EventThread: Visual Summarization and Stage Analysis of Event Sequence Data
EventThread: Visual Summarization and Stage Analysis of Event Sequence Data
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EventThread：事件序列数据的可视化总结和阶段分析
DOI：
10.1109/tvcg.2017.2745320
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发表时间：
2018-01-01
影响因子：
5.2
通讯作者：
Cao, Nan
中科院分区：
文献类型：
--
作者：
Guo, Shunan;Xu, Ke;Cao, Nan
事件序列数据（例如电子健康记录、个人学业记录或汽车服务记录）是一段时间内发生的一系列事件的有序数据。分析事件序列的集合可以揭示常见的或语义上重要的序列模式。例如，事件序列分析可能会揭示治疗疾病的常用护理计划、教授的典型出版模式以及导致保养良好的汽车的服务模式。然而，以视觉方式探索大量事件序列或具有大量事件类型的序列是具有挑战性的。现有方法侧重于使用统计分析来提取明确匹配的事件模式，以创建事件随时间进展的阶段。然而，这些方法无法捕获事件序列相似但不相同的演化的潜在集群。在本文中，我们介绍了一种名为 EventThread 的新型可视化系统，该系统基于张量分析将事件序列聚类为线程，并通过按相似性将线程交互式分组为特定时间的簇来可视化潜在阶段类别和演化模式。我们通过三个不同应用领域的使用场景以及对专家用户的采访来展示 EventThread 的有效性。
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.