Measuring Rule-based LTLf Process Specifications: A Probabilistic Data-driven Approach

Author:

Alessio Cecconi, Luca Barbaro, Claudio Di Ciccio, Arik Senderovich

Keyword:

Computer Science, Artificial Intelligence, Artificial Intelligence (cs.AI), Logic in Computer Science (cs.LO)

journal:

date:

2023-05-08 16:00:00

Abstract

Declarative process specifications define the behavior of processes by means of rules based on Linear Temporal Logic on Finite Traces (LTLf). In a mining context, these specifications are inferred from, and checked on, multi-sets of runs recorded by information systems (namely, event logs). To this end, being able to gauge the degree to which process data comply with a specification is key. However, existing mining and verification techniques analyze the rules in isolation, thereby disregarding their interplay. In this paper, we introduce a framework to devise probabilistic measures for declarative process specifications. Thereupon, we propose a technique that measures the degree of satisfaction of specifications over event logs. To assess our approach, we conduct an evaluation with real-world data, evidencing its applicability in discovery, checking, and drift detection contexts.

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