Object-Centric Local Process Models

TL;DR

This paper introduces Object-Centric Local Process Models (OCLPMs), a novel approach using object-centric Petri nets to analyze complex processes without a single case notion, supported by a discovery algorithm and case studies.

Contribution

The paper presents the first method for object-centric local process modeling, integrating behavioral patterns with object-centric Petri nets and providing an implementation in ProM.

Findings

Effective analysis of complex processes without single case notion

Successful application demonstrated in two case studies

Approach evaluated on various event logs showing promising results

Abstract

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.