Providing Domain Knowledge for Process Mining with ReWOO-Based Agents
Publication date
2025-03-28
Editors
Delgado, Andrea
Slaats, Tijs
Advisors
Supervisors
Document Type
Part of book
Metadata
Show full item recordCollections
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