Answer Set Programming (ASP), a well-known declarative programming paradigm, has recently found practical application in Process Mining, particularly in tasks involving declarative specifications of business processes. Declare is the most popular declarative process modeling language. It provides a way to model processes by sets of constraints, expressed in Linear Temporal Logic over Finite Traces (LTLf ), that valid traces must satisfy. Existing ASP-based solutions encode a Declare constraint by the corresponding LTLf formula or its equivalent automaton, derived using well-established techniques. In this paper, we propose a novel encoding for Declare constraints, which models their semantics directly as ASP rules, without resorting to intermediate representations. We evaluate the effectiveness of the novel approach on two Process Mining tasks by comparing it to alternative ASP encodings and a Python library for Declare.

A Direct ASP Encoding for Declare

Fionda, Valeria;Ielo, Antonio
;
Ricca, Francesco
2024-01-01

Abstract

Answer Set Programming (ASP), a well-known declarative programming paradigm, has recently found practical application in Process Mining, particularly in tasks involving declarative specifications of business processes. Declare is the most popular declarative process modeling language. It provides a way to model processes by sets of constraints, expressed in Linear Temporal Logic over Finite Traces (LTLf ), that valid traces must satisfy. Existing ASP-based solutions encode a Declare constraint by the corresponding LTLf formula or its equivalent automaton, derived using well-established techniques. In this paper, we propose a novel encoding for Declare constraints, which models their semantics directly as ASP rules, without resorting to intermediate representations. We evaluate the effectiveness of the novel approach on two Process Mining tasks by comparing it to alternative ASP encodings and a Python library for Declare.
2024
9783031520372
9783031520389
Answer Set Programming
Declare
Process Mining
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/379157
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 0
social impact