liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
OCPM2: Extending the Process Mining Methodology for Object-Centric Event Data Extraction
Stockholm University.
Linköping University, Faculty of Science & Engineering. Linköping University, Department of Computer and Information Science, Software and Systems.ORCID iD: 0000-0001-7621-0985
Stockholm University.
Stockholm University.
2025 (English)In: Lecture Notes in Business Information Processing, Springer, 2025, Vol. 558, 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. Vol. 558, p. 123-140
Series
Lecture Notes in Business Information Processing, ISSN 1865-1348, E-ISSN 1865-1356
Keywords [en]
Object-Centric Process Mining, Methodology, Log Extraction
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:liu:diva-216206DOI: 10.1007/978-3-031-95397-2_8ISI: 001551506600008Scopus ID: 2-s2.0-105009215436ISBN: 9783031953965 (print)ISBN: 9783031953972 (electronic)OAI: oai:DiVA.org:liu-216206DiVA, id: diva2:1987260
Conference
International Conference on Business Process Modeling, Development and Support, Vienna, AUSTRIA, JUN 16-17, 2025
Available from: 2025-08-05 Created: 2025-08-05 Last updated: 2025-09-29

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Khayatbashi, Shahrzad

Search in DiVA

By author/editor
Khayatbashi, Shahrzad
By organisation
Faculty of Science & EngineeringSoftware and Systems
Computer Systems

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 53 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf