Temporal Summaries: Supporting Temporal Categorical Searching, Aggregation and Comparison

Authors:
Taowei David Wang;Catherine Plaisant;Ben Shneiderman;Neil Spring;David Roseman;Greg Marchand;Vikramjit Mukherjee;Mark Smith
Affiliations:
Human-Computer Interaction Lab and Dept. of Computer Science, University of Maryland at College Park;Human-Computer Interaction Lab and Dept. of Computer Science, University of Maryland at College Park;Human-Computer Interaction Lab and Dept. of Computer Science, University of Maryland at College Park;Dept. of Computer Science, University of Maryland at College Park;ER One Institute, Washington Hospital Center, Medstar Health;ER One Institute, Washington Hospital Center, Medstar Health;ER One Institute, Washington Hospital Center, Medstar Health;ER One Institute, Washington Hospital Center, Medstar Health
Venue:
IEEE Transactions on Visualization and Computer Graphics
Year:
2009

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Abstract

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 previsous 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.