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Issue No. 01 - Jan. (2018 vol. 24)
ISSN: 1077-2626
pp: 56-65
Shunan Guo , East China Normal University
Ke Xu , Hong Kong University of Science and Technology
Rongwen Zhao , iDVx LabTongji University
David Gotz , University of North Carolina, Chapel Hill
Hongyuan Zha , East China Normal University
Nan Cao , iDVx LabTongji University
ABSTRACT
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.
INDEX TERMS
Data visualization, Visualization, Automobiles, Hidden Markov models, Algorithm design and analysis, Semantics, Clustering algorithms
CITATION

S. Guo, K. Xu, R. Zhao, D. Gotz, H. Zha and N. Cao, "EventThread: Visual Summarization and Stage Analysis of Event Sequence Data," in IEEE Transactions on Visualization & Computer Graphics, vol. 24, no. 1, pp. 56-65, 2018.
doi:10.1109/TVCG.2017.2745320
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