…

Efficient hemodynamic event detection utilizing relational databases and wavelet analysis

NASA Technical Reports Server (NTRS)

Saeed, M.; Mark, R. G.

2001-01-01

Development of a temporal query framework for time-oriented medical databases has hitherto been a challenging problem. We describe a novel method for the detection of hemodynamic events in multiparameter trends utilizing wavelet coefficients in a MySQL relational database. Storage of the wavelet coefficients allowed for a compact representation of the trends, and provided robust descriptors for the dynamics of the parameter time series. A data model was developed to allow for simplified queries along several dimensions and time scales. Of particular importance, the data model and wavelet framework allowed for queries to be processed with minimal table-join operations. A web-based search engine was developed to allow for user-defined queries. Typical queries required between 0.01 and 0.02 seconds, with at least two orders of magnitude improvement in speed over conventional queries. This powerful and innovative structure will facilitate research on large-scale time-oriented medical databases.

G-Hash: Towards Fast Kernel-based Similarity Search in Large Graph Databases.

PubMed

Wang, Xiaohong; Smalter, Aaron; Huan, Jun; Lushington, Gerald H

2009-01-01

Structured data including sets, sequences, trees and graphs, pose significant challenges to fundamental aspects of data management such as efficient storage, indexing, and similarity search. With the fast accumulation of graph databases, similarity search in graph databases has emerged as an important research topic. Graph similarity search has applications in a wide range of domains including cheminformatics, bioinformatics, sensor network management, social network management, and XML documents, among others.Most of the current graph indexing methods focus on subgraph query processing, i.e. determining the set of database graphs that contains the query graph and hence do not directly support similarity search. In data mining and machine learning, various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models for supervised learning, graph kernel functions have (i) high computational complexity and (ii) non-trivial difficulty to be indexed in a graph database.Our objective is to bridge graph kernel function and similarity search in graph databases by proposing (i) a novel kernel-based similarity measurement and (ii) an efficient indexing structure for graph data management. Our method of similarity measurement builds upon local features extracted from each node and their neighboring nodes in graphs. A hash table is utilized to support efficient storage and fast search of the extracted local features. Using the hash table, a graph kernel function is defined to capture the intrinsic similarity of graphs and for fast similarity query processing. We have implemented our method, which we have named G-hash, and have demonstrated its utility on large chemical graph databases. Our results show that the G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Most importantly, the new similarity measurement and the index structure is scalable to large database with smaller indexing size, faster indexing construction time, and faster query processing time as compared to state-of-the-art indexing methods such as C-tree, gIndex, and GraphGrep.

They’re heating up: Internet search query trends reveal significant public interest in heat-not-burn tobacco products

PubMed Central

Caputi, Theodore L.; Leas, Eric; Dredze, Mark; Cohen, Joanna E.; Ayers, John W.

2017-01-01

Heat-not-burn tobacco products, battery powered devices that heat leaf tobacco to approximately 500 degrees Fahrenheit to produce an inhalable aerosol, are being introduced in markets around the world. Japan, where manufacturers have marketed several heat-not-burn brands since 2014, has been the focal national test market, with the intention of developing global marketing strategies. We used Google search query data to estimate, for the first time, the scale and growth potential of heat-not-burn tobacco products. Average monthly searches for heat-not-burn products rose 1,426% (95%CI: 746,3574) between their first (2015) and second (2016) complete years on the market and an additional 100% (95%CI: 60, 173) between the products second (2016) and third years on the market (Jan-Sep 2017). There are now between 5.9 and 7.5 million heat-not-burn related Google searches in Japan each month based on September 2017 estimates. Moreover, forecasts relying on the historical trends suggest heat-not-burn searches will increase an additional 32% (95%CI: -4 to 79) during 2018, compared to current estimates for 2017 (Jan-Sep), with continued growth thereafter expected. Contrasting heat-not-burn’s rise in Japan to electronic cigarettes’ rise in the United States we find searches for heat-not-burn eclipsed electronic cigarette searches during April 2016. Moreover, the change in average monthly queries for heat-not-burn in Japan between 2015 and 2017 was 399 (95% CI: 184, 1490) times larger than the change in average monthly queries for electronic cigarettes in the Unites States over the same time period, increasing by 2,956% (95% CI: 1729, 7304) compared to only 7% (95% CI: 3,13). Our findings are a clarion call for tobacco control leaders to ready themselves as heat-not-burn tobacco products will likely garner substantial interest as they are introduced into new markets. Public health practitioners should expand heat-not-burn tobacco product surveillance, adjust existing tobacco control strategies to account for heat-not-burn tobacco products, and preemptively study the health risks/benefits, popular perceptions, and health messaging around heat-not-burn tobacco products. PMID:29020019

Is 'self-medication' a useful term to retrieve related publications in the literature? A systematic exploration of related terms.

PubMed

Mansouri, Ava; Sarayani, Amir; Ashouri, Asieh; Sherafatmand, Mona; Hadjibabaie, Molouk; Gholami, Kheirollah

2015-01-01

Self-Medication (SM), i.e. using medications to treat oneself, is a major concern for health researchers and policy makers. The terms "self medication" or "self-medication" (SM terms) have been used to explain various concepts while several terms have also been employed to define this practice. Hence, retrieving relevant publications would require exhaustive literature screening. So, we assessed the current situation of SM terms in the literature to improve the relevancy of search outcomes. In this Systematic exploration, SM terms were searched in the 6 following databases and publisher's portals till April 2012: Web of Science, Scopus, PubMed, Google scholar, ScienceDirect, and Wiley. A simple search query was used to include only publications with SM terms. We used Relative-Risk (RR) to estimate the probability of SM terms use in related compared to unrelated publications. Sensitivity and specificity of SM terms as keywords in search query were also calculated. Relevant terms to SM practice were extracted and their Likelihood Ratio positive and negative (LR+/-) were calculated to assess their effect on the probability of search outcomes relevancy in addition to previous search queries. We also evaluated the content of unrelated publications. All mentioned steps were performed in title (TI) and title or abstract (TIAB) of publications. 1999 related and 1917 unrelated publications were found. SM terms RR was 4.5 in TI and 2.1 in TIAB. SM terms sensitivity and specificity respectively were 55.4% and 87.7% in TI and 84.0% and 59.5% in TIAB. "OTC" and "Over-The-Counter Medication", with LR+ 16.78 and 16.30 respectively, provided the most conclusive increase in the probability of the relevancy of publications. The most common unrelated SM themes were self-medication hypothesis, drug abuse and Zoopharmacognosy. Due to relatively low specificity or sensitivity of SM terms, relevant terms should be employed in search queries and clear definitions of SM applications should be applied to improve the relevancy of publications.

They're heating up: Internet search query trends reveal significant public interest in heat-not-burn tobacco products.

PubMed

Caputi, Theodore L; Leas, Eric; Dredze, Mark; Cohen, Joanna E; Ayers, John W

2017-01-01

Heat-not-burn tobacco products, battery powered devices that heat leaf tobacco to approximately 500 degrees Fahrenheit to produce an inhalable aerosol, are being introduced in markets around the world. Japan, where manufacturers have marketed several heat-not-burn brands since 2014, has been the focal national test market, with the intention of developing global marketing strategies. We used Google search query data to estimate, for the first time, the scale and growth potential of heat-not-burn tobacco products. Average monthly searches for heat-not-burn products rose 1,426% (95%CI: 746,3574) between their first (2015) and second (2016) complete years on the market and an additional 100% (95%CI: 60, 173) between the products second (2016) and third years on the market (Jan-Sep 2017). There are now between 5.9 and 7.5 million heat-not-burn related Google searches in Japan each month based on September 2017 estimates. Moreover, forecasts relying on the historical trends suggest heat-not-burn searches will increase an additional 32% (95%CI: -4 to 79) during 2018, compared to current estimates for 2017 (Jan-Sep), with continued growth thereafter expected. Contrasting heat-not-burn's rise in Japan to electronic cigarettes' rise in the United States we find searches for heat-not-burn eclipsed electronic cigarette searches during April 2016. Moreover, the change in average monthly queries for heat-not-burn in Japan between 2015 and 2017 was 399 (95% CI: 184, 1490) times larger than the change in average monthly queries for electronic cigarettes in the Unites States over the same time period, increasing by 2,956% (95% CI: 1729, 7304) compared to only 7% (95% CI: 3,13). Our findings are a clarion call for tobacco control leaders to ready themselves as heat-not-burn tobacco products will likely garner substantial interest as they are introduced into new markets. Public health practitioners should expand heat-not-burn tobacco product surveillance, adjust existing tobacco control strategies to account for heat-not-burn tobacco products, and preemptively study the health risks/benefits, popular perceptions, and health messaging around heat-not-burn tobacco products.

Is ‘Self-Medication’ a Useful Term to Retrieve Related Publications in the Literature? A Systematic Exploration of Related Terms

PubMed Central

Mansouri, Ava; Sarayani, Amir; Ashouri, Asieh; Sherafatmand, Mona; Hadjibabaie, Molouk; Gholami, Kheirollah

2015-01-01

Background Self-Medication (SM), i.e. using medications to treat oneself, is a major concern for health researchers and policy makers. The terms “self medication” or “self-medication” (SM terms) have been used to explain various concepts while several terms have also been employed to define this practice. Hence, retrieving relevant publications would require exhaustive literature screening. So, we assessed the current situation of SM terms in the literature to improve the relevancy of search outcomes. Methods In this Systematic exploration, SM terms were searched in the 6 following databases and publisher’s portals till April 2012: Web of Science, Scopus, PubMed, Google scholar, ScienceDirect, and Wiley. A simple search query was used to include only publications with SM terms. We used Relative-Risk (RR) to estimate the probability of SM terms use in related compared to unrelated publications. Sensitivity and specificity of SM terms as keywords in search query were also calculated. Relevant terms to SM practice were extracted and their Likelihood Ratio positive and negative (LR+/-) were calculated to assess their effect on the probability of search outcomes relevancy in addition to previous search queries. We also evaluated the content of unrelated publications. All mentioned steps were performed in title (TI) and title or abstract (TIAB) of publications. Results 1999 related and 1917 unrelated publications were found. SM terms RR was 4.5 in TI and 2.1 in TIAB. SM terms sensitivity and specificity respectively were 55.4% and 87.7% in TI and 84.0% and 59.5% in TIAB. “OTC” and “Over-The-Counter Medication”, with LR+ 16.78 and 16.30 respectively, provided the most conclusive increase in the probability of the relevancy of publications. The most common unrelated SM themes were self-medication hypothesis, drug abuse and Zoopharmacognosy. Conclusions Due to relatively low specificity or sensitivity of SM terms, relevant terms should be employed in search queries and clear definitions of SM applications should be applied to improve the relevancy of publications. PMID:25932634

Automation and integration of components for generalized semantic markup of electronic medical texts.

PubMed Central

Dugan, J. M.; Berrios, D. C.; Liu, X.; Kim, D. K.; Kaizer, H.; Fagan, L. M.

1999-01-01

Our group has built an information retrieval system based on a complex semantic markup of medical textbooks. We describe the construction of a set of web-based knowledge-acquisition tools that expedites the collection and maintenance of the concepts required for text markup and the search interface required for information retrieval from the marked text. In the text markup system, domain experts (DEs) identify sections of text that contain one or more elements from a finite set of concepts. End users can then query the text using a predefined set of questions, each of which identifies a subset of complementary concepts. The search process matches that subset of concepts to relevant points in the text. The current process requires that the DE invest significant time to generate the required concepts and questions. We propose a new system--called ACQUIRE (Acquisition of Concepts and Queries in an Integrated Retrieval Environment)--that assists a DE in two essential tasks in the text-markup process. First, it helps her to develop, edit, and maintain the concept model: the set of concepts with which she marks the text. Second, ACQUIRE helps her to develop a query model: the set of specific questions that end users can later use to search the marked text. The DE incorporates concepts from the concept model when she creates the questions in the query model. The major benefit of the ACQUIRE system is a reduction in the time and effort required for the text-markup process. We compared the process of concept- and query-model creation using ACQUIRE to the process used in previous work by rebuilding two existing models that we previously constructed manually. We observed a significant decrease in the time required to build and maintain the concept and query models. Images Figure 1 Figure 2 Figure 4 Figure 5 PMID:10566457

Improving average ranking precision in user searches for biomedical research datasets

PubMed Central

Gobeill, Julien; Gaudinat, Arnaud; Vachon, Thérèse; Ruch, Patrick

2017-01-01

Abstract Availability of research datasets is keystone for health and life science study reproducibility and scientific progress. Due to the heterogeneity and complexity of these data, a main challenge to be overcome by research data management systems is to provide users with the best answers for their search queries. In the context of the 2016 bioCADDIE Dataset Retrieval Challenge, we investigate a novel ranking pipeline to improve the search of datasets used in biomedical experiments. Our system comprises a query expansion model based on word embeddings, a similarity measure algorithm that takes into consideration the relevance of the query terms, and a dataset categorization method that boosts the rank of datasets matching query constraints. The system was evaluated using a corpus with 800k datasets and 21 annotated user queries, and provided competitive results when compared to the other challenge participants. In the official run, it achieved the highest infAP, being +22.3% higher than the median infAP of the participant’s best submissions. Overall, it is ranked at top 2 if an aggregated metric using the best official measures per participant is considered. The query expansion method showed positive impact on the system’s performance increasing our baseline up to +5.0% and +3.4% for the infAP and infNDCG metrics, respectively. The similarity measure algorithm showed robust performance in different training conditions, with small performance variations compared to the Divergence from Randomness framework. Finally, the result categorization did not have significant impact on the system’s performance. We believe that our solution could be used to enhance biomedical dataset management systems. The use of data driven expansion methods, such as those based on word embeddings, could be an alternative to the complexity of biomedical terminologies. Nevertheless, due to the limited size of the assessment set, further experiments need to be performed to draw conclusive results. Database URL: https://biocaddie.org/benchmark-data PMID:29220475

SEMCARE: Multilingual Semantic Search in Semi-Structured Clinical Data.

