Associate Professor
Technical University of Denmark
Object-Centric Process Mining (OCPM) addresses the limitations of traditional process mining by allowing events to relate to multiple object types, thus better reflecting the complexity of real-world systems. However, current OCPM techniques are limited to static, log-based inputs and do not support real-time analysis, which is increasingly becoming critical in dynamic business settings. This work introduces a novel framework for Streaming Object-Centric Process Mining (SOCPM), enabling the online discovery of object-centric process models. The framework supports the construction – in real-time – of Object-Centric Directly-Follows Graphs (OC-DFGs) and a relational structure that captures inter-object cardinalities from streaming data, for which we introduce a dedicated lossy counting miner. The framework is implemented using the open-source pyBeamline library, and it is validated on public datasets, demonstrating strong alignment with offline methods and capabilities to adapt to evolving behaviors.
In Proceedings of International Workshop on Stream Management & Analytics for Process Mining (SMA4PM); October 2025.
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