Probabilistic Compliance of Uncertain Traces in Declarative Process Mining

This paper presents our work in progress about the integration of Probabilistic Logic Programming (PLP) with Declarative Process Mining (DPM) to address uncertainty in business process management. Traditional DPM approaches, such as DECLARE, use deterministic constraints to permit/forbid activities, but real-world processes often involve incomplete or unreliable data. To bridge this gap, we recap our previous work on introducing in a separate way probabilistic extensions for events, traces, and constraints inspired by PLP’s Distribution Semantics. We present here an extension to our formal semantics to take into account at the same time uncertain events and uncertain constraints in order to perform compliance of a trace versus a process model. Preliminary experiments on a healthcare process demonstrