Conformance checking is an area of process mining that studies methods for measuring and characterizing commonalities and discrepancies between processes recorded in event logs of IT-systems and designed processes, either captured in explicit process models or implicitly induced by information systems. Applications of conformance checking range from measuring the quality of models automatically discovered from event logs, via regulatory process compliance, to automated process enhancement. Recently, process mining researchers initiated a discussion on the desired properties the conformance measures should possess. This discussion acknowledges that existing measures often do not satisfy the desired properties. Besides, there is a lack of understanding by the process mining community of the desired properties for conformance measures that address partially matching processes, i.e., processes that are not identical but differ in some process steps. In this article, we extend the recently introduced precision and recall conformance measures between an event log and process model that are based on the concept of entropy from information theory to account for partially matching processes. We discuss the properties the presented extended measures inherit from the original measures as well as properties for partially matching processes the new measures satisfy. All the presented conformance measures have been implemented in a publicly available tool. We present qualitative and quantitative evaluations based on our implementation that show the feasibility of using the proposed measures in industrial settings.

一致性检查是过程挖掘的一个领域，它研究用于测量和表征IT系统事件日志中记录的过程与设计过程之间的共性和差异的方法，这些过程既可以被显式过程模型捕获，也可以由信息系统隐式引入。一致性检查的应用范围从测量事件日志中自动发现的模型质量（通过法规过程合规性）到自动化过程增强。最近，过程挖掘研究人员开始讨论一致性度量应具有的期望属性。讨论承认，现有措施通常无法满足所需的属性。除了，流程挖掘社区缺乏对一致性度量的期望属性的了解，这些一致性度量解决了部分匹配的流程，即不相同但在某些流程步骤中不同的流程。在本文中，我们将基于事件信息和信息模型的熵概念的事件日志和过程模型之间最近引入的精度和召回一致性度量进行扩展，以解决部分匹配的过程。我们讨论了提出的扩展度量从原始度量继承的属性，以及新度量满足的部分匹配过程的属性。所提供的所有符合性措施均已在公开可用的工具中实施。