Process mining acts as a valuable tool to analyse the behaviour of an organisation by offering techniques to discover, monitor and enhance real processes. The key to process mining is to discovery understandable process models. However, real-life logs can be complex with redundant activities, which share similar behaviour but have different syntax. We show that the existence of such redundant activities heavily affects the quality of discovered process models. Existing approaches filter activities by frequency, which cannot solve problems caused by redundant activities. In this paper, we propose first to discover redundant activities in the log level and, then, use the discovery results to simplify event logs. Two publicly available data sets are used to evaluate the usability of our approach in real-life processes. Our approach can be adopted as a preprocessing step before applying any discovery algorithms to produce simplify models.

翻译：加工采矿是分析一个组织的行为的一种宝贵工具,它提供了发现、监测和加强真实过程的技术。加工采矿的关键是发现可理解的过程模型。然而,实际生活记录与重复活动可能十分复杂,重复活动具有相似的行为,但有不同的语法。我们表明,这种重复活动的存在严重影响了所发现的过程模型的质量。现有办法按频率过滤活动,无法解决重复活动造成的问题。在本文件中,我们首先提议在日志一级发现多余的活动,然后利用发现的结果来简化事件日志。两个公开可得到的数据集用来评估我们在实际生活过程中的方法的可用性。我们的方法可以作为预先处理步骤,然后应用任何发现算法来制作简化模型。