Process mining is a technology that helps understand, analyze, and improve processes. It has been present for around two decades, and although initially tailored for business processes, the spectrum of analyzed processes nowadays is evermore growing. To support more complex and diverse processes, subdisciplines such as object-centric process mining and behavioral pattern mining have emerged. Behavioral patterns allow for analyzing parts of the process in isolation, while object-centric process mining enables combining different perspectives of the process. In this work, we introduce \emph{Object-Centric Local Process Models} (OCLPMs). OCLPMs are behavioral patterns tailored to analyzing complex processes where no single case notion exists and we leverage object-centric Petri nets to model them. Additionally, we present a discovery algorithm that starts from object-centric event logs, and implement the proposed approach in the open-source framework ProM. Finally, we demonstrate the applicability of OCLPMs in two case studies and evaluate the approach on various event logs.

翻译：过程挖掘是一种帮助理解、分析和改进过程的技术。它已存在约二十年，虽然最初是为业务流程量身定制的，但如今所分析过程的范围正在不断扩展。为了支持更复杂和多样化的过程，诸如面向对象的过程挖掘和行为模式挖掘等子学科已经出现。行为模式允许独立分析过程的各个部分，而面向对象的过程挖掘则能够整合过程的不同视角。在这项工作中，我们引入了 \emph{面向对象的局部过程模型}（OCLPMs）。OCLPMs 是专为分析不存在单一案例概念的复杂过程而设计的行为模式，我们利用面向对象的Petri网对其进行建模。此外，我们提出了一种从面向对象的事件日志开始的发现算法，并在开源框架 ProM 中实现了所提出的方法。最后，我们通过两个案例研究展示了 OCLPMs 的适用性，并在多种事件日志上对该方法进行了评估。