PubMed

López-García, Pablo; Kreuzthaler, Markus; Schulz, Stefan; Scherr, Daniel; Daumke, Philipp; Markó, Kornél; Kors, Jan A; van Mulligen, Erik M; Wang, Xinkai; Gonna, Hanney; Behr, Elijah; Honrado, Ángel

2016-01-01

The vast amount of clinical data in electronic health records constitutes a great potential for secondary use. However, most of this content consists of unstructured or semi-structured texts, which is difficult to process. Several challenges are still pending: medical language idiosyncrasies in different natural languages, and the large variety of medical terminology systems. In this paper we present SEMCARE, a European initiative designed to minimize these problems by providing a multi-lingual platform (English, German, and Dutch) that allows users to express complex queries and obtain relevant search results from clinical texts. SEMCARE is based on a selection of adapted biomedical terminologies, together with Apache UIMA and Apache Solr as open source state-of-the-art natural language pipeline and indexing technologies. SEMCARE has been deployed and is currently being tested at three medical institutions in the UK, Austria, and the Netherlands, showing promising results in a cardiology use case.

Image Search Reranking With Hierarchical Topic Awareness.

PubMed

Tian, Xinmei; Yang, Linjun; Lu, Yijuan; Tian, Qi; Tao, Dacheng

2015-10-01

With much attention from both academia and industrial communities, visual search reranking has recently been proposed to refine image search results obtained from text-based image search engines. Most of the traditional reranking methods cannot capture both relevance and diversity of the search results at the same time. Or they ignore the hierarchical topic structure of search result. Each topic is treated equally and independently. However, in real applications, images returned for certain queries are naturally in hierarchical organization, rather than simple parallel relation. In this paper, a new reranking method "topic-aware reranking (TARerank)" is proposed. TARerank describes the hierarchical topic structure of search results in one model, and seamlessly captures both relevance and diversity of the image search results simultaneously. Through a structured learning framework, relevance and diversity are modeled in TARerank by a set of carefully designed features, and then the model is learned from human-labeled training samples. The learned model is expected to predict reranking results with high relevance and diversity for testing queries. To verify the effectiveness of the proposed method, we collect an image search dataset and conduct comparison experiments on it. The experimental results demonstrate that the proposed TARerank outperforms the existing relevance-based and diversified reranking methods.

Abyss or Shelter? On the Relevance of Web Search Engines' Search Results When People Google for Suicide.

PubMed

Haim, Mario; Arendt, Florian; Scherr, Sebastian

2017-02-01

Despite evidence that suicide rates can increase after suicides are widely reported in the media, appropriate depictions of suicide in the media can help people to overcome suicidal crises and can thus elicit preventive effects. We argue on the level of individual media users that a similar ambivalence can be postulated for search results on online suicide-related search queries. Importantly, the filter bubble hypothesis (Pariser, 2011) states that search results are biased by algorithms based on a person's previous search behavior. In this study, we investigated whether suicide-related search queries, including either potentially suicide-preventive or -facilitative terms, influence subsequent search results. This might thus protect or harm suicidal Internet users. We utilized a 3 (search history: suicide-related harmful, suicide-related helpful, and suicide-unrelated) × 2 (reactive: clicking the top-most result link and no clicking) experimental design applying agent-based testing. While findings show no influences either of search histories or of reactivity on search results in a subsequent situation, the presentation of a helpline offer raises concerns about possible detrimental algorithmic decision-making: Algorithms "decided" whether or not to present a helpline, and this automated decision, then, followed the agent throughout the rest of the observation period. Implications for policy-making and search providers are discussed.

Incremental Query Rewriting with Resolution

NASA Astrophysics Data System (ADS)

Riazanov, Alexandre; Aragão, Marcelo A. T.

We address the problem of semantic querying of relational databases (RDB) modulo knowledge bases using very expressive knowledge representation formalisms, such as full first-order logic or its various fragments. We propose to use a resolution-based first-order logic (FOL) reasoner for computing schematic answers to deductive queries, with the subsequent translation of these schematic answers to SQL queries which are evaluated using a conventional relational DBMS. We call our method incremental query rewriting, because an original semantic query is rewritten into a (potentially infinite) series of SQL queries. In this chapter, we outline the main idea of our technique - using abstractions of databases and constrained clauses for deriving schematic answers, and provide completeness and soundness proofs to justify the applicability of this technique to the case of resolution for FOL without equality. The proposed method can be directly used with regular RDBs, including legacy databases. Moreover, we propose it as a potential basis for an efficient Web-scale semantic search technology.

KBGIS-2: A knowledge-based geographic information system

NASA Technical Reports Server (NTRS)

Smith, T.; Peuquet, D.; Menon, S.; Agarwal, P.

1986-01-01

The architecture and working of a recently implemented knowledge-based geographic information system (KBGIS-2) that was designed to satisfy several general criteria for the geographic information system are described. The system has four major functions that include query-answering, learning, and editing. The main query finds constrained locations for spatial objects that are describable in a predicate-calculus based spatial objects language. The main search procedures include a family of constraint-satisfaction procedures that use a spatial object knowledge base to search efficiently for complex spatial objects in large, multilayered spatial data bases. These data bases are represented in quadtree form. The search strategy is designed to reduce the computational cost of search in the average case. The learning capabilities of the system include the addition of new locations of complex spatial objects to the knowledge base as queries are answered, and the ability to learn inductively definitions of new spatial objects from examples. The new definitions are added to the knowledge base by the system. The system is currently performing all its designated tasks successfully, although currently implemented on inadequate hardware. Future reports will detail the performance characteristics of the system, and various new extensions are planned in order to enhance the power of KBGIS-2.

Automated Assistance in the Formulation of Search Statements for Bibliographic Databases.

ERIC Educational Resources Information Center

Oakes, Michael P.; Taylor, Malcolm J.

1998-01-01

Reports on the design of an automated query system to help pharmacologists access the Derwent Drug File (DDF). Topics include knowledge types; knowledge representation; role of the search intermediary; vocabulary selection, thesaurus, and user input in natural language; browsing; evaluation methods; and search statement generation for the World…

Putting Google Scholar to the Test: A Preliminary Study

ERIC Educational Resources Information Center

Robinson, Mary L.; Wusteman, Judith

2007-01-01

Purpose: To describe a small-scale quantitative evaluation of the scholarly information search engine, Google Scholar. Design/methodology/approach: Google Scholar's ability to retrieve scholarly information was compared to that of three popular search engines: Ask.com, Google and Yahoo! Test queries were presented to all four search engines and…

"Just the Answers, Please": Choosing a Web Search Service.

ERIC Educational Resources Information Center

Feldman, Susan

1997-01-01

Presents guidelines for selecting World Wide Web search engines. Real-life questions were used to test six search engines. Queries sought company information, product reviews, medical information, foreign information, technical reports, and current events. Compares performance and features of AltaVista, Excite, HotBot, Infoseek, Lycos, and Open…

US consumer interest in non-cigarette tobacco products spikes around the 2009 federal tobacco tax increase.

PubMed

Jo, Catherine L; Ayers, John W; Althouse, Benjamin M; Emery, Sherry; Huang, Jidong; Ribisl, Kurt M

2015-07-01

This quasi-experimental longitudinal study monitored aggregate Google search queries as a proxy for consumer interest in non-cigarette tobacco products (NTP) around the time of the 2009 US federal tobacco tax increase. Query trends for searches mentioning common NTP were downloaded from Google's public archives. The mean relative increase was estimated by comparing the observed with expected query volume for the 16 weeks around the tax. After the tax was announced, queries spiked for chewing tobacco, cigarillos, electronic cigarettes ('e-cigarettes'), roll-your-own (RYO) tobacco, snuff, and snus. E-cigarette queries were 75% (95% CI 70% to 80%) higher than expected 8 weeks before and after the tax, followed by RYO 59% (95% CI 53% to 65%), snus 34% (95% CI 31% to 37%), chewing tobacco 17% (95% CI 15% to 20%), cigarillos 14% (95% CI 11% to 17%), and snuff 13% (95% CI 10% to 14%). Unique queries increasing the most were 'ryo cigarettes' 427% (95% CI 308% to 534%), 'ryo tobacco' 348% (95% CI 300% to 391%), 'best electronic cigarette' 221% (95% CI 185% to 257%), and 'e-cigarette' 205% (95% CI 163% to 245%). The 2009 tobacco tax increase triggered large increases in consumer interest for some NTP, particularly e-cigarettes and RYO tobacco. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

A similarity-based data warehousing environment for medical images.

PubMed

Teixeira, Jefferson William; Annibal, Luana Peixoto; Felipe, Joaquim Cezar; Ciferri, Ricardo Rodrigues; Ciferri, Cristina Dutra de Aguiar

2015-11-01

A core issue of the decision-making process in the medical field is to support the execution of analytical (OLAP) similarity queries over images in data warehousing environments. In this paper, we focus on this issue. We propose imageDWE, a non-conventional data warehousing environment that enables the storage of intrinsic features taken from medical images in a data warehouse and supports OLAP similarity queries over them. To comply with this goal, we introduce the concept of perceptual layer, which is an abstraction used to represent an image dataset according to a given feature descriptor in order to enable similarity search. Based on this concept, we propose the imageDW, an extended data warehouse with dimension tables specifically designed to support one or more perceptual layers. We also detail how to build an imageDW and how to load image data into it. Furthermore, we show how to process OLAP similarity queries composed of a conventional predicate and a similarity search predicate that encompasses the specification of one or more perceptual layers. Moreover, we introduce an index technique to improve the OLAP query processing over images. We carried out performance tests over a data warehouse environment that consolidated medical images from exams of several modalities. The results demonstrated the feasibility and efficiency of our proposed imageDWE to manage images and to process OLAP similarity queries. The results also demonstrated that the use of the proposed index technique guaranteed a great improvement in query processing. Copyright © 2015 Elsevier Ltd. All rights reserved.

Rule-based deduplication of article records from bibliographic databases.

PubMed

Jiang, Yu; Lin, Can; Meng, Weiyi; Yu, Clement; Cohen, Aaron M; Smalheiser, Neil R

2014-01-01

We recently designed and deployed a metasearch engine, Metta, that sends queries and retrieves search results from five leading biomedical databases: PubMed, EMBASE, CINAHL, PsycINFO and the Cochrane Central Register of Controlled Trials. Because many articles are indexed in more than one of these databases, it is desirable to deduplicate the retrieved article records. This is not a trivial problem because data fields contain a lot of missing and erroneous entries, and because certain types of information are recorded differently (and inconsistently) in the different databases. The present report describes our rule-based method for deduplicating article records across databases and includes an open-source script module that can be deployed freely. Metta was designed to satisfy the particular needs of people who are writing systematic reviews in evidence-based medicine. These users want the highest possible recall in retrieval, so it is important to err on the side of not deduplicating any records that refer to distinct articles, and it is important to perform deduplication online in real time. Our deduplication module is designed with these constraints in mind. Articles that share the same publication year are compared sequentially on parameters including PubMed ID number, digital object identifier, journal name, article title and author list, using text approximation techniques. In a review of Metta searches carried out by public users, we found that the deduplication module was more effective at identifying duplicates than EndNote without making any erroneous assignments.

