CausalFlow：事件序列中因果关系的可视化分析
CausalFlow: Visual Analytics of Causality in Event Sequences
摘要：
理解事件之间的关系在不同的领域都起着重要的作用，例如从应用程序日志中识别用户某些行为的原因，以及根据历史记录解释运动员的行为。共现被广泛用于描述事件之间的关系。然而，共现关系提供的见解是模糊的，这导致在解决领域问题的困难。在本文中，我们使用因果关系模型的事件之间的关系，并提出了一个可视化的方法进行因果关系分析的事件序列。我们将自动因果发现方法的方法，并提出了一个检测事件因果关系的模型。考虑到可解释性，我们设计了一种新的可视化命名为因果流集成检测到的因果关系到基于时间轴的事件序列可视化。通过这种设计，用户可以了解某些事件的发生并确定因果路径。我们进一步实现了一个交互式系统，以帮助用户全面分析事件序列。提供了两个案例研究，以评估该方法的可用性。
英文摘要：
Understanding the relation of events plays an important role in different domains, such as identifying the reasons for users' certain actions from application logs as well as explaining sports players' behaviors according to historical records. Co-occurrence has been widely used to characterize the relation of events. However, insights provided by the co-occurrence relation are vague, which leads to difficulties in addressing domain problems. In this paper, we use causation to model the relation of events and present a visualization approach for conducting the causation analysis of event sequences. We integrate automatic causal discovery methods into the approach and propose a model for detecting event causalities. Considering the interpretability, we design a novel visualization named causal flow to integrate the detected causality into timeline-based event sequence visualizations. With this design, users can understand the occurrence of certain events and identify the causal pathways. We further implement an interactive system to help users comprehensively analyze event sequences. Two case studies are provided to evaluate the usability of the approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
