Visual Summarization and Analysis of Soft Patterns in Temporal Event Sequences
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. We refer to these as “Soft Patterns.” In this project, we explore novel visualization methods for clustering event sequences into threads based on tensor analysis. The threads are then visualized to display latent stage categories and evolution patterns. Interactive grouping allows threads to be grouped by similarity into time-specific clusters.
Shunan Guo, Ke Xu, Rongwen Zhao, David Gotz, Hongyuan Zha, and Nan Cao. EventThread: Visual Summarization and Stage Analysis of Event Sequence Data. IEEE Transactions on Visualization and Computer Graphics (In press).
[A preprint of this article is can be viewed by clicking here.]
David Gotz. Soft Patterns: Moving Beyond Explicit Sequential Patterns During Visual Analysis of Longitudinal Event Datasets. IEEE VIS Workshop on Temporal and Sequential Event Analysis, Baltimore, MD (2016).