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.

中文翻译：

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事件序列数据可视化分析综述

事件序列数据按发生时间顺序记录一系列离散事件。它们普遍存在于从电子健康记录到网络日志的各种应用中，具有大规模、高维、异构的特点。事件序列数据的这种高度复杂性使分析人员难以手动探索和查找模式，从而导致对可视化分析技术的计算和感知辅助以从事件序列数据集中提取和传达见解的需求不断增加。在本文中，我们回顾了最先进的可视化分析方法，用我们提出的设计空间表征它们，并根据分析任务和应用对它们进行分类。从我们查阅相关文献来看，