Measuring rule-based LTLf process specifications: A probabilistic data-driven approach.

Declarative process specifications define the behavior of processes by means of rules based on Linear Temporal Logic on Finite Traces LTL f. 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 for diverse process mining tasks, including discovery, checking, and drift detection. • Interestingness measures for rule-based process specifications are introduced. • A probabilistic estimation framework of single rules and entire specifications is proposed. • Measures of specifications differ from the aggregation of the measures for their individual rules. • Measures exhibit different sensitivity to process behavior changes over time. [ABSTRACT FROM AUTHOR]