ASP-Based Declarative Process Mining (Extended Abstract)
This work addresses process mining challenges for researchers and practitioners by extending capabilities beyond existing standards, though it appears incremental in method adaptation.
The authors tackled the problem of modeling and solving declarative process mining tasks by proposing Answer Set Programming (ASP) as an approach, which can handle more general specifications like linear-time temporal logic over finite traces compared to the standard DECLARE language.
We propose Answer Set Programming (ASP) as an approach for modeling and solving problems from the area of Declarative Process Mining (DPM). We consider here three classical problems, namely, Log Generation, Conformance Checking, and Query Checking. These problems are addressed from both a control-flow and a data-aware perspective. The approach is based on the representation of process specifications as (finite-state) automata. Since these are strictly more expressive than the de facto DPM standard specification language DECLARE, more general specifications than those typical of DPM can be handled, such as formulas in linear-time temporal logic over finite traces. (Full version available in the Proceedings of the 36th AAAI Conference on Artificial Intelligence).