Journal
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
Volume 24, Issue 1, Pages 56-65Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2017.2745320
Keywords
Visual Knowledge Representation; Visual Knowledge Discovery; Data Clustering; Time Series Data; Illustrative Visualization
Categories
Funding
- NSFC [61602306]
- STCSM [15JC1401700]
- National Grants for the Thousand Young Talents in China
- NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Information [U1609220]
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Event sequence data such as electronic health records, a person's academic records, or car service records, are ordered series of events which have occurred over a period of time. Analyzing collections of event sequences can reveal common or semantically important sequential patterns. For example, event sequence analysis might reveal frequently used care plans for treating a disease, typical publishing patterns of professors, and the patterns of service that result in a well-maintained car. It is challenging, however, to visually explore large numbers of event sequences, or sequences with large numbers of event types. Existing methods focus on extracting explicitly matching patterns of events using statistical analysis to create stages of event progression over time. However, these methods fail to capture latent clusters of similar but not identical evolutions of event sequences. In this paper, we introduce a novel visualization system named EventThread which clusters event sequences into threads based on tensor analysis and visualizes the latent stage categories and evolution patterns by interactively grouping the threads by similarity into time-specific clusters. We demonstrate the effectiveness of EventThread through usage scenarios in three different application domains and via interviews with an expert user.
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