A Scalable Approach to Probabilistic Compliance in Declarative Process Mining

In the field of Process Mining, one of the main tasks is the assessment of compliance between actual process executions and a specific model, which is called compliance or conformance checking. Recently, a few works have started to propose probabilistic declarative process models, where the model is a set of constraints associated with a probability to model uncertainty, leading to the task of probabilistic conformance checking. In this paper, we propose to adopt PASCAL, an algorithm that learns Probabilistic Constraint Logic Theories in the learning from interpretation setting and that assigns to interpretations the probability of belonging to the positive class, for efficiently computing the probability of conformance of process traces against probabilistic declarative process models. We compare PASCAL with two other existing tools that perform probabilistic conformance checking. Results show that PASCAL achieves superior performance in terms of execution time, handling a much larger numbers of constraints and traces with a logarithmic computational complexity.