# Survey on Visual Analysis of Event Sequence Data

arXiv: https://arxiv.org/abs/2006.14291 · HTML: https://mlsanity.com/paper/2006.14291/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2020.

## Built on by (most cited first, 10 of 10)

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- [Comparing Native and Non-native English Speakers' Behaviors in Collaborative Writing through Visual Analytics](https://mlsanity.com/paper/2502.18681/) (2025)
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## Abstract (arXiv)

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.