GRAG4PM: Graph Retrieval Augmented Generation Framework Adapted for Process Mining

Recent advancements in generative AI have improved process mining, making workflow analysis more accessible and scalable. However, large language models lack structured reasoning and fail to capture sequential dependencies in workflows, limiting their effectiveness. While retrieval-augmented generation (RAG) improves contextual knowledge integration, it does not enforce process execution constraints, leading to inconsistencies in workflow modeling. To address this, we propose GRAG4PM, a graph retrieval-augmented generation framework designed for process mining. GRAG4PM introduces three key innovations: a hierarchical graph architecture that captures multi-level process semantics, an adaptive pruning mechanism that refines workflow representations while preserving critical information, and a process-specific dual-indexing scheme that adapts existing hybrid retrieval to the constraints of workflow graphs. Experimental results demonstrate that GRAG4PM significantly improves workflow validation, anomaly detection, and predictive analysis, particularly in handling incomplete information. Across the three evaluated process-aware tasks, workflow-state validation, violation checking, and next-activity prediction, GRAG4PM achieves an average Macro-F1 score of 0.7259, outperforming Graph RAG (0.4394) and standard RAG (0.4036). Under missing-attribute settings, GRAG4PM also maintains the highest average Macro-F1 across masking conditions, confirming its robustness in incomplete data scenarios. By balancing structured workflow constraints with flexible AI-driven retrieval, GRAG4PM enables dynamic, interpretable, and adaptive process mining solutions and explores promising directions for future research on improving workflow solutions for AI.