Synchronizing Process Model and Event Abstraction for Grounded Process Intelligence (Extended Version)

2025-05-29

AI Summary

In process intelligence, model abstraction (MA) and event abstraction (EA) are often misaligned, causing simplified process models to lose behavioral grounding in the original event log and undermining analytical reliability. Method: This paper introduces the first formal framework for synchronizing MA and EA. It employs behavior profiles to realize non-order-preserving model abstraction and derives a semantically equivalent event abstraction method therefrom. Contribution/Results: We formally prove that synchronized abstraction strictly preserves behavioral equivalence—overcoming the fundamental limitation of conventional abstraction approaches, which yield logs lacking support for the abstracted model. The proposed mechanism enables discovery of behaviorally equivalent process models, ensuring all process analyses remain grounded in actual event behavior. By guaranteeing semantic consistency between abstracted models and their underlying event data, our framework establishes a novel foundation for interpretable and verifiable process intelligence.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Model abstraction (MA) and event abstraction (EA) are means to reduce complexity of (discovered) models and event data. Imagine a process intelligence project that aims to analyze a model discovered from event data which is further abstracted, possibly multiple times, to reach optimality goals, e.g., reducing model size. So far, after discovering the model, there is no technique that enables the synchronized abstraction of the underlying event log. This results in loosing the grounding in the real-world behavior contained in the log and, in turn, restricts analysis insights. Hence, in this work, we provide the formal basis for synchronized model and event abstraction, i.e., we prove that abstracting a process model by MA and discovering a process model from an abstracted event log yields an equivalent process model. We prove the feasibility of our approach based on behavioral profile abstraction as non-order preserving MA technique, resulting in a novel EA technique.

Problem

• Synchronizing model and event abstraction techniques
• Maintaining grounding in real-world behavior during abstraction
• Ensuring equivalent process models after abstraction steps

Innovation

• Synchronized model and event abstraction technique
• Behavioral profile abstraction for non-order preservation
• Formal basis for equivalent abstracted process models