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).
2022, Proceedings of the 36th AAAI Conference on Artificial Intelligence, Pages 5539-5547 (volume: 36)
ASP-Based Declarative Process Mining (04b Atto di convegno in volume)
Chiariello Francesco, Maggi Fabrizio Maria, Patrizi Fabio
Gruppo di ricerca: Artificial Intelligence and Knowledge Representation