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).
2022
National Conference of the American Association for Artificial Intelligence
Answer set programming; process mining; business process management
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
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].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1664266
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