Providing Domain Knowledge for Process Mining with ReWOO-Based Agents

Publication date

2025-03-28

Authors

Vogt, Max W.
van der Putten, Peter
Reijers, H.A.ORCID 0000-0001-9634-5852ISNI 0000000037238136

Editors

Delgado, Andrea
Slaats, Tijs

Advisors

Supervisors

Document Type

Part of book
Open Access logo

License

cc_by

Abstract

Process mining practitioners often face the challenge of interpreting complex process data and driving process improvements with limited expertise in process optimization, tools, and the application domain of the process. This study explores the integration of LLM-based agentic frameworks in process mining to bridge this gap and democratize access to process optimization. We developed a Proof-of-Concept that leverages a Reasoning WithOut Observation (ReWOO)-based agent to perform process discovery, problem identification, generate ecosystem domain knowledge, and propose potential process improvements. Our experiments on a range of business processes suggest that LLM-based agent systems can insert meaningful domain knowledge into process mining tool interactions.

Keywords

Agents, Domain knowledge, LLM, Process mining, ReWOO, Management Information Systems, Control and Systems Engineering, Business and International Management, Information Systems, Modelling and Simulation, Information Systems and Management

Citation

Vogt, M W, van der Putten, P & Reijers, H A 2025, Providing Domain Knowledge for Process Mining with ReWOO-Based Agents. in A Delgado & T Slaats (eds), Process Mining Workshops - ICPM 2024 International Workshops, Lyngby, Denmark, October 14–18, 2024, Revised Selected Papers. Lecture Notes in Business Information Processing, vol. 533, Springer, pp. 663-676, International Workshops which were held in conjunction with the 6th International Conference on Process Mining, ICPM 2024, Lyngby, Denmark, 14/10/24. https://doi.org/10.1007/978-3-031-82225-4_49, conference