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

TL;DR

This paper introduces a formal approach for synchronized model and event abstraction in process intelligence, ensuring that abstracted models and logs remain behaviorally equivalent, thus preserving real-world process insights.

Contribution

It provides the first formal foundation for synchronized abstraction, combining model and event abstraction techniques to maintain behavioral consistency.

Findings

Proves behavioral equivalence between abstracted models and logs.

Develops a novel event abstraction technique based on behavioral profiles.

Demonstrates feasibility through formal proofs and behavioral analysis.

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.