Rule-based deduplication of article records from bibliographic databases

PubMed Central

Jiang, Yu; Lin, Can; Meng, Weiyi; Yu, Clement; Cohen, Aaron M.; Smalheiser, Neil R.

2014-01-01

We recently designed and deployed a metasearch engine, Metta, that sends queries and retrieves search results from five leading biomedical databases: PubMed, EMBASE, CINAHL, PsycINFO and the Cochrane Central Register of Controlled Trials. Because many articles are indexed in more than one of these databases, it is desirable to deduplicate the retrieved article records. This is not a trivial problem because data fields contain a lot of missing and erroneous entries, and because certain types of information are recorded differently (and inconsistently) in the different databases. The present report describes our rule-based method for deduplicating article records across databases and includes an open-source script module that can be deployed freely. Metta was designed to satisfy the particular needs of people who are writing systematic reviews in evidence-based medicine. These users want the highest possible recall in retrieval, so it is important to err on the side of not deduplicating any records that refer to distinct articles, and it is important to perform deduplication online in real time. Our deduplication module is designed with these constraints in mind. Articles that share the same publication year are compared sequentially on parameters including PubMed ID number, digital object identifier, journal name, article title and author list, using text approximation techniques. In a review of Metta searches carried out by public users, we found that the deduplication module was more effective at identifying duplicates than EndNote without making any erroneous assignments. PMID:24434031

Fragger: a protein fragment picker for structural queries.

PubMed

Berenger, Francois; Simoncini, David; Voet, Arnout; Shrestha, Rojan; Zhang, Kam Y J

2017-01-01

Protein modeling and design activities often require querying the Protein Data Bank (PDB) with a structural fragment, possibly containing gaps. For some applications, it is preferable to work on a specific subset of the PDB or with unpublished structures. These requirements, along with specific user needs, motivated the creation of a new software to manage and query 3D protein fragments. Fragger is a protein fragment picker that allows protein fragment databases to be created and queried. All fragment lengths are supported and any set of PDB files can be used to create a database. Fragger can efficiently search a fragment database with a query fragment and a distance threshold. Matching fragments are ranked by distance to the query. The query fragment can have structural gaps and the allowed amino acid sequences matching a query can be constrained via a regular expression of one-letter amino acid codes. Fragger also incorporates a tool to compute the backbone RMSD of one versus many fragments in high throughput. Fragger should be useful for protein design, loop grafting and related structural bioinformatics tasks.

A web-based data-querying tool based on ontology-driven methodology and flowchart-based model.

PubMed

Ping, Xiao-Ou; Chung, Yufang; Tseng, Yi-Ju; Liang, Ja-Der; Yang, Pei-Ming; Huang, Guan-Tarn; Lai, Feipei

2013-10-08

Because of the increased adoption rate of electronic medical record (EMR) systems, more health care records have been increasingly accumulating in clinical data repositories. Therefore, querying the data stored in these repositories is crucial for retrieving the knowledge from such large volumes of clinical data. The aim of this study is to develop a Web-based approach for enriching the capabilities of the data-querying system along the three following considerations: (1) the interface design used for query formulation, (2) the representation of query results, and (3) the models used for formulating query criteria. The Guideline Interchange Format version 3.5 (GLIF3.5), an ontology-driven clinical guideline representation language, was used for formulating the query tasks based on the GLIF3.5 flowchart in the Protégé environment. The flowchart-based data-querying model (FBDQM) query execution engine was developed and implemented for executing queries and presenting the results through a visual and graphical interface. To examine a broad variety of patient data, the clinical data generator was implemented to automatically generate the clinical data in the repository, and the generated data, thereby, were employed to evaluate the system. The accuracy and time performance of the system for three medical query tasks relevant to liver cancer were evaluated based on the clinical data generator in the experiments with varying numbers of patients. In this study, a prototype system was developed to test the feasibility of applying a methodology for building a query execution engine using FBDQMs by formulating query tasks using the existing GLIF. The FBDQM-based query execution engine was used to successfully retrieve the clinical data based on the query tasks formatted using the GLIF3.5 in the experiments with varying numbers of patients. The accuracy of the three queries (ie, "degree of liver damage," "degree of liver damage when applying a mutually exclusive setting," and "treatments for liver cancer") was 100% for all four experiments (10 patients, 100 patients, 1000 patients, and 10,000 patients). Among the three measured query phases, (1) structured query language operations, (2) criteria verification, and (3) other, the first two had the longest execution time. The ontology-driven FBDQM-based approach enriched the capabilities of the data-querying system. The adoption of the GLIF3.5 increased the potential for interoperability, shareability, and reusability of the query tasks.

Web-search trends shed light on the nature of lunacy: relationship between moon phases and epilepsy information-seeking behavior.

PubMed

Otte, Willem M; van Diessen, Eric; Bell, Gail S; Sander, Josemir W

2013-12-01

In old and modern times and across cultures, recurrent seizures have been attributed to the lunar phase. It is unclear whether this relationship should be classified as a myth or whether a true connection exists between moon phases and seizures. We analyzed the worldwide aggregated search queries related to epilepsy health-seeking behavior between 2005 and 2012. Epilepsy-related Internet searches increased in periods with a high moon illumination. The overall association was weak (r=0.11, 95% confidence interval: 0.07 to 0.14) but seems to be higher than most control search queries not related to epilepsy. Increased sleep deprivation during periods of full moon might explain this positive association and warrants further study into epilepsy-related health-seeking behavior on the Internet, the lunar phase, and its contribution to nocturnal luminance. © 2013.

The lexeme hypotheses: Their use to generate highly grammatical and completely computerized medical records.

PubMed

Macfarlane, Donald

2016-07-01

Medical records often contain free text created by harried clinicians. Free text often contains errors which make it an unsuitable target for computerized data extraction. The cost of healthcare can be reduced by creating medical records that are fully computerized at their inception. We examine hypotheses that enable us to construct such records. We regard the text of the medical record as being an ordered collection of meaningful fragments. The intellectual content (or "lexeme") of each text fragment in the record is considered separately from the language that used to express it. We further consider that each lexeme exists as a combination of a lexeme query (defining the issue being addressed) and a lexeme response to that query. The medical record can then be perceived as a stream of these responses. The responses can be expressed in any style or language, including computer code. Examining medical records in this light gives rise to a number of observations and hypotheses. The physical location and nature of the medical episode (which we term "context") determines the general layout of the record. The order that lexeme-queries are addressed in within the record is highly consistent ("coherence"). Issues are only addressed if they are logically called-for by the context or by a previously-selected lexeme response ("predicance"), and only to a needed depth of detail ("level"). We hypothesize that all of the lexeme queries required to write any clinical notes can be stored in a large database ("lexicon") in coherence order, wherein each lexeme query is associated with its own collection of lexeme responses. We hypothesize that the issue a note-writer will need to address next is identifiable purely by using the rules of coherence, level and predicance. We have tested these hypotheses with a computer program which repeatedly offers the user a menu of lexeme responses with associated text. On selection, the program issues the text fragment, and its corresponding computer code, to output files. The program then uses coherence, predicance and level to navigate to the next appropriate lexeme query for presentation to the user. The net result is that the user creates a grammatically correct and completely computerized note at the time of its inception. The value of this approach and its practical implementation to create medical records are discussed. In our work so far, the hypotheses appear not to be false, but further testing is needed using a larger lexicon to establish their robustness in actual clinical practice. Copyright © 2016 Elsevier Ltd. All rights reserved.

Know your market: use of online query tools to quantify trends in patient information-seeking behavior for varicose vein treatment.

PubMed

Harsha, Asheesh K; Schmitt, J Eric; Stavropoulos, S William

2014-01-01

To analyze Internet search data to characterize the temporal and geographic interest of Internet users in the United States in varicose vein treatment. From January 1, 2004, to September 1, 2012, the Google Trends tool was used to analyze query data for "varicose vein treatment" to identify individuals seeking treatment information for varicose veins. The term "varicose vein treatment" returned a search volume index (SVI), representing the search frequency relative to the total search volume during a specific time interval and region. Linear regression analysis and Kruskal-Wallis one-way analysis of variance were employed to characterize search results. Search traffic for varicose vein treatment increased by 520% over the 104-month study period. There was an annual mean increase of 28% (range, -18%-100%; standard deviation [SD], 35%), with a statistically significant linear increase in average yearly SVI over time (R(2) = 0.94, P < .0001). All years showed positive growth in mean SVI except for 2008 (18% decrease). There were statistically significant differences in SVI by month (Kruskal-Wallis, P < .0001) with significantly higher mean SVI compared with other months in May (190% increase; range, 26%-670%; SD, 15%) and June (209% increase; range, 35%-700%; SD, 20%). The southern United States showed significantly higher search traffic than all other regions (Tukey-Kramer, P < .00001). There have been significant increases in Internet search traffic related to varicose vein treatment in the past 8 years. Reflected in this trend is an annual peak in search traffic in the late spring months with an overall geographic bias toward southern states. Rigorous analysis of Internet search queries for medical procedures may prove useful to guide the efficient use of limited resources and marketing dollars. © 2013 The Society of Interventional Radiology Published by SIR All rights reserved.

Software for Studying and Enhancing Educational Uses of Geospatial Semantics and Data

ERIC Educational Resources Information Center

Nodenot, Thierry; Sallaberry, Christian; Gaio, Mauro

2010-01-01

Geographically related queries form nearly one-fifth of all queries submitted to the Excite search engine and the most frequently occurring terms are names of places. This paper focuses on digital libraries and extends the basic services of existing library management systems to include new ones that are dedicated to geographic information…

Finding Relevant Data in a Sea of Languages

DTIC Science & Technology

2016-04-26

full machine-translated text , unbiased word clouds , query-biased word clouds , and query-biased sentence...and information retrieval to automate language processing tasks so that the limited number of linguists available for analyzing text and spoken...the crime (stock market). The Cross-LAnguage Search Engine (CLASE) has already preprocessed the documents, extracting text to identify the language

SeqWare Query Engine: storing and searching sequence data in the cloud.

PubMed

O'Connor, Brian D; Merriman, Barry; Nelson, Stanley F

2010-12-21

Since the introduction of next-generation DNA sequencers the rapid increase in sequencer throughput, and associated drop in costs, has resulted in more than a dozen human genomes being resequenced over the last few years. These efforts are merely a prelude for a future in which genome resequencing will be commonplace for both biomedical research and clinical applications. The dramatic increase in sequencer output strains all facets of computational infrastructure, especially databases and query interfaces. The advent of cloud computing, and a variety of powerful tools designed to process petascale datasets, provide a compelling solution to these ever increasing demands. In this work, we present the SeqWare Query Engine which has been created using modern cloud computing technologies and designed to support databasing information from thousands of genomes. Our backend implementation was built using the highly scalable, NoSQL HBase database from the Hadoop project. We also created a web-based frontend that provides both a programmatic and interactive query interface and integrates with widely used genome browsers and tools. Using the query engine, users can load and query variants (SNVs, indels, translocations, etc) with a rich level of annotations including coverage and functional consequences. As a proof of concept we loaded several whole genome datasets including the U87MG cell line. We also used a glioblastoma multiforme tumor/normal pair to both profile performance and provide an example of using the Hadoop MapReduce framework within the query engine. This software is open source and freely available from the SeqWare project (http://seqware.sourceforge.net). The SeqWare Query Engine provided an easy way to make the U87MG genome accessible to programmers and non-programmers alike. This enabled a faster and more open exploration of results, quicker tuning of parameters for heuristic variant calling filters, and a common data interface to simplify development of analytical tools. The range of data types supported, the ease of querying and integrating with existing tools, and the robust scalability of the underlying cloud-based technologies make SeqWare Query Engine a nature fit for storing and searching ever-growing genome sequence datasets.

