OCPM$^2$: Extending the Process Mining Methodology for Object-Centric Event Data Extraction
This work addresses the complexity of log extraction for object-centric process mining, which is incremental as it extends an existing framework to a specific domain.
The paper tackles the challenge of extracting Object-Centric Event Data (OCED) for process mining by proposing a methodology based on the PM² framework, validated in a real-world educational setting to extract an Object-Centric Event Log (OCEL) from systems like a learning management system and an administrative grading system.
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 PM\inst{2}, 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.