Probabilistic Traces in Declarative Process Mining

When dealing with real-world processes, it is essential to consider their inher- ent uncertainty to more accurately represent their nature. In this work, we consider cases in which some information in the log might be unreliable. We propose a novel semantics for probabilistic process traces, based on the Distribution Semantics from Probabilistic Logic Programming, which allows one to annotate event executions of an observed trace with a probability representing the uncertainty of the event as the degree of our belief in that event happening. Then, we propose a novel definition of probabilistic compliance of a probabilistic process trace w.r.t. a declarative process specification, and how to compute it using a proba- bilistic abduction proof-procedure. Experimental results on a real-world healthcare protocol are presented to evaluate the feasibility of the proposed semantics on conformance checking.