SeqWare Query Engine: storing and searching sequence data in the cloud

PubMed Central

2010-01-01

Background Since the introduction of next-generation DNA sequencers the rapid increase in sequencer throughput, and associated drop in costs, has resulted in more than a dozen human genomes being resequenced over the last few years. These efforts are merely a prelude for a future in which genome resequencing will be commonplace for both biomedical research and clinical applications. The dramatic increase in sequencer output strains all facets of computational infrastructure, especially databases and query interfaces. The advent of cloud computing, and a variety of powerful tools designed to process petascale datasets, provide a compelling solution to these ever increasing demands. Results In this work, we present the SeqWare Query Engine which has been created using modern cloud computing technologies and designed to support databasing information from thousands of genomes. Our backend implementation was built using the highly scalable, NoSQL HBase database from the Hadoop project. We also created a web-based frontend that provides both a programmatic and interactive query interface and integrates with widely used genome browsers and tools. Using the query engine, users can load and query variants (SNVs, indels, translocations, etc) with a rich level of annotations including coverage and functional consequences. As a proof of concept we loaded several whole genome datasets including the U87MG cell line. We also used a glioblastoma multiforme tumor/normal pair to both profile performance and provide an example of using the Hadoop MapReduce framework within the query engine. This software is open source and freely available from the SeqWare project (http://seqware.sourceforge.net). Conclusions The SeqWare Query Engine provided an easy way to make the U87MG genome accessible to programmers and non-programmers alike. This enabled a faster and more open exploration of results, quicker tuning of parameters for heuristic variant calling filters, and a common data interface to simplify development of analytical tools. The range of data types supported, the ease of querying and integrating with existing tools, and the robust scalability of the underlying cloud-based technologies make SeqWare Query Engine a nature fit for storing and searching ever-growing genome sequence datasets. PMID:21210981

Quantum Private Queries

NASA Astrophysics Data System (ADS)

Giovannetti, Vittorio; Lloyd, Seth; Maccone, Lorenzo

2008-06-01

We propose a cheat sensitive quantum protocol to perform a private search on a classical database which is efficient in terms of communication complexity. It allows a user to retrieve an item from the database provider without revealing which item he or she retrieved: if the provider tries to obtain information on the query, the person querying the database can find it out. The protocol ensures also perfect data privacy of the database: the information that the user can retrieve in a single query is bounded and does not depend on the size of the database. With respect to the known (quantum and classical) strategies for private information retrieval, our protocol displays an exponential reduction in communication complexity and in running-time computational complexity.

Querying databases of trajectories of differential equations: Data structures for trajectories

NASA Technical Reports Server (NTRS)

Grossman, Robert

1989-01-01

One approach to qualitative reasoning about dynamical systems is to extract qualitative information by searching or making queries on databases containing very large numbers of trajectories. The efficiency of such queries depends crucially upon finding an appropriate data structure for trajectories of dynamical systems. Suppose that a large number of parameterized trajectories gamma of a dynamical system evolving in R sup N are stored in a database. Let Eta is contained in set R sup N denote a parameterized path in Euclidean Space, and let the Euclidean Norm denote a norm on the space of paths. A data structure is defined to represent trajectories of dynamical systems, and an algorithm is sketched which answers queries.

Balancing Efficiency and Effectiveness for Fusion-Based Search Engines in the "Big Data" Environment

ERIC Educational Resources Information Center

Li, Jieyu; Huang, Chunlan; Wang, Xiuhong; Wu, Shengli

2016-01-01

Introduction: In the big data age, we have to deal with a tremendous amount of information, which can be collected from various types of sources. For information search systems such as Web search engines or online digital libraries, the collection of documents becomes larger and larger. For some queries, an information search system needs to…

Research on Agriculture Domain Meta-Search Engine System

NASA Astrophysics Data System (ADS)

Xie, Nengfu; Wang, Wensheng

The rapid growth of agriculture web information brings a fact that search engine can not return a satisfied result for users’ queries. In this paper, we propose an agriculture domain search engine system, called ADSE, that can obtains results by an advance interface to several searches and aggregates them. We also discuss two key technologies: agriculture information determination and engine.

Predicting consumer behavior with Web search.

PubMed

Goel, Sharad; Hofman, Jake M; Lahaie, Sébastien; Pennock, David M; Watts, Duncan J

2010-10-12

Recent work has demonstrated that Web search volume can "predict the present," meaning that it can be used to accurately track outcomes such as unemployment levels, auto and home sales, and disease prevalence in near real time. Here we show that what consumers are searching for online can also predict their collective future behavior days or even weeks in advance. Specifically we use search query volume to forecast the opening weekend box-office revenue for feature films, first-month sales of video games, and the rank of songs on the Billboard Hot 100 chart, finding in all cases that search counts are highly predictive of future outcomes. We also find that search counts generally boost the performance of baseline models fit on other publicly available data, where the boost varies from modest to dramatic, depending on the application in question. Finally, we reexamine previous work on tracking flu trends and show that, perhaps surprisingly, the utility of search data relative to a simple autoregressive model is modest. We conclude that in the absence of other data sources, or where small improvements in predictive performance are material, search queries provide a useful guide to the near future.

Predicting consumer behavior with Web search

PubMed Central

Goel, Sharad; Hofman, Jake M.; Lahaie, Sébastien; Pennock, David M.; Watts, Duncan J.

2010-01-01

Recent work has demonstrated that Web search volume can “predict the present,” meaning that it can be used to accurately track outcomes such as unemployment levels, auto and home sales, and disease prevalence in near real time. Here we show that what consumers are searching for online can also predict their collective future behavior days or even weeks in advance. Specifically we use search query volume to forecast the opening weekend box-office revenue for feature films, first-month sales of video games, and the rank of songs on the Billboard Hot 100 chart, finding in all cases that search counts are highly predictive of future outcomes. We also find that search counts generally boost the performance of baseline models fit on other publicly available data, where the boost varies from modest to dramatic, depending on the application in question. Finally, we reexamine previous work on tracking flu trends and show that, perhaps surprisingly, the utility of search data relative to a simple autoregressive model is modest. We conclude that in the absence of other data sources, or where small improvements in predictive performance are material, search queries provide a useful guide to the near future. PMID:20876140

Supporting inter-topic entity search for biomedical Linked Data based on heterogeneous relationships.

PubMed

Zong, Nansu; Lee, Sungin; Ahn, Jinhyun; Kim, Hong-Gee

2017-08-01

The keyword-based entity search restricts search space based on the preference of search. When given keywords and preferences are not related to the same biomedical topic, existing biomedical Linked Data search engines fail to deliver satisfactory results. This research aims to tackle this issue by supporting an inter-topic search-improving search with inputs, keywords and preferences, under different topics. This study developed an effective algorithm in which the relations between biomedical entities were used in tandem with a keyword-based entity search, Siren. The algorithm, PERank, which is an adaptation of Personalized PageRank (PPR), uses a pair of input: (1) search preferences, and (2) entities from a keyword-based entity search with a keyword query, to formalize the search results on-the-fly based on the index of the precomputed Individual Personalized PageRank Vectors (IPPVs). Our experiments were performed over ten linked life datasets for two query sets, one with keyword-preference topic correspondence (intra-topic search), and the other without (inter-topic search). The experiments showed that the proposed method achieved better search results, for example a 14% increase in precision for the inter-topic search than the baseline keyword-based search engine. The proposed method improved the keyword-based biomedical entity search by supporting the inter-topic search without affecting the intra-topic search based on the relations between different entities. Copyright © 2017 Elsevier Ltd. All rights reserved.

Turning Search into Knowledge Management.

ERIC Educational Resources Information Center

Kaufman, David

2002-01-01

Discussion of knowledge management for electronic data focuses on creating a high quality similarity ranking algorithm. Topics include similarity ranking and unstructured data management; searching, categorization, and summarization of documents; query evaluation; considering sentences in addition to keywords; and vector models. (LRW)

MDPHnet: Secure, Distributed Sharing of Electronic Health Record Data for Public Health Surveillance, Evaluation, and Planning

PubMed Central

Vogel, Joshua; Brown, Jeffrey S.; Land, Thomas; Platt, Richard

2014-01-01

Electronic health record systems contain clinically detailed data from large populations of patients that could significantly enrich public health surveillance. Clinical practices’ security, privacy, and proprietary concerns, however, have limited their willingness to share these data with public health agencies. We describe a novel distributed network for public health surveillance called MDPHnet. The system allows the Massachusetts Department of Public Health (MDPH) to initiate custom queries against participating practices’ electronic health records while the data remain behind each practice’s firewall. Practices can review proposed queries before execution and approve query results before releasing them to the health department. MDPH is using the system for routine surveillance for priority conditions and to evaluate the impact of public health interventions. PMID:25322301

Chandra Source Catalog: User Interfaces

NASA Astrophysics Data System (ADS)

Bonaventura, Nina; Evans, I. N.; Harbo, P. N.; Rots, A. H.; Tibbetts, M. S.; Van Stone, D. W.; Zografou, P.; Anderson, C. S.; Chen, J. C.; Davis, J. E.; Doe, S. M.; Evans, J. D.; Fabbiano, G.; Galle, E.; Gibbs, D. G.; Glotfelty, K. J.; Grier, J. D.; Hain, R.; Hall, D. M.; He, X.; Houck, J. C.; Karovska, M.; Lauer, J.; McCollough, M. L.; McDowell, J. C.; Miller, J. B.; Mitschang, A. W.; Morgan, D. L.; Nichols, J. S.; Nowak, M. A.; Plummer, D. A.; Primini, F. A.; Refsdal, B. L.; Siemiginowska, A. L.; Sundheim, B. A.; Winkelman, S. L.

2010-03-01

The CSCview data mining interface is available for browsing the Chandra Source Catalog (CSC) and downloading tables of quality-assured source properties and data products. Once the desired source properties and search criteria are entered into the CSCview query form, the resulting source matches are returned in a table along with the values of the requested source properties for each source. (The catalog can be searched on any source property, not just position.) At this point, the table of search results may be saved to a text file, and the available data products for each source may be downloaded. CSCview save files are output in RDB-like and VOTable format. The available CSC data products include event files, spectra, lightcurves, and images, all of which are processed with the CIAO software. CSC data may also be accessed non-interactively with Unix command-line tools such as cURL and Wget, using ADQL 2.0 query syntax. In fact, CSCview features a separate ADQL query form for those who wish to specify this type of query within the GUI. Several interfaces are available for learning if a source is included in the catalog (in addition to CSCview): 1) the CSC interface to Sky in Google Earth shows the footprint of each Chandra observation on the sky, along with the CSC footprint for comparison (CSC source properties are also accessible when a source within a Chandra field-of-view is clicked); 2) the CSC Limiting Sensitivity online tool indicates if a source at an input celestial location was too faint for detection; 3) an IVOA Simple Cone Search interface locates all CSC sources within a specified radius of an R.A. and Dec.; and 4) the CSC-SDSS cross-match service returns the list of sources common to the CSC and SDSS, either all such sources or a subset based on search criteria.

Fixing Dataset Search

NASA Technical Reports Server (NTRS)

Lynnes, Chris

2014-01-01

Three current search engines are queried for ozone data at the GES DISC. The results range from sub-optimal to counter-intuitive. We propose a method to fix dataset search by implementing a robust relevancy ranking scheme. The relevancy ranking scheme is based on several heuristics culled from more than 20 years of helping users select datasets.

Development and Evaluation of Thesauri-Based Bibliographic Biomedical Search Engine

ERIC Educational Resources Information Center

Alghoson, Abdullah

2017-01-01

Due to the large volume and exponential growth of biomedical documents (e.g., books, journal articles), it has become increasingly challenging for biomedical search engines to retrieve relevant documents based on users' search queries. Part of the challenge is the matching mechanism of free-text indexing that performs matching based on…

Features: Real-Time Adaptive Feature and Document Learning for Web Search.

ERIC Educational Resources Information Center

Chen, Zhixiang; Meng, Xiannong; Fowler, Richard H.; Zhu, Binhai

2001-01-01

Describes Features, an intelligent Web search engine that is able to perform real-time adaptive feature (i.e., keyword) and document learning. Explains how Features learns from users' document relevance feedback and automatically extracts and suggests indexing keywords relevant to a search query, and learns from users' keyword relevance feedback…

Ephemeral Relevance and User Activities in a Search Session

ERIC Educational Resources Information Center

Jiang, Jiepu

2016-01-01

We study relevance judgment and user activities in a search session. We focus on ephemeral relevance--a contextual measurement regarding the amount of useful information a searcher acquired from a clicked result at a particular time--and two primary types of search activities--query reformulation and click. The purpose of the study is both…

Meta Data Mining in Earth Remote Sensing Data Archives

NASA Astrophysics Data System (ADS)

Davis, B.; Steinwand, D.

2014-12-01

Modern search and discovery tools for satellite based remote sensing data are often catalog based and rely on query systems which use scene- (or granule-) based meta data for those queries. While these traditional catalog systems are often robust, very little has been done in the way of meta data mining to aid in the search and discovery process. The recently coined term "Big Data" can be applied in the remote sensing world's efforts to derive information from the vast data holdings of satellite based land remote sensing data. Large catalog-based search and discovery systems such as the United States Geological Survey's Earth Explorer system and the NASA Earth Observing System Data and Information System's Reverb-ECHO system provide comprehensive access to these data holdings, but do little to expose the underlying scene-based meta data. These catalog-based systems are extremely flexible, but are manually intensive and often require a high level of user expertise. Exposing scene-based meta data to external, web-based services can enable machine-driven queries to aid in the search and discovery process. Furthermore, services which expose additional scene-based content data (such as product quality information) are now available and can provide a "deeper look" into remote sensing data archives too large for efficient manual search methods. This presentation shows examples of the mining of Landsat and Aster scene-based meta data, and an experimental service using OPeNDAP to extract information from quality band from multiple granules in the MODIS archive.

