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).
ASP-based declarative process mining / Chiariello, Francesco; Maggi, Fabrizio Maria; Patrizi, Fabio. - 36:5(2022), pp. 5539-5547. (Intervento presentato al convegno National Conference of the American Association for Artificial Intelligence tenutosi a Virtual, Online) [10.1609/aaai.v36i5.20493].
ASP-based declarative process mining
Chiariello, Francesco
;Maggi, Fabrizio Maria;Patrizi, Fabio
2022
Abstract
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).File | Dimensione | Formato | |
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