Temporal Summaries: Supporting Temporal Categorical Searching, Aggregation and Comparison

TitleTemporal Summaries: Supporting Temporal Categorical Searching, Aggregation and Comparison
Publication TypeJournal Articles
Year of Publication2009
AuthorsWang TD, Plaisant C, Shneiderman B, Spring N, Roseman D, Marchand G, Mukherjee V, Smith M
JournalIEEE Transactions on Visualization and Computer Graphics
Pagination1049 - 1056
Date Published2009/12//Nov
ISBN Number1077-2626
KeywordsAggregates, Collaborative work, Computational Biology, Computer Graphics, Data analysis, data visualisation, Data visualization, Databases, Factual, Displays, Event detection, Filters, Heparin, History, Human computer interaction, Human-computer interaction, HUMANS, Information Visualization, Interaction design, interactive visualization technique, Medical Records Systems, Computerized, Pattern Recognition, Automated, Performance analysis, Springs, temporal categorical data visualization, temporal categorical searching, temporal ordering, temporal summaries, Thrombocytopenia, Time factors

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.