Seasons, Searches, and Intentions: What The Internet Can Tell Us About The Bed Bug (Hemiptera: Cimicidae) Epidemic.

PubMed

Sentana-Lledo, Daniel; Barbu, Corentin M; Ngo, Michelle N; Wu, Yage; Sethuraman, Karthik; Levy, Michael Z

2016-01-01

The common bed bug (Cimex lectularius L.) is once again prevalent in the United States. We investigated temporal patterns in Google search queries for bed bugs and co-occurring terms, and conducted in-person surveys to explore the intentions behind searches that included those terms. Searches for "bed bugs" rose steadily through 2011 and then plateaued, suggesting that the epidemic has reached an equilibrium in the United States. However, queries including terms that survey respondents associated strongly with having bed bugs (e.g., "exterminator," "remedies") continued to climb, while terms more closely associated with informational searches (e.g., "hotels," "about") fell. Respondents' rankings of terms and nonseasonal trends in Google search volume as assessed by a cosinor model were significantly correlated (Kendall's Tau-b P = 0.015). We find no evidence from Google Trends that the bed bug epidemic in the United States has reached equilibrium. © The Authors 2015. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Indexing data cubes for content-based searches in radio astronomy

NASA Astrophysics Data System (ADS)

Araya, M.; Candia, G.; Gregorio, R.; Mendoza, M.; Solar, M.

2016-01-01

Methods for observing space have changed profoundly in the past few decades. The methods needed to detect and record astronomical objects have shifted from conventional observations in the optical range to more sophisticated methods which permit the detection of not only the shape of an object but also the velocity and frequency of emissions in the millimeter-scale wavelength range and the chemical substances from which they originate. The consolidation of radio astronomy through a range of global-scale projects such as the Very Long Baseline Array (VLBA) and the Atacama Large Millimeter/submillimeter Array (ALMA) reinforces the need to develop better methods of data processing that can automatically detect regions of interest (ROIs) within data cubes (position-position-velocity), index them and facilitate subsequent searches via methods based on queries using spatial coordinates and/or velocity ranges. In this article, we present the development of an automatic system for indexing ROIs in data cubes that is capable of automatically detecting and recording ROIs while reducing the necessary storage space. The system is able to process data cubes containing megabytes of data in fractions of a second without human supervision, thus allowing it to be incorporated into a production line for displaying objects in a virtual observatory. We conducted a set of comprehensive experiments to illustrate how our system works. As a result, an index of 3% of the input size was stored in a spatial database, representing a compression ratio equal to 33:1 over an input of 20.875 GB, achieving an index of 773 MB approximately. On the other hand, a single query can be evaluated over our system in a fraction of second, showing that the indexing step works as a shock-absorber of the computational time involved in data cube processing. The system forms part of the Chilean Virtual Observatory (ChiVO), an initiative which belongs to the International Virtual Observatory Alliance (IVOA) that seeks to provide the capability of content-based searches on data cubes to the astronomical community.

Google search behavior for status epilepticus.

PubMed

Brigo, Francesco; Trinka, Eugen

2015-08-01

Millions of people surf the Internet every day as a source of health-care information looking for materials about symptoms, diagnosis, treatments and their possible adverse effects, or diagnostic procedures. Google is the most popular search engine and is used by patients and physicians to search for online health-related information. This study aimed to evaluate changes in Google search behavior occurring in English-speaking countries over time for the term "status epilepticus" (SE). Using Google Trends, data on global search queries for the term SE between the 1st of January 2004 and 31st of December 2014 were analyzed. Search volume numbers over time (downloaded as CSV datasets) were analyzed by applying the "health" category filter. The research trends for the term SE remained fairly constant over time. The greatest search volume for the term SE was reported in the United States, followed by India, Australia, the United Kingdom, Canada, the Netherlands, Thailand, and Germany. Most terms associated with the search queries were related to SE definition, symptoms, subtypes, and treatment. The volume of searches for some queries (nonconvulsive, focal, and refractory SE; SE definition; SE guidelines; SE symptoms; SE management; SE treatment) was enormously increased over time (search popularity has exceeded a 5000% growth since 2004). Most people use search engines to look for the term SE to obtain information on its definition, subtypes, and management. The greatest search volume occurred not only in developed countries but also in developing countries where raising awareness about SE still remains a challenging task and where there is reduced public knowledge of epilepsy. Health information seeking (the extent to which people search for health information online) reflects the health-related information needs of Internet users for a specific disease. Google Trends shows that Internet users have a great demand for information concerning some aspects of SE (definition, subtypes, symptoms, treatment, and guidelines). Policy makers and neurological scientific societies have the responsibility to try to meet these information needs and to better target public information campaigns on SE to the general population. This article is part of a Special Issue entitled "Status Epilepticus". Copyright © 2015 Elsevier Inc. All rights reserved.

Facilitating Cohort Discovery by Enhancing Ontology Exploration, Query Management and Query Sharing for Large Clinical Data Repositories.

PubMed

Tao, Shiqiang; Cui, Licong; Wu, Xi; Zhang, Guo-Qiang

2017-01-01

To help researchers better access clinical data, we developed a prototype query engine called DataSphere for exploring large-scale integrated clinical data repositories. DataSphere expedites data importing using a NoSQL data management system and dynamically renders its user interface for concept-based querying tasks. DataSphere provides an interactive query-building interface together with query translation and optimization strategies, which enable users to build and execute queries effectively and efficiently. We successfully loaded a dataset of one million patients for University of Kentucky (UK) Healthcare into DataSphere with more than 300 million clinical data records. We evaluated DataSphere by comparing it with an instance of i2b2 deployed at UK Healthcare, demonstrating that DataSphere provides enhanced user experience for both query building and execution.

Facilitating Cohort Discovery by Enhancing Ontology Exploration, Query Management and Query Sharing for Large Clinical Data Repositories

PubMed Central

Tao, Shiqiang; Cui, Licong; Wu, Xi; Zhang, Guo-Qiang

2017-01-01

To help researchers better access clinical data, we developed a prototype query engine called DataSphere for exploring large-scale integrated clinical data repositories. DataSphere expedites data importing using a NoSQL data management system and dynamically renders its user interface for concept-based querying tasks. DataSphere provides an interactive query-building interface together with query translation and optimization strategies, which enable users to build and execute queries effectively and efficiently. We successfully loaded a dataset of one million patients for University of Kentucky (UK) Healthcare into DataSphere with more than 300 million clinical data records. We evaluated DataSphere by comparing it with an instance of i2b2 deployed at UK Healthcare, demonstrating that DataSphere provides enhanced user experience for both query building and execution. PMID:29854239

Capturing public interest toward new tools for controlling human immunodeficiency virus (HIV) infection exploiting data from Google Trends.

PubMed

Mahroum, Naim; Bragazzi, Nicola Luigi; Brigo, Francesco; Waknin, Roy; Sharif, Kassem; Mahagna, Hussein; Amital, Howard; Watad, Abdulla

2018-04-01

Human immunodeficiency virus vaccination and pre-exposure prophylaxis represent two different emerging preventive tools. Google Trends was used to assess the public interest toward these tools in terms of digital activities. Worldwide web searches concerning the human immunodeficiency virus vaccine represented 0.34 percent, 0.03 percent, and 46.97 percent of human immunodeficiency virus, acquired immune deficiency syndrome, and human immunodeficiency virus/acquired immune deficiency syndrome treatment-related Google Trends queries, respectively. Concerning temporal trends, digital activities were shown to increase from 0 percent as of 1 January 2004 percent to 46 percent as of 8 October 2017 with two spikes observed in May and July 2012, coinciding with the US Food and Drug Administration approval. Bursts in search number and volume were recorded as human immunodeficiency virus vaccine trials emerged. This search topic has decreased in the past decade in parallel to the increase in Truvada-related topics. Concentrated searches were noticed among African countries with high human immunodeficiency virus/acquired immune deficiency syndrome prevalence. Stakeholders should take advantage of public interest especially in preventive medicine in high disease burden countries.

GeneLab Analysis Working Group Kick-Off Meeting

NASA Technical Reports Server (NTRS)

Costes, Sylvain V.

2018-01-01

Goals to achieve for GeneLab AWG - GL vision - Review of GeneLab AWG charter Timeline and milestones for 2018 Logistics - Monthly Meeting - Workshop - Internship - ASGSR Introduction of team leads and goals of each group Introduction of all members Q/A Three-tier Client Strategy to Democratize Data Physiological changes, pathway enrichment, differential expression, normalization, processing metadata, reproducibility, Data federation/integration with heterogeneous bioinformatics external databases The GLDS currently serves over 100 omics investigations to the biomedical community via open access. In order to expand the scope of metadata record searches via the GLDS, we designed a metadata warehouse that collects and updates metadata records from external systems housing similar data. To demonstrate the capabilities of federated search and retrieval of these data, we imported metadata records from three open-access data systems into the GLDS metadata warehouse: NCBI's Gene Expression Omnibus (GEO), EBI's PRoteomics IDEntifications (PRIDE) repository, and the Metagenomics Analysis server (MG-RAST). Each of these systems defines metadata for omics data sets differently. One solution to bridge such differences is to employ a common object model (COM) to which each systems' representation of metadata can be mapped. Warehoused metadata records are then transformed at ETL to this single, common representation. Queries generated via the GLDS are then executed against the warehouse, and matching records are shown in the COM representation (Fig. 1). While this approach is relatively straightforward to implement, the volume of the data in the omics domain presents challenges in dealing with latency and currency of records. Furthermore, the lack of a coordinated has been federated data search for and retrieval of these kinds of data across other open-access systems, so that users are able to conduct biological meta-investigations using data from a variety of sources. Such meta-investigations are key to corroborating findings from many kinds of assays and translating them into systems biology knowledge and, eventually, therapeutics.

Integrating Smart Health in the US Health Care System: Infodemiology Study of Asthma Monitoring in the Google Era

PubMed Central

Sampri, Alexia; Sypsa, Karla; Tsagarakis, Konstantinos P

2018-01-01

Background With the internet’s penetration and use constantly expanding, this vast amount of information can be employed in order to better assess issues in the US health care system. Google Trends, a popular tool in big data analytics, has been widely used in the past to examine interest in various medical and health-related topics and has shown great potential in forecastings, predictions, and nowcastings. As empirical relationships between online queries and human behavior have been shown to exist, a new opportunity to explore the behavior toward asthma—a common respiratory disease—is present. Objective This study aimed at forecasting the online behavior toward asthma and examined the correlations between queries and reported cases in order to explore the possibility of nowcasting asthma prevalence in the United States using online search traffic data. Methods Applying Holt-Winters exponential smoothing to Google Trends time series from 2004 to 2015 for the term “asthma,” forecasts for online queries at state and national levels are estimated from 2016 to 2020 and validated against available Google query data from January 2016 to June 2017. Correlations among yearly Google queries and between Google queries and reported asthma cases are examined. Results Our analysis shows that search queries exhibit seasonality within each year and the relationships between each 2 years’ queries are statistically significant (P<.05). Estimated forecasting models for a 5-year period (2016 through 2020) for Google queries are robust and validated against available data from January 2016 to June 2017. Significant correlations were found between (1) online queries and National Health Interview Survey lifetime asthma (r=–.82, P=.001) and current asthma (r=–.77, P=.004) rates from 2004 to 2015 and (2) between online queries and Behavioral Risk Factor Surveillance System lifetime (r=–.78, P=.003) and current asthma (r=–.79, P=.002) rates from 2004 to 2014. The correlations are negative, but lag analysis to identify the period of response cannot be employed until short-interval data on asthma prevalence are made available. Conclusions Online behavior toward asthma can be accurately predicted, and significant correlations between online queries and reported cases exist. This method of forecasting Google queries can be used by health care officials to nowcast asthma prevalence by city, state, or nationally, subject to future availability of daily, weekly, or monthly data on reported cases. This method could therefore be used for improved monitoring and assessment of the needs surrounding the current population of patients with asthma. PMID:29530839

Diurnal Variations of Depression-Related Health Information Seeking: Case Study in Finland Using Google Trends Data

