OCPM$^2$: Extending the Process Mining Methodology for Object-Centric Event Data Extraction

2025-03-13

AI Summary

To address the low extraction efficiency of Object-Centered Event Data (OCED), high log reconstruction costs, and difficulties in multi-perspective relational modeling in multi-entity collaborative scenarios, this paper proposes the first structured OCED extraction framework. Grounded in the PM² process mining paradigm, the framework integrates the OCEL standard with the PM² metamodel to enable cross-system data integration (e.g., between Learning Management Systems and academic administration systems), supporting “one-time extraction, multi-perspective reuse.” Evaluated on real-world educational processes, it successfully constructs high-quality object-centered event logs (OCELs), significantly reducing log reconstruction complexity. The framework demonstrates both feasibility and cross-domain generalizability. This work establishes a reusable, engineering-oriented pathway and methodological foundation for Object-Centered Process Mining (OCPM), advancing practical adoption of OCED-based analysis in heterogeneous enterprise environments.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Natural Language Processing: Information Extraction
• Knowledge Representation and Reasoning: Description Logics

Application Category

• Web Mining and Content Analysis: Bridging structured and unstructured data
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

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 method for extracting OCED based on PMinst{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.

Problem

• Extracting Object-Centric Event Data (OCED) for multi-perspective process analysis.
• Reducing complexity in log extraction for Object-Centric Process Mining (OCPM).
• Validating a framework for OCED extraction in real-world educational systems.

Innovation

• Extends PM2 for Object-Centric Event Data extraction
• Introduces structured framework for OCED extraction
• Validates framework in educational setting with OCEL