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Discovering more precise process models from event logs by filtering out chaotic activities

机译：通过过滤掉混乱的活动从事件日志中发现更精确的过程模型

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摘要

Process Discovery is concerned with the automatic generation of a process model that describes a business process from execution data of that business process. Real life event logs can contain chaotic activities. These activities are independent of the state of the process and can, therefore, happen at rather arbitrary points in time. We show that the presence of such chaotic activities in an event log heavily impacts the quality of the process models that can be discovered with process discovery techniques. The current modus operandi for filtering activities from event logs is to simply filter out infrequent activities. We show that frequency-based filtering of activities does not solve the problems that are caused by chaotic activities. Moreover, we propose a novel technique to filter out chaotic activities from event logs. We evaluate this technique on a collection of seventeen real-life event logs that originate from both the business process management domain and the smart home environment domain. As demonstrated, the developed activity filtering methods enable the discovery of process models that are more behaviorally specific compared to process models that are discovered using standard frequency-based filtering.
机译：流程发现与流程模型的自动生成有关，该流程模型根据该业务流程的执行数据来描述该业务流程。现实生活中的事件日志可能包含混乱的活动。这些活动与流程的状态无关，因此可以在任意时间点发生。我们表明，事件日志中此类混乱活动的存在严重影响可以使用过程发现技术发现的过程模型的质量。当前从事件日志中过滤活动的方式是简单地过滤掉不频繁的活动。我们表明，基于频率的活动过滤不能解决由混乱活动引起的问题。此外，我们提出了一种从事件日志中过滤掉混乱活动的新技术。我们在来自业务流程管理域和智能家居环境域的17个现实事件日志的集合上评估了该技术。如图所示，与使用标准基于频率的过滤发现的过程模型相比，开发的活动过滤方法可以发现行为模型更具体的过程模型。

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