PubMed Central

Kettunen, Jyrki; Eirola, Emil; Paakkonen, Heikki

2018-01-01

Background Some of the temporal variations and clock-like rhythms that govern several different health-related behaviors can be traced in near real-time with the help of search engine data. This is especially useful when studying phenomena where little or no traditional data exist. One specific area where traditional data are incomplete is the study of diurnal mood variations, or daily changes in individuals’ overall mood state in relation to depression-like symptoms. Objective The objective of this exploratory study was to analyze diurnal variations for interest in depression on the Web to discover hourly patterns of depression interest and help seeking. Methods Hourly query volume data for 6 depression-related queries in Finland were downloaded from Google Trends in March 2017. A continuous wavelet transform (CWT) was applied to the hourly data to focus on the diurnal variation. Longer term trends and noise were also eliminated from the data to extract the diurnal variation for each query term. An analysis of variance was conducted to determine the statistical differences between the distributions of each hour. Data were also trichotomized and analyzed in 3 time blocks to make comparisons between different time periods during the day. Results Search volumes for all depression-related query terms showed a unimodal regular pattern during the 24 hours of the day. All queries feature clear peaks during the nighttime hours around 11 PM to 4 AM and troughs between 5 AM and 10 PM. In the means of the CWT-reconstructed data, the differences in nighttime and daytime interest are evident, with a difference of 37.3 percentage points (pp) for the term “Depression,” 33.5 pp for “Masennustesti,” 30.6 pp for “Masennus,” 12.8 pp for “Depression test,” 12.0 pp for “Masennus testi,” and 11.8 pp for “Masennus oireet.” The trichotomization showed peaks in the first time block (00.00 AM-7.59 AM) for all 6 terms. The search volumes then decreased significantly during the second time block (8.00 AM-3.59 PM) for the terms “Masennus oireet” (P<.001), “Masennus” (P=.001), “Depression” (P=.005), and “Depression test” (P=.004). Higher search volumes for the terms “Masennus” (P=.14), “Masennustesti” (P=.07), and “Depression test” (P=.10) were present between the second and third time blocks. Conclusions Help seeking for depression has clear diurnal patterns, with significant rise in depression-related query volumes toward the evening and night. Thus, search engine query data support the notion of the evening-worse pattern in diurnal mood variation. Information on the timely nature of depression-related interest on an hourly level could improve the chances for early intervention, which is beneficial for positive health outcomes. PMID:29792291

Diurnal Variations of Depression-Related Health Information Seeking: Case Study in Finland Using Google Trends Data.

PubMed

Tana, Jonas Christoffer; Kettunen, Jyrki; Eirola, Emil; Paakkonen, Heikki

2018-05-23

Some of the temporal variations and clock-like rhythms that govern several different health-related behaviors can be traced in near real-time with the help of search engine data. This is especially useful when studying phenomena where little or no traditional data exist. One specific area where traditional data are incomplete is the study of diurnal mood variations, or daily changes in individuals' overall mood state in relation to depression-like symptoms. The objective of this exploratory study was to analyze diurnal variations for interest in depression on the Web to discover hourly patterns of depression interest and help seeking. Hourly query volume data for 6 depression-related queries in Finland were downloaded from Google Trends in March 2017. A continuous wavelet transform (CWT) was applied to the hourly data to focus on the diurnal variation. Longer term trends and noise were also eliminated from the data to extract the diurnal variation for each query term. An analysis of variance was conducted to determine the statistical differences between the distributions of each hour. Data were also trichotomized and analyzed in 3 time blocks to make comparisons between different time periods during the day. Search volumes for all depression-related query terms showed a unimodal regular pattern during the 24 hours of the day. All queries feature clear peaks during the nighttime hours around 11 PM to 4 AM and troughs between 5 AM and 10 PM. In the means of the CWT-reconstructed data, the differences in nighttime and daytime interest are evident, with a difference of 37.3 percentage points (pp) for the term "Depression," 33.5 pp for "Masennustesti," 30.6 pp for "Masennus," 12.8 pp for "Depression test," 12.0 pp for "Masennus testi," and 11.8 pp for "Masennus oireet." The trichotomization showed peaks in the first time block (00.00 AM-7.59 AM) for all 6 terms. The search volumes then decreased significantly during the second time block (8.00 AM-3.59 PM) for the terms "Masennus oireet" (P<.001), "Masennus" (P=.001), "Depression" (P=.005), and "Depression test" (P=.004). Higher search volumes for the terms "Masennus" (P=.14), "Masennustesti" (P=.07), and "Depression test" (P=.10) were present between the second and third time blocks. Help seeking for depression has clear diurnal patterns, with significant rise in depression-related query volumes toward the evening and night. Thus, search engine query data support the notion of the evening-worse pattern in diurnal mood variation. Information on the timely nature of depression-related interest on an hourly level could improve the chances for early intervention, which is beneficial for positive health outcomes. ©Jonas Christoffer Tana, Jyrki Kettunen, Emil Eirola, Heikki Paakkonen. Originally published in JMIR Mental Health (http://mental.jmir.org), 23.05.2018.

A relational database in neurosurgery.

PubMed

Sicurello, F; Marchetti, M R; Cazzaniga, P

1995-01-01

This paper describes teh automatic procedure for a clinical record management in a Neurosurgery ward. The automated record allows the storage, querying and effective management of clinical data. This is useful during the patient stay and also for data processing and analysis aiming at clinical research and statistical studies. The clinical record is problem-oriented. It contains a minimum data set regarding every patient and a data set which is defined by a classification nomenclature (using an inner protocol). The main parts of the clinical record are the following tables: PERSONAL DATA: contains the fields relating to personal and admission data of the patient. The compilation of some fields is compulsory because they serve as input for the automated discharge letter. This table is used as an identifier for patient retrieval. composed of five different tables according to the kind of data. They are: familiar anamnesis, physiological anamnesis, past and next pathology anamnesis, and trauma anamnesis. GENERAL OBJECTIVITY: contains the general physical information of a patient. The field hold default values, which quickens the compilation and assures the recording of normal values. NEUROLOGICAL EXAMINATION: contains information about the neurological status of the patient. Also in this table, ther are default values in the fields. COMA: contains standardized ata and classifications. The multiple choices are automated and driven and belong to homogeneous classes. SURGICAL OPERATIONS: the information recording is made defining the general kind of operation and then defining the peculiar kind of operation. INSTRUMENTAL EXAMINATIONS: some examination results are recorded in a free structure, while other ones (TAC, etc.) follow codified structure. In order to identify a pathology by means of TAC, it is enough to record three values corresponding to three variables. THis classification fully describes a lot of neurosurgical pathologies. DISCHARGE: contains conclusions, therapies, result, and hospital course. Medical language is closer to the natural one and presents some abiguities. In order to solve this problem, a classification nomenclature was used for diagnosis definition. DISCHARGE LETTER: the document given to the patient when he is discharged. It extracts data from the previously described modules and contains standard headings. The information stored int he database is structured (e.g., diagnosis, name, surname, etc.) and access to this data takes place when the user wants to search the database, using particular queries where the identifying data of a patient is put as conditions for the research (SELECT age, name WHERE diagnosis="TRAUMA"). Logical operators and relational algebra of the relational DBMS allows more complex queries ((diagnosis="TRAUMA" AND age="19") OR sex="M"). The queries are deterministic, because data management uses a classification nomenclature. Data retrieval takes place through a matching, and the DBMS answers directly to the queries. The information retrieval speed depends upon the kind of system that is used; in our case retrieval time is low because the accesses to disk are few even for big databases. In medicine, clinical records can have a hierarchical structure and/or a relational one. Nevertheless, the hierarchical model presents a disadvantage: it is not very flexible because it is linked to a pre-defined structure; as a matter of fact, the definition of path is established in the beginning and not during the execution. Thus, a better representation of the system at a logical level requries a relational DBMS which exploits the relationships between entities in a vertical and horizontal way. That is why the developers adopted a mixed strategy which exploits the advantages of both models and which is provided by M Technology with SQL language (M/SQL). For the future, it is important to have at one's disposal multimedia technologies, which integrate different kinds of information (alp

Environmental Mission Impact Assessment

DTIC Science & Technology

2008-01-01

System Agency’s (DISA) Federated Search service. The mission impacts can be generated for a general rectangular area, or generated for routes, route...that respond to queries (format- ted according to DISA’s Federated Search specifi- FIGURE 2 EVIS service-oriented architecture design, illustrating the

Privacy Perspectives for Online Searchers: Confidentiality with Confidence?

ERIC Educational Resources Information Center

Duberman, Josh; Beaudet, Michael

2000-01-01

Presents issues and questions involved in online privacy from the information professional's perspective. Topics include consumer concerns; query confidentiality; securing computers from intrusion; electronic mail; search engines; patents and intellectual property searches; government's role; Internet service providers; database mining; user…

A searching and reporting system for relational databases using a graph-based metadata representation.

PubMed

Hewitt, Robin; Gobbi, Alberto; Lee, Man-Ling

2005-01-01

Relational databases are the current standard for storing and retrieving data in the pharmaceutical and biotech industries. However, retrieving data from a relational database requires specialized knowledge of the database schema and of the SQL query language. At Anadys, we have developed an easy-to-use system for searching and reporting data in a relational database to support our drug discovery project teams. This system is fast and flexible and allows users to access all data without having to write SQL queries. This paper presents the hierarchical, graph-based metadata representation and SQL-construction methods that, together, are the basis of this system's capabilities.

Increasing Scalability of Researcher Network Extraction from the Web

NASA Astrophysics Data System (ADS)

Asada, Yohei; Matsuo, Yutaka; Ishizuka, Mitsuru

Social networks, which describe relations among people or organizations as a network, have recently attracted attention. With the help of a social network, we can analyze the structure of a community and thereby promote efficient communications within it. We investigate the problem of extracting a network of researchers from the Web, to assist efficient cooperation among researchers. Our method uses a search engine to get the cooccurences of names of two researchers and calculates the streangth of the relation between them. Then we label the relation by analyzing the Web pages in which these two names cooccur. Research on social network extraction using search engines as ours, is attracting attention in Japan as well as abroad. However, the former approaches issue too many queries to search engines to extract a large-scale network. In this paper, we propose a method to filter superfluous queries and facilitates the extraction of large-scale networks. By this method we are able to extract a network of around 3000-nodes. Our experimental results show that the proposed method reduces the number of queries significantly while preserving the quality of the network as compared to former methods.

Recommending images of user interests from the biomedical literature

NASA Astrophysics Data System (ADS)

Clukey, Steven; Xu, Songhua

2013-03-01

Every year hundreds of thousands of biomedical images are published in journals and conferences. Consequently, finding images relevant to one's interests becomes an ever daunting task. This vast amount of literature creates a need for intelligent and easy-to-use tools that can help researchers effectively navigate through the content corpus and conveniently locate materials of their interests. Traditionally, literature search tools allow users to query content using topic keywords. However, manual query composition is often time and energy consuming. A better system would be one that can automatically deliver relevant content to a researcher without having the end user manually manifest one's search intent and interests via search queries. Such a computer-aided assistance for information access can be provided by a system that first determines a researcher's interests automatically and then recommends images relevant to the person's interests accordingly. The technology can greatly improve a researcher's ability to stay up to date in their fields of study by allowing them to efficiently browse images and documents matching their needs and interests among the vast amount of the biomedical literature. A prototype system implementation of the technology can be accessed via http://www.smartdataware.com.

Predicting user click behaviour in search engine advertisements

NASA Astrophysics Data System (ADS)

Daryaie Zanjani, Mohammad; Khadivi, Shahram

2015-10-01

According to the specific requirements and interests of users, search engines select and display advertisements that match user needs and have higher probability of attracting users' attention based on their previous search history. New objects such as user, advertisement or query cause a deterioration of precision in targeted advertising due to their lack of history. This article surveys this challenge. In the case of new objects, we first extract similar observed objects to the new object and then we use their history as the history of new object. Similarity between objects is measured based on correlation, which is a relation between user and advertisement when the advertisement is displayed to the user. This method is used for all objects, so it has helped us to accurately select relevant advertisements for users' queries. In our proposed model, we assume that similar users behave in a similar manner. We find that users with few queries are similar to new users. We will show that correlation between users and advertisements' keywords is high. Thus, users who pay attention to advertisements' keywords, click similar advertisements. In addition, users who pay attention to specific brand names might have similar behaviours too.

Fuzzy queries above relational database

NASA Astrophysics Data System (ADS)

Smolka, Pavel; Bradac, Vladimir

2017-11-01

The aim of the theme is to introduce a possibility of fuzzy queries implemented in relational databases. The issue is described on a model which identifies the appropriate part of the problem domain for fuzzy approach. The model is demonstrated on a database of wines focused on searching in it. The construction of the database complies with the Law of the Czech Republic.

