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 - Exploiting aspectual features and connecting words for summarization-inspired temporal-relation extraction JF - Information Processing & Management Y1 - 2007 A1 - Dorr, Bonnie J A1 - Gaasterland,Terry KW - Parsing and corpus analysis KW - Summarization KW - temporal ordering KW - Temporal-relation extraction KW - Tense, aspect and connecting words AB - This paper presents a model that incorporates contemporary theories of tense and aspect and develops a new framework for extracting temporal relations between two sentence-internal events, given their tense, aspect, and a temporal connecting word relating the two events. A linguistic constraint on event combination has been implemented to detect incorrect parser analyses and potentially apply syntactic reanalysis or semantic reinterpretation—in preparation for subsequent processing for multi-document summarization. An important contribution of this work is the extension of two different existing theoretical frameworks—Hornstein’s 1990 theory of tense analysis and Allen’s 1984 theory on event ordering—and the combination of both into a unified system for representing and constraining combinations of different event types (points, closed intervals, and open-ended intervals). We show that our theoretical results have been verified in a large-scale corpus analysis. The framework is designed to inform a temporally motivated sentence-ordering module in an implemented multi-document summarization system. VL - 43 SN - 0306-4573 UR - http://www.sciencedirect.com/science/article/pii/S0306457307000271 CP - 6 M3 - 10.1016/j.ipm.2007.01.008 ER -