In the field of process mining, it is worth noting that process mining techniques assume that the resulting event logs can not only continuously record the occurrence of events but also contain all event data. However, like in IoT systems, data transmission may fail due to weak signal or resource competition, which causes the company’s information system to be unable to keep a complete event log. Based on a incomplete event log, the process model obtained by using existing process mining technologies is deviated from actual business process to a certain degree. In this paper, we propose a method for repairing missing activities based on succession relation of activities from event logs. We use an activity relation matrix to represent the event log and cluster it. The number of traces in the cluster is used as a measure of similarity calculation between incomplete traces and cluster results. Parallel activities in selecting pre-occurrence and post-occurrence activities of missing activities from incomplete traces are considered. Experimental results on real-life event logs show that our approach performs better than previous method in repairing missing activities.

在过程挖掘领域中，值得注意的是，过程挖掘技术假定生成的事件日志不仅可以连续记录事件的发生，而且可以包含所有事件数据。但是，就像在IoT系统中一样，由于信号或资源竞争较弱，数据传输可能会失败，这将导致公司的信息系统无法保存完整的事件日志。基于不完整的事件日志，使用现有流程挖掘技术获得的流程模型在一定程度上偏离了实际业务流程。在本文中，我们提出了一种基于事件日志中活动的继承关系修复丢失活动的方法。我们使用活动关系矩阵来表示事件日志并将其聚类。群集中的迹线数量用作度量不完整迹线和群集结果之间的相似性。考虑从不完整的痕迹中选择缺失活动的发生前和发生后活动的并行活动。真实事件日志的实验结果表明，我们的方法在修复丢失的活动方面比以前的方法表现更好。