Survey on Visual Analysis of Event Sequence Data
It addresses the problem of analyzing large-scale, high-dimensional event sequence data for analysts in fields like healthcare and networking, but it is incremental as it surveys existing methods.
This paper reviews state-of-the-art visual analytics approaches for event sequence data, which are complex and difficult to analyze manually, by characterizing them with a proposed design space and categorizing them based on tasks and applications.
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.