New Survey of Visual Analytics Methods for Event Sequence Data to Appear in IEEE TVCG

A review of state-of-the-art visual analytics approaches, a formalized design space, and open challenges for future work.

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 a joint effort between researchers at the Intelligent Big Data Visualization Lab at Tongji University and our own Visual Analtics and Communication Lab here at UNC, we conducted a literature review of state-of-the-art visual analytics approaches, characterize those approaches with our new design space, and categorized the research contributions based on analytical tasks and applications. Based on our review of the relevant literature, we have also identified several remaining research challenges and future research opportunities.

The article has been accepted for publication in IEEE Transactions for Visualization and Computer Graphics (TVCG) and will appear in an upcoming issue. More details including a open full-text preprint of the article can be found on the publication’s page on this website.


Yu Guo, Shunan Guo, Zhuochen Jin, Smiti Kaul, David Gotz, Nan Cao (To Appear) A Survey on Visual Analysis of Event Sequence Data. IEEE Transactions on Visualization and Computer Graphics (TVCG).