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OCPM2: Extending the Process Mining Methodology for Object-Centric Event Data Extraction
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.ORCID iD: 0000-0002-0870-0330
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.ORCID iD: 0000-0002-6633-8587
2025 (English)In: Enterprise, Business-Process and Information Systems Modeling: 26th International Conference, BPMDS 2025, and 30th International Conference, EMMSAD 2025, Vienna, Austria, June 16–17, 2025, Proceedings / [ed] Renata Guizzardi, Luise Pufahl, Arnon Sturm, Han van der Aa, Springer, 2025, p. 123-140Conference paper, Published paper (Refereed)
Abstract [en]

Object-Centric Process Mining (OCPM) enables business process analysis from multiple perspectives. For example, an educational path can be examined from the viewpoints of students, teachers, and groups. This analysis depends on Object-Centric Event Data (OCED), which captures relationships between events and object types, representing different perspectives. Unlike traditional process mining techniques, extracting OCED minimizes the need for repeated log extractions when shifting the analytical focus. However, recording these complex relationships increases the complexity of the log extraction process. To address this challenge, this paper proposes a methodology for extracting OCED based on PM2, a well-established process mining framework. Our approach introduces a structured framework that guides data analysts and engineers in extracting OCED for process analysis. We validate this framework by applying it in a real-world educational setting, demonstrating its effectiveness in extracting an Object-Centric Event Log (OCEL), which serves as the standard format for recording OCED, from a learning management system and an administrative grading system.

Place, publisher, year, edition, pages
Springer, 2025. p. 123-140
Series
Lecture Notes in Business Information Processing, ISSN 1865-1348, E-ISSN 1865-1356 ; 558
Keywords [en]
Object-Centric Process Mining, Methodology, Log Extraction
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-244416DOI: 10.1007/978-3-031-95397-2_8Scopus ID: 2-s2.0-105009215436ISBN: 978-3-031-95396-5 (print)OAI: oai:DiVA.org:su-244416DiVA, id: diva2:1971088
Conference
26th International Conference, BPMDS 2025, and 30th International Conference, EMMSAD 2025, Vienna, Austria, June 16–17, 2025
Available from: 2025-06-17 Created: 2025-06-17 Last updated: 2025-08-11Bibliographically approved

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Miri, NajmehZdravkovic, JelenaJalali, Amin

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