期刊
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
卷 28, 期 12, 页码 5091-5112出版社
IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2021.3100413
关键词
Data visualization; Visual analytics; Task analysis; Data mining; Sequences; Pipelines; Medical diagnostic imaging; Visual analysis; event sequences; visualization
资金
- NSFC [62061136003]
This paper reviews the state-of-the-art visual analytics approaches for event sequence data and categorizes them based on analytical tasks and applications. The authors also identify several remaining research challenges and future research opportunities.
Event sequence data record series of discrete events in the time order of occurrence. They are commonly observed in a variety of applications ranging from electronic health records to network logs, with the characteristics of large-scale, high-dimensional and heterogeneous. This high complexity of event sequence data makes it difficult for analysts to manually explore and find patterns, resulting in ever-increasing needs for computational and perceptual aids from visual analytics techniques to extract and communicate insights from event sequence datasets. In this paper, we review the state-of-the-art visual analytics approaches, characterize them with our proposed design space, and categorize them based on analytical tasks and applications. From our review of relevant literature, we have also identified several remaining research challenges and future research opportunities.
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