Synchronizing Process Model and Event Abstraction for Grounded Process Intelligence (Extended Version)
This addresses a gap in process intelligence for analyzing abstracted models while maintaining grounding in event data, though it appears incremental as it builds on existing abstraction techniques.
The paper tackles the problem of losing grounding in real-world behavior when abstracting process models without synchronizing the underlying event log, by providing a formal basis for synchronized model and event abstraction and proving equivalence between abstracting a model and discovering from an abstracted log.
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.