基于时间的标注细化的生成
On Generation of Time-based Label Refinements
摘要：
流程挖掘是一个研究领域，专注于事件数据的分析，旨在提取流程中的见解。将过程挖掘技术应用于智能家居环境中的数据，有可能为（不）健康习惯提供有价值的见解，并为环境辅助生活解决方案做出贡献。然而，找到正确的事件标签来启用流程挖掘技术的应用远非微不足道，因为简单地使用触发传感器作为传感器事件的标签会导致无信息模型，从而允许太多的行为（过度泛化）。领域专家建议的传感器级事件标签的改进已经表明能够发现更精确和更有洞察力的过程模型。然而，目前还没有自动化的方法来生成细化的事件标签的过程挖掘的上下文中。在本文中，我们提出了一个框架，自动生成的标签细化的时间属性的基础上的事件。我们展示了一个案例研究与现实生活中的智能家居事件数据，行为更具体，因此更有见地，过程模型可以通过使用自动生成的精炼标签过程发现。
英文摘要：
Process mining is a research field focused on the analysis of event data with the aim of extracting insights in processes. Applying process mining techniques on data from smart home environments has the potential to provide valuable insights in (un)healthy habits and to contribute to ambient assisted living solutions. Finding the right event labels to enable application of process mining techniques is however far from trivial, as simply using the triggering sensor as the label for sensor events results in uninformative models that allow for too much behavior (overgeneralizing). Refinements of sensor level event labels suggested by domain experts have shown to enable discovery of more precise and insightful process models. However, there exist no automated approach to generate refinements of event labels in the context of process mining. In this paper we propose a framework for automated generation of label refinements based on the time attribute of events. We show on a case study with real life smart home event data that behaviorally more specific, and therefore more insightful, process models can be found by using automatically generated refined labels in process discovery.
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