Query-Biased Preview over Outsourced and Encrypted Data

PubMed Central

Luo, Guangchun; Qin, Ke; Chen, Aiguo

2013-01-01

For both convenience and security, more and more users encrypt their sensitive data before outsourcing it to a third party such as cloud storage service. However, searching for the desired documents becomes problematic since it is costly to download and decrypt each possibly needed document to check if it contains the desired content. An informative query-biased preview feature, as applied in modern search engine, could help the users to learn about the content without downloading the entire document. However, when the data are encrypted, securely extracting a keyword-in-context snippet from the data as a preview becomes a challenge. Based on private information retrieval protocol and the core concept of searchable encryption, we propose a single-server and two-round solution to securely obtain a query-biased snippet over the encrypted data from the server. We achieve this novel result by making a document (plaintext) previewable under any cryptosystem and constructing a secure index to support dynamic computation for a best matched snippet when queried by some keywords. For each document, the scheme has O(d) storage complexity and O(log(d/s) + s + d/s) communication complexity, where d is the document size and s is the snippet length. PMID:24078798

Query-biased preview over outsourced and encrypted data.

PubMed

Peng, Ningduo; Luo, Guangchun; Qin, Ke; Chen, Aiguo

2013-01-01

For both convenience and security, more and more users encrypt their sensitive data before outsourcing it to a third party such as cloud storage service. However, searching for the desired documents becomes problematic since it is costly to download and decrypt each possibly needed document to check if it contains the desired content. An informative query-biased preview feature, as applied in modern search engine, could help the users to learn about the content without downloading the entire document. However, when the data are encrypted, securely extracting a keyword-in-context snippet from the data as a preview becomes a challenge. Based on private information retrieval protocol and the core concept of searchable encryption, we propose a single-server and two-round solution to securely obtain a query-biased snippet over the encrypted data from the server. We achieve this novel result by making a document (plaintext) previewable under any cryptosystem and constructing a secure index to support dynamic computation for a best matched snippet when queried by some keywords. For each document, the scheme has O(d) storage complexity and O(log(d/s) + s + d/s) communication complexity, where d is the document size and s is the snippet length.

CellLineNavigator: a workbench for cancer cell line analysis

PubMed Central

Krupp, Markus; Itzel, Timo; Maass, Thorsten; Hildebrandt, Andreas; Galle, Peter R.; Teufel, Andreas

2013-01-01

The CellLineNavigator database, freely available at http://www.medicalgenomics.org/celllinenavigator, is a web-based workbench for large scale comparisons of a large collection of diverse cell lines. It aims to support experimental design in the fields of genomics, systems biology and translational biomedical research. Currently, this compendium holds genome wide expression profiles of 317 different cancer cell lines, categorized into 57 different pathological states and 28 individual tissues. To enlarge the scope of CellLineNavigator, the database was furthermore closely linked to commonly used bioinformatics databases and knowledge repositories. To ensure easy data access and search ability, a simple data and an intuitive querying interface were implemented. It allows the user to explore and filter gene expression, focusing on pathological or physiological conditions. For a more complex search, the advanced query interface may be used to query for (i) differentially expressed genes; (ii) pathological or physiological conditions; or (iii) gene names or functional attributes, such as Kyoto Encyclopaedia of Genes and Genomes pathway maps. These queries may also be combined. Finally, CellLineNavigator allows additional advanced analysis of differentially regulated genes by a direct link to the Database for Annotation, Visualization and Integrated Discovery (DAVID) Bioinformatics Resources. PMID:23118487

A Novel Evaluation of World No Tobacco Day in Latin America

PubMed Central

Althouse, Benjamin M; Allem, Jon-Patrick; Ford, Daniel E; Ribisl, Kurt M; Cohen, Joanna E

2012-01-01

Background World No Tobacco Day (WNTD), commemorated annually on May 31, aims to inform the public about tobacco harms. Because tobacco control surveillance is usually annualized, the effectiveness of WNTD remains unexplored into its 25th year. Objective To explore the potential of digital surveillance (infoveillance) to evaluate the impacts of WNTD on population awareness of and interest in cessation. Methods Health-related news stories and Internet search queries were aggregated to form a continuous and real-time data stream. We monitored daily news coverage of and Internet search queries for cessation in seven Latin American nations from 2006 to 2011. Results Cessation news coverage peaked around WNTD, typically increasing 71% (95% confidence interval [CI] 61–81), ranging from 61% in Mexico to 83% in Venezuela. Queries indicative of cessation interest peaked on WNTD, increasing 40% (95% CI 32–48), ranging from 24% in Colombia to 84% in Venezuela. A doubling in cessation news coverage was associated with approximately a 50% increase in cessation queries. To gain a practical perspective, we compared WNTD-related activity with New Year’s Day and several cigarette excise tax increases in Mexico. Cessation queries around WNTD were typically greater than New Year’s Day and approximated a 2.8% (95% CI –0.8 to 6.3) increase in cigarette excise taxes. Conclusions This novel evaluation suggests WNTD had a significant impact on popular awareness (media trends) and individual interest (query trends) in smoking cessation. Because WNTD is constantly evolving, our work is also a model for real-time surveillance and potential improvement in WNTD and similar initiatives. PMID:22634568

Conceptual mapping of user's queries to medical subject headings.

PubMed Central

Zieman, Y. L.; Bleich, H. L.

1997-01-01

This paper describes a way to map users' queries to relevant Medical Subject Headings (MeSH terms) used by the National Library of Medicine to index the biomedical literature. The method, called SENSE (SEarch with New SEmantics), transforms words and phrases in the users' queries into primary conceptual components and compares these components with those of the MeSH vocabulary. Similar to the way in which most numbers can be split into numerical factors and expressed as their product--for example, 42 can be expressed as 2*21, 6*7, 3*14, 2*3*7,--so most medical concepts can be split into "semantic factors" and expressed as their juxtaposition. Note that if we split 42 into its primary factors, the breakdown is unique: 2*3*7. Similarly, when we split medical concepts into their "primary semantic factors" the breakdown is also unique. For example, the MeSH term 'renovascular hypertension' can be split morphologically into reno, vascular, hyper, and tension--morphemes that can then be translated into their primary semantic factors--kidney, blood vessel, high, and pressure. By "factoring" each MeSH term in this way, and by similarly factoring the user's query, we can match query to MeSH term by searching for combinations of common factors. Unlike UMLS and other methods that match at the level of words or phrases, SENSE matches at the level of concepts; in this way, a wide variety of words and phrases that have the same meaning produce the same match. Now used in PaperChase, the method is surprisingly powerful in matching users' queries to Medical Subject Headings. PMID:9357680

Search engines, news wires and digital epidemiology: Presumptions and facts.

PubMed

Kaveh-Yazdy, Fatemeh; Zareh-Bidoki, Ali-Mohammad

2018-07-01

Digital epidemiology tries to identify diseases dynamics and spread behaviors using digital traces collected via search engines logs and social media posts. However, the impacts of news on information-seeking behaviors have been remained unknown. Data employed in this research provided from two sources, (1) Parsijoo search engine query logs of 48 months, and (2) a set of documents of 28 months of Parsijoo's news service. Two classes of topics, i.e. macro-topics and micro-topics were selected to be tracked in query logs and news. Keywords of the macro-topics were automatically generated using web provided resources and exceeded 10k. Keyword set of micro-topics were limited to a numerable list including terms related to diseases and health-related activities. The tests are established in the form of three studies. Study A includes temporal analyses of 7 macro-topics in query logs. Study B considers analyzing seasonality of searching patterns of 9 micro-topics, and Study C assesses the impact of news media coverage on users' health-related information-seeking behaviors. Study A showed that the hourly distribution of various macro-topics followed the changes in social activity level. Conversely, the interestingness of macro-topics did not follow the regulation of topic distributions. Among macro-topics, "Pharmacotherapy" has highest interestingness level and wider time-window of popularity. In Study B, seasonality of a limited number of diseases and health-related activities were analyzed. Trends of infectious diseases, such as flu, mumps and chicken pox were seasonal. Due to seasonality of most of diseases covered in national vaccination plans, the trend belonging to "Immunization and Vaccination" was seasonal, as well. Cancer awareness events caused peaks in search trends of "Cancer" and "Screening" micro-topics in specific days of each year that mimic repeated patterns which may mistakenly be identified as seasonality. In study C, we assessed the co-integration and correlation between news and query trends. Our results demonstrated that micro-topics sparsely covered in news media had lowest level of impressiveness and, subsequently, the lowest impact on users' intents. Our results can reveal public reaction to social events, diseases and prevention procedures. Furthermore, we found that news trends are co-integrated with search queries and are able to reveal health-related events; however, they cannot be used interchangeably. It is recommended that the user-generated contents and news documents are analyzed mutually and interactively. Copyright © 2018 Elsevier B.V. All rights reserved.

Pilot Study on the Prevalence of Imposed Queries in a School Library Media Center.

ERIC Educational Resources Information Center

Gross, Melissa

1997-01-01

Discussion of information-seeking behavior focuses on a study of the imposed query, as opposed to self-generated queries, in an elementary school library media center in order to quantify its presence, to record characteristics of the users that carry them, and to identify the persons imposing them. The coding sheet is appended. Contains one table…

Case report classics in otolaryngology - head and neck surgery: citation analysis.

PubMed

Edelmayer, L W; Fenton, J E; Yellin, S A; Shearer, D J; Coelho, D H

2018-06-12

To analyse publication and citations trends of case reports within otolaryngology - head and neck surgery literature, with specific attention to the most-cited reports.Study designDatabase query. Web of Science was searched for article type 'case reports' published in the leading otolaryngology - head and neck surgery journals since 1945. Variables including publication dates, citation dates and numbers, author, author number, and others were recorded and analysed for trends. The reports with the most citations (classics) were further studied. Of nearly 67 000 published articles in leading otolaryngology - head and neck surgery journals, the overall number of case reports as a percentage of the total has substantially decreased over time. A total of 110 case report classics were identified for which citations have increased. Although the case report may not be worthy of its tarnished record, declining trends in publication suggest a limited future for this valuable research and educational resource.

Using Search Query Surveillance to Monitor Tax Avoidance and Smoking Cessation following the United States' 2009 “SCHIP” Cigarette Tax Increase

PubMed Central

Ayers, John W.; Ribisl, Kurt; Brownstein, John S.

2011-01-01

Smokers can use the web to continue or quit their habit. Online vendors sell reduced or tax-free cigarettes lowering smoking costs, while health advocates use the web to promote cessation. We examined how smokers' tax avoidance and smoking cessation Internet search queries were motivated by the United States' (US) 2009 State Children's Health Insurance Program (SCHIP) federal cigarette excise tax increase and two other state specific tax increases. Google keyword searches among residents in a taxed geography (US or US state) were compared to an untaxed geography (Canada) for two years around each tax increase. Search data were normalized to a relative search volume (RSV) scale, where the highest search proportion was labeled 100 with lesser proportions scaled by how they relatively compared to the highest proportion. Changes in RSV were estimated by comparing means during and after the tax increase to means before the tax increase, across taxed and untaxed geographies. The SCHIP tax was associated with an 11.8% (95% confidence interval [95%CI], 5.7 to 17.9; p<.001) immediate increase in cessation searches; however, searches quickly abated and approximated differences from pre-tax levels in Canada during the months after the tax. Tax avoidance searches increased 27.9% (95%CI, 15.9 to 39.9; p<.001) and 5.3% (95%CI, 3.6 to 7.1; p<.001) during and in the months after the tax compared to Canada, respectively, suggesting avoidance is the more pronounced and durable response. Trends were similar for state-specific tax increases but suggest strong interactive processes across taxes. When the SCHIP tax followed Florida's tax, versus not, it promoted more cessation and avoidance searches. Efforts to combat tax avoidance and increase cessation may be enhanced by using interventions targeted and tailored to smokers' searches. Search query surveillance is a valuable real-time, free and public method, that may be generalized to other behavioral, biological, informational or psychological outcomes manifested online. PMID:21436883

Automatic home medical product recommendation.

PubMed

Luo, Gang; Thomas, Selena B; Tang, Chunqiang

2012-04-01

Web-based personal health records (PHRs) are being widely deployed. To improve PHR's capability and usability, we proposed the concept of intelligent PHR (iPHR). In this paper, we use automatic home medical product recommendation as a concrete application to demonstrate the benefits of introducing intelligence into PHRs. In this new application domain, we develop several techniques to address the emerging challenges. Our approach uses treatment knowledge and nursing knowledge, and extends the language modeling method to (1) construct a topic-selection input interface for recommending home medical products, (2) produce a global ranking of Web pages retrieved by multiple queries, and (3) provide diverse search results. We demonstrate the effectiveness of our techniques using USMLE medical exam cases.

Visual Exploratory Search of Relationship Graphs on Smartphones

PubMed Central

Ouyang, Jianquan; Zheng, Hao; Kong, Fanbin; Liu, Tianming

2013-01-01

This paper presents a novel framework for Visual Exploratory Search of Relationship Graphs on Smartphones (VESRGS) that is composed of three major components: inference and representation of semantic relationship graphs on the Web via meta-search, visual exploratory search of relationship graphs through both querying and browsing strategies, and human-computer interactions via the multi-touch interface and mobile Internet on smartphones. In comparison with traditional lookup search methodologies, the proposed VESRGS system is characterized with the following perceived advantages. 1) It infers rich semantic relationships between the querying keywords and other related concepts from large-scale meta-search results from Google, Yahoo! and Bing search engines, and represents semantic relationships via graphs; 2) the exploratory search approach empowers users to naturally and effectively explore, adventure and discover knowledge in a rich information world of interlinked relationship graphs in a personalized fashion; 3) it effectively takes the advantages of smartphones’ user-friendly interfaces and ubiquitous Internet connection and portability. Our extensive experimental results have demonstrated that the VESRGS framework can significantly improve the users’ capability of seeking the most relevant relationship information to their own specific needs. We envision that the VESRGS framework can be a starting point for future exploration of novel, effective search strategies in the mobile Internet era. PMID:24223936

An experimental search strategy retrieves more precise results than PubMed and Google for questions about medical interventions

PubMed Central

Dylla, Daniel P.; Megison, Susan D.

2015-01-01

Objective. We compared the precision of a search strategy designed specifically to retrieve randomized controlled trials (RCTs) and systematic reviews of RCTs with search strategies designed for broader purposes. Methods. We designed an experimental search strategy that automatically revised searches up to five times by using increasingly restrictive queries as long at least 50 citations were retrieved. We compared the ability of the experimental and alternative strategies to retrieve studies relevant to 312 test questions. The primary outcome, search precision, was defined for each strategy as the proportion of relevant, high quality citations among the first 50 citations retrieved. Results. The experimental strategy had the highest median precision (5.5%; interquartile range [IQR]: 0%–12%) followed by the narrow strategy of the PubMed Clinical Queries (4.0%; IQR: 0%–10%). The experimental strategy found the most high quality citations (median 2; IQR: 0–6) and was the strategy most likely to find at least one high quality citation (73% of searches; 95% confidence interval 68%–78%). All comparisons were statistically significant. Conclusions. The experimental strategy performed the best in all outcomes although all strategies had low precision. PMID:25922798

CellAtlasSearch: a scalable search engine for single cells.

PubMed

Srivastava, Divyanshu; Iyer, Arvind; Kumar, Vibhor; Sengupta, Debarka

2018-05-21

Owing to the advent of high throughput single cell transcriptomics, past few years have seen exponential growth in production of gene expression data. Recently efforts have been made by various research groups to homogenize and store single cell expression from a large number of studies. The true value of this ever increasing data deluge can be unlocked by making it searchable. To this end, we propose CellAtlasSearch, a novel search architecture for high dimensional expression data, which is massively parallel as well as light-weight, thus infinitely scalable. In CellAtlasSearch, we use a Graphical Processing Unit (GPU) friendly version of Locality Sensitive Hashing (LSH) for unmatched speedup in data processing and query. Currently, CellAtlasSearch features over 300 000 reference expression profiles including both bulk and single-cell data. It enables the user query individual single cell transcriptomes and finds matching samples from the database along with necessary meta information. CellAtlasSearch aims to assist researchers and clinicians in characterizing unannotated single cells. It also facilitates noise free, low dimensional representation of single-cell expression profiles by projecting them on a wide variety of reference samples. The web-server is accessible at: http://www.cellatlassearch.com.

Kangaroo – A pattern-matching program for biological sequences

PubMed Central

2002-01-01

Background Biologists are often interested in performing a simple database search to identify proteins or genes that contain a well-defined sequence pattern. Many databases do not provide straightforward or readily available query tools to perform simple searches, such as identifying transcription binding sites, protein motifs, or repetitive DNA sequences. However, in many cases simple pattern-matching searches can reveal a wealth of information. We present in this paper a regular expression pattern-matching tool that was used to identify short repetitive DNA sequences in human coding regions for the purpose of identifying potential mutation sites in mismatch repair deficient cells. Results Kangaroo is a web-based regular expression pattern-matching program that can search for patterns in DNA, protein, or coding region sequences in ten different organisms. The program is implemented to facilitate a wide range of queries with no restriction on the length or complexity of the query expression. The program is accessible on the web at http://bioinfo.mshri.on.ca/kangaroo/ and the source code is freely distributed at http://sourceforge.net/projects/slritools/. Conclusion A low-level simple pattern-matching application can prove to be a useful tool in many research settings. For example, Kangaroo was used to identify potential genetic targets in a human colorectal cancer variant that is characterized by a high frequency of mutations in coding regions containing mononucleotide repeats. PMID:12150718

A Web-Based Data-Querying Tool Based on Ontology-Driven Methodology and Flowchart-Based Model

PubMed Central

Ping, Xiao-Ou; Chung, Yufang; Liang, Ja-Der; Yang, Pei-Ming; Huang, Guan-Tarn; Lai, Feipei

2013-01-01

Background Because of the increased adoption rate of electronic medical record (EMR) systems, more health care records have been increasingly accumulating in clinical data repositories. Therefore, querying the data stored in these repositories is crucial for retrieving the knowledge from such large volumes of clinical data. Objective The aim of this study is to develop a Web-based approach for enriching the capabilities of the data-querying system along the three following considerations: (1) the interface design used for query formulation, (2) the representation of query results, and (3) the models used for formulating query criteria. Methods The Guideline Interchange Format version 3.5 (GLIF3.5), an ontology-driven clinical guideline representation language, was used for formulating the query tasks based on the GLIF3.5 flowchart in the Protégé environment. The flowchart-based data-querying model (FBDQM) query execution engine was developed and implemented for executing queries and presenting the results through a visual and graphical interface. To examine a broad variety of patient data, the clinical data generator was implemented to automatically generate the clinical data in the repository, and the generated data, thereby, were employed to evaluate the system. The accuracy and time performance of the system for three medical query tasks relevant to liver cancer were evaluated based on the clinical data generator in the experiments with varying numbers of patients. Results In this study, a prototype system was developed to test the feasibility of applying a methodology for building a query execution engine using FBDQMs by formulating query tasks using the existing GLIF. The FBDQM-based query execution engine was used to successfully retrieve the clinical data based on the query tasks formatted using the GLIF3.5 in the experiments with varying numbers of patients. The accuracy of the three queries (ie, “degree of liver damage,” “degree of liver damage when applying a mutually exclusive setting,” and “treatments for liver cancer”) was 100% for all four experiments (10 patients, 100 patients, 1000 patients, and 10,000 patients). Among the three measured query phases, (1) structured query language operations, (2) criteria verification, and (3) other, the first two had the longest execution time. Conclusions The ontology-driven FBDQM-based approach enriched the capabilities of the data-querying system. The adoption of the GLIF3.5 increased the potential for interoperability, shareability, and reusability of the query tasks. PMID:25600078

A cloud-based framework for large-scale traditional Chinese medical record retrieval.

PubMed

Liu, Lijun; Liu, Li; Fu, Xiaodong; Huang, Qingsong; Zhang, Xianwen; Zhang, Yin

2018-01-01

Electronic medical records are increasingly common in medical practice. The secondary use of medical records has become increasingly important. It relies on the ability to retrieve the complete information about desired patient populations. How to effectively and accurately retrieve relevant medical records from large- scale medical big data is becoming a big challenge. Therefore, we propose an efficient and robust framework based on cloud for large-scale Traditional Chinese Medical Records (TCMRs) retrieval. We propose a parallel index building method and build a distributed search cluster, the former is used to improve the performance of index building, and the latter is used to provide high concurrent online TCMRs retrieval. Then, a real-time multi-indexing model is proposed to ensure the latest relevant TCMRs are indexed and retrieved in real-time, and a semantics-based query expansion method and a multi- factor ranking model are proposed to improve retrieval quality. Third, we implement a template-based visualization method for displaying medical reports. The proposed parallel indexing method and distributed search cluster can improve the performance of index building and provide high concurrent online TCMRs retrieval. The multi-indexing model can ensure the latest relevant TCMRs are indexed and retrieved in real-time. The semantics expansion method and the multi-factor ranking model can enhance retrieval quality. The template-based visualization method can enhance the availability and universality, where the medical reports are displayed via friendly web interface. In conclusion, compared with the current medical record retrieval systems, our system provides some advantages that are useful in improving the secondary use of large-scale traditional Chinese medical records in cloud environment. The proposed system is more easily integrated with existing clinical systems and be used in various scenarios. Copyright © 2017. Published by Elsevier Inc.

Comparing the Performance of NoSQL Approaches for Managing Archetype-Based Electronic Health Record Data

PubMed Central

Freire, Sergio Miranda; Teodoro, Douglas; Wei-Kleiner, Fang; Sundvall, Erik; Karlsson, Daniel; Lambrix, Patrick

2016-01-01

This study provides an experimental performance evaluation on population-based queries of NoSQL databases storing archetype-based Electronic Health Record (EHR) data. There are few published studies regarding the performance of persistence mechanisms for systems that use multilevel modelling approaches, especially when the focus is on population-based queries. A healthcare dataset with 4.2 million records stored in a relational database (MySQL) was used to generate XML and JSON documents based on the openEHR reference model. Six datasets with different sizes were created from these documents and imported into three single machine XML databases (BaseX, eXistdb and Berkeley DB XML) and into a distributed NoSQL database system based on the MapReduce approach, Couchbase, deployed in different cluster configurations of 1, 2, 4, 8 and 12 machines. Population-based queries were submitted to those databases and to the original relational database. Database size and query response times are presented. The XML databases were considerably slower and required much more space than Couchbase. Overall, Couchbase had better response times than MySQL, especially for larger datasets. However, Couchbase requires indexing for each differently formulated query and the indexing time increases with the size of the datasets. The performances of the clusters with 2, 4, 8 and 12 nodes were not better than the single node cluster in relation to the query response time, but the indexing time was reduced proportionally to the number of nodes. The tested XML databases had acceptable performance for openEHR-based data in some querying use cases and small datasets, but were generally much slower than Couchbase. Couchbase also outperformed the response times of the relational database, but required more disk space and had a much longer indexing time. Systems like Couchbase are thus interesting research targets for scalable storage and querying of archetype-based EHR data when population-based use cases are of interest. PMID:26958859

Comparing the Performance of NoSQL Approaches for Managing Archetype-Based Electronic Health Record Data.

PubMed

Freire, Sergio Miranda; Teodoro, Douglas; Wei-Kleiner, Fang; Sundvall, Erik; Karlsson, Daniel; Lambrix, Patrick

2016-01-01

This study provides an experimental performance evaluation on population-based queries of NoSQL databases storing archetype-based Electronic Health Record (EHR) data. There are few published studies regarding the performance of persistence mechanisms for systems that use multilevel modelling approaches, especially when the focus is on population-based queries. A healthcare dataset with 4.2 million records stored in a relational database (MySQL) was used to generate XML and JSON documents based on the openEHR reference model. Six datasets with different sizes were created from these documents and imported into three single machine XML databases (BaseX, eXistdb and Berkeley DB XML) and into a distributed NoSQL database system based on the MapReduce approach, Couchbase, deployed in different cluster configurations of 1, 2, 4, 8 and 12 machines. Population-based queries were submitted to those databases and to the original relational database. Database size and query response times are presented. The XML databases were considerably slower and required much more space than Couchbase. Overall, Couchbase had better response times than MySQL, especially for larger datasets. However, Couchbase requires indexing for each differently formulated query and the indexing time increases with the size of the datasets. The performances of the clusters with 2, 4, 8 and 12 nodes were not better than the single node cluster in relation to the query response time, but the indexing time was reduced proportionally to the number of nodes. The tested XML databases had acceptable performance for openEHR-based data in some querying use cases and small datasets, but were generally much slower than Couchbase. Couchbase also outperformed the response times of the relational database, but required more disk space and had a much longer indexing time. Systems like Couchbase are thus interesting research targets for scalable storage and querying of archetype-based EHR data when population-based use cases are of interest.

Modeling User Behavior and Attention in Search

ERIC Educational Resources Information Center

Huang, Jeff

2013-01-01

In Web search, query and click log data are easy to collect but they fail to capture user behaviors that do not lead to clicks. As search engines reach the limits inherent in click data and are hungry for more data in a competitive environment, mining cursor movements, hovering, and scrolling becomes important. This dissertation investigates how…