# OCPM2: Extending the Process Mining Methodology for Object-Centric Event Data Extraction

arXiv: https://arxiv.org/abs/2503.10735 · HTML: https://mlsanity.com/paper/2503.10735/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 13 Mar 2025.

## Builds on (most cited first, 8 of 8)

- [Multi-Dimensional Event Data in Graph Databases](https://mlsanity.com/paper/2005.14552/) (2020)
- [OCEL (Object-Centric Event Log) 2.0 Specification](https://mlsanity.com/paper/2403.01975/) (2024)
- [Towards a Simple and Extensible Standard for Object-Centric Event Data (OCED) -- Core Model, Design Space, and Lessons Learned](https://mlsanity.com/paper/2410.14495/) (2024)
- [An Event Data Extraction Approach from SAP ERP for Process Mining](https://mlsanity.com/paper/2110.03467/) (2021)
- [Object Type Clustering using Markov Directly-Follow Multigraph in Object-Centric Process Mining](https://mlsanity.com/paper/2206.11017/) (2022)
- [Enhancing Data-Awareness of Object-Centric Event Logs](https://mlsanity.com/paper/2212.02858/) (2022)
- [Transforming Object-Centric Event Logs to Temporal Event Knowledge Graphs (Extended Version)](https://mlsanity.com/paper/2406.07596/) (2024)
- [Advancing Object-Centric Process Mining with Multi-Dimensional Data Operations](https://mlsanity.com/paper/2412.00393/) (2024)

## Abstract (arXiv)

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 PMinst2, 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.