Mining Timing Constraints from Event Logs for Process Model
Mining Timing Constraints from Event Logs for Process Model
复制标题
DOI：
10.1109/compsac48688.2020.0-139
复制
发表时间：
2020-07
期刊：
影响因子：
--
通讯作者：
Zhenyu Zhang;Chunhui Guo;Shangping Ren
中科院分区：
文献类型：
--
作者：
Zhenyu Zhang;Chunhui Guo;Shangping Ren
流程挖掘是一种从事件日志中提取流程模型的技术。事件日志包含与事件相关的大量信息，如事件的时间戳、触发事件的操作等。现有的流程挖掘研究主要集中在发现事件日志背后的流程模型。如何从与所发现的过程模型相关联的事件日志中发现时间约束还没有得到很好的研究。在本文中，我们提出了一种扩展现有流程挖掘技术的方法，不仅可以挖掘时间约束，还可以将时间约束与现有流程挖掘算法发现和构建的流程模型相结合。该方法包括三个主要步骤，即首先，对于已有的流程挖掘算法构建的表示为工作流网的给定流程模型，提取该工作流网模型中每个变迁的时间依赖集。其次，基于时间依赖集，提出了一种从事件日志中提取模型中每个转换的时间约束的算法。第三，将原有的工作流网扩展为时间Petri网，其中发现的时间约束与其对应的变迁相关联。通过一个实际的道路交通精细化管理过程场景，展示了该方法如何从事件日志中发现精细化管理过程中的时间约束。
Process mining is a technique for extracting process models from event logs. Event logs contain abundant information related to an event such as the timestamp of the event, the actions that triggers the event, etc. Much of existing process mining research has been focused on discoveries of process models behind event logs. How to uncover the timing constraints from event logs that are associated with the discovered process models is not well-studied. In this paper, we present an approach that extends existing process mining techniques to not only mine but also integrate timing constraints with process models discovered and constructed by existing process mining algorithms. The approach contains three major steps, i.e., first, for a given process model constructed by an existing process mining algorithm and represented as a workflow net, extract a time dependent set for each transition in the workflow net model. Second, based on the time dependent sets, develop an algorithm to extract timing constraints from event logs for each transition in the model. Third, extend the original workflow net into a time Petri net where the discovered timing constraints are associated with their corresponding transitions. A real-life road traffic fine management process scenario is used as a case study to show how timing constraints in the fine management process can be discovered from event logs with our approach.