Process discovery is one of the primary process mining tasks and starting point for process improvements using event data. Existing process discovery techniques aim to find process models that best describe the observed behavior. The focus can be on recall (i.e., replay fitness) or precision. Here, we take a different perspective. We aim to discover a process model that allows for the good behavior observed, and does not allow for the bad behavior. In order to do this, we assume that we have a desirable event log ($L^+$) and an undesirable event log ($L^-$). For example, the desirable event log consists of the cases that were handled within two weeks, and the undesirable event log consists of the cases that took longer. Our discovery approach explores the tradeoff between supporting the cases in the desirable event log and avoiding the cases in the undesirable event log. The proposed framework uses a new inductive mining approach that has been implemented and tested on several real-life event logs. Experimental results show that our approach outperforms other approaches that use only the desirable event log ($L^+$). This supports the intuitive understanding that problematic cases can and should be used to improve processes.

翻译：过程发现是过程挖掘的首要任务之一，也是利用事件数据进行过程改进的起点。现有过程发现技术旨在寻找最能描述观察行为的过程模型，其关注点通常在于召回率（即回放拟合度）或精确度。本文从不同视角出发，旨在发现一个既允许观察到的良好行为、又禁止不良行为的过程模型。为此，我们假设存在一个期望事件日志（$L^+$）和一个非期望事件日志（$L^-$）。例如，期望事件日志包含两周内处理完成的案例，而非期望事件日志包含耗时更长的案例。我们的发现方法探索了支持期望事件日志中的案例与避免非期望事件日志中案例之间的权衡。该框架采用了一种新的归纳式挖掘方法，已在多个真实事件日志上完成实现与测试。实验结果表明，我们的方法优于仅使用期望事件日志（$L^+$）的其他方法，这印证了直观认知：问题案例可以且应当用于改进流程。