Probabilistic Conformance Checking Based on Declarative Process Models

Abstract

Conformance checking is a fundamental task to detect deviations between the actual and the expected courses of execution of a business process. In this context, temporal business constraints have been extensively adopted to declaratively capture the expected behavior of the process. However, traditionally, these constraints are interpreted logically in a crisp way: a process execution trace conforms with a constraint model if all the constraints therein are satisfied. This is too restrictive when one wants to capture best practices, constraints involving uncontrollable activities, and exceptional but still conforming behaviors. This calls for the extension of business constraints with uncertainty. In this paper, we tackle this timely and important challenge, relying on recent results on probabilistic temporal logics over finite traces. Specifically, we equip business constraints with a natural, probabilistic notion of uncertainty. We discuss the semantic implications of the resulting framework and show how probabilistic conformance checking and constraint entailment can be tackled therein.