论文标题
在数据增强过程模型中支持域数据选择
Supporting Domain Data Selection in Data-Enhanced Process Models
论文作者
论文摘要
流程挖掘通过使用来自现实世界数据的事件日志来发现过程模型，从而弥合了过程管理和数据科学之间的差距。除强制性事件属性外，其他属性可以是代表域数据（例如人力资源和成本）事件的一部分。数据增强的过程模型可视化与过程模型中与过程活动直接关联的域数据，从而以事件属性聚合的形式监视域数据的实际值。但是，事件日志可能具有许多属性，因此很难决定，在整个过程中可以观察到哪一个。本文介绍了支持域数据选择的三种机制，使过程分析师和域专家能够逐步了解其感兴趣的信息。我们将建议的技术应用于美国住院的模拟现实数据集。
Process mining bridges the gap between process management and data science by discovering process models using event logs derived from real-world data. Besides mandatory event attributes, additional attributes can be part of an event representing domain data, such as human resources and costs. Data-enhanced process models provide a visualization of domain data associated to process activities directly in the process model, allowing to monitor the actual values of domain data in the form of event attribute aggregations. However, event logs can have so many attributes that it is difficult to decide, which one is of interest to observe throughout the process. This paper introduces three mechanisms to support domain data selection, allowing process analysts and domain experts to progressively reach their information of interest. We applied the proposed technique on the MIMIC-IV real-world data set on hospitalizations in the US.
