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Declarative process specifications define the behavior of processes by meansof rules based on Linear Temporal Logic on Finite Traces (LTLf). In a miningcontext, these specifications are inferred from, and checked on, multi-sets ofruns recorded by information systems (namely, event logs). To this end, beingable to gauge the degree to which process data comply with a specification iskey. However, existing mining and verification techniques analyze the rules inisolation, thereby disregarding their interplay. In this paper, we introduce aframework to devise probabilistic measures for declarative processspecifications. Thereupon, we propose a technique that measures the degree ofsatisfaction of specifications over event logs. To assess our approach, weconduct an evaluation with real-world data, evidencing its applicability indiscovery, checking, and drift detection contexts.

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