TY - JOUR T1 - Visual Exploration across Biomedical Databases JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics Y1 - 2011 A1 - Lieberman,M.D. A1 - Taheri, S. A1 - Guo,Huimin A1 - Mirrashed,F. A1 - Yahav,I. A1 - Aris,A. A1 - Shneiderman, Ben KW - Bioinformatics KW - Biomedical computing KW - biomedical databases KW - cross-database exploration KW - Data exploration and discovery KW - Data visualization KW - database management systems KW - Databases, Factual KW - DNA KW - graph theory KW - Information Storage and Retrieval KW - information visualization. KW - Keyword search KW - medical computing KW - natural language processing KW - Proteins KW - semantic networks KW - semantics KW - sequences KW - text mining KW - User-Computer Interface KW - user-defined semantics KW - visual databases AB - Though biomedical research often draws on knowledge from a wide variety of fields, few visualization methods for biomedical data incorporate meaningful cross-database exploration. A new approach is offered for visualizing and exploring a query-based subset of multiple heterogeneous biomedical databases. Databases are modeled as an entity-relation graph containing nodes (database records) and links (relationships between records). Users specify a keyword search string to retrieve an initial set of nodes, and then explore intra- and interdatabase links. Results are visualized with user-defined semantic substrates to take advantage of the rich set of attributes usually present in biomedical data. Comments from domain experts indicate that this visualization method is potentially advantageous for biomedical knowledge exploration. VL - 8 SN - 1545-5963 CP - 2 M3 - 10.1109/TCBB.2010.1 ER - TY - JOUR T1 - Temporal Summaries: Supporting Temporal Categorical Searching, Aggregation and Comparison JF - IEEE Transactions on Visualization and Computer Graphics Y1 - 2009 A1 - Wang,T. D A1 - Plaisant, Catherine A1 - Shneiderman, Ben A1 - Spring, Neil A1 - Roseman,D. A1 - Marchand,G. A1 - Mukherjee,V. A1 - Smith,M. KW - Aggregates KW - Collaborative work KW - Computational Biology KW - Computer Graphics KW - Data analysis KW - data visualisation KW - Data visualization KW - Databases, Factual KW - Displays KW - Event detection KW - Filters KW - Heparin KW - History KW - Human computer interaction KW - Human-computer interaction KW - HUMANS KW - Information Visualization KW - Interaction design KW - interactive visualization technique KW - Medical Records Systems, Computerized KW - Pattern Recognition, Automated KW - Performance analysis KW - Springs KW - temporal categorical data visualization KW - temporal categorical searching KW - temporal ordering KW - temporal summaries KW - Thrombocytopenia KW - Time factors AB - When analyzing thousands of event histories, analysts often want to see the events as an aggregate to detect insights and generate new hypotheses about the data. An analysis tool must emphasize both the prevalence and the temporal ordering of these events. Additionally, the analysis tool must also support flexible comparisons to allow analysts to gather visual evidence. In a previous work, we introduced align, rank, and filter (ARF) to accentuate temporal ordering. In this paper, we present temporal summaries, an interactive visualization technique that highlights the prevalence of event occurrences. Temporal summaries dynamically aggregate events in multiple granularities (year, month, week, day, hour, etc.) for the purpose of spotting trends over time and comparing several groups of records. They provide affordances for analysts to perform temporal range filters. We demonstrate the applicability of this approach in two extensive case studies with analysts who applied temporal summaries to search, filter, and look for patterns in electronic health records and academic records. VL - 15 SN - 1077-2626 CP - 6 M3 - 10.1109/TVCG.2009.187 ER - TY - JOUR T1 - Interactive Entity Resolution in Relational Data: A Visual Analytic Tool and Its Evaluation JF - IEEE Transactions on Visualization and Computer Graphics Y1 - 2008 A1 - Kang,Hyunmo A1 - Getoor, Lise A1 - Shneiderman, Ben A1 - Bilgic,M. A1 - Licamele,L. KW - algorithms KW - Computer Graphics KW - D-Dupe KW - data visualisation KW - database management systems KW - Databases, Factual KW - graphical user interface KW - Graphical user interfaces KW - human-centered computing KW - Image Interpretation, Computer-Assisted KW - Information Storage and Retrieval KW - Information Visualization KW - interactive entity resolution KW - relational context visualization KW - Relational databases KW - relational entity resolution algorithm KW - User interfaces KW - user-centered design KW - User-Computer Interface KW - visual analytic tool AB - Databases often contain uncertain and imprecise references to real-world entities. Entity resolution, the process of reconciling multiple references to underlying real-world entities, is an important data cleaning process required before accurate visualization or analysis of the data is possible. In many cases, in addition to noisy data describing entities, there is data describing the relationships among the entities. This relational data is important during the entity resolution process; it is useful both for the algorithms which determine likely database references to be resolved and for visual analytic tools which support the entity resolution process. In this paper, we introduce a novel user interface, D-Dupe, for interactive entity resolution in relational data. D-Dupe effectively combines relational entity resolution algorithms with a novel network visualization that enables users to make use of an entity's relational context for making resolution decisions. Since resolution decisions often are interdependent, D-Dupe facilitates understanding this complex process through animations which highlight combined inferences and a history mechanism which allows users to inspect chains of resolution decisions. An empirical study with 12 users confirmed the benefits of the relational context visualization on the performance of entity resolution tasks in relational data in terms of time as well as users' confidence and satisfaction. VL - 14 SN - 1077-2626 CP - 5 M3 - 10.1109/TVCG.2008.55 ER -