Event sequence data is increasingly available in various application domains, such as business process management, software engineering, or medical pathways. Processes in these domains are typically represented as process diagrams or flow charts. So far, various techniques have been developed for automatically generating such diagrams from event sequence data. An open challenge is the visual analysis of drift phenomena when processes change over time. In this paper, we address this research gap. Our contribution is a system for fine-granular process drift detection and corresponding visualizations for event logs of executed business processes. We evaluated our system both on synthetic and real-world data. On synthetic logs, we achieved an average F-score of 0.96 and outperformed all the state-of-the-art methods. On real-world logs, we identified all types of process drifts in a comprehensive manner. Finally, we conducted a user study highlighting that our visualizations are easy to use and useful as perceived by process mining experts. In this way, our work contributes to research on process mining, event sequence analysis, and visualization of temporal data.
翻译:各个应用领域,如业务流程管理、软件工程或医疗路径,都越来越多地提供事件序列数据。这些领域的流程通常以流程图或流程图的形式呈现。到目前为止,已经开发了各种技术,以便从事件序列数据中自动生成此类图表。一个公开的挑战就是随着时间的变化对漂流现象进行视觉分析。在本文中,我们探讨了这一研究差距。我们的贡献是建立一个微调过程漂移探测系统,并相应地对已执行业务流程的事件记录进行可视化。我们在合成和真实世界数据方面都评估了我们的系统。在合成日志上,我们取得了平均F芯数0.96,并且超过了所有最先进的方法。在现实世界日志上,我们以全面的方式确定了所有类型的流程漂流。最后,我们开展了一项用户研究,强调我们的视觉化很容易使用,而且正如进程采矿专家所察觉的那样是有用的。通过这种方式,我们的工作有助于对进程采矿、事件序列分析和时间数据的可视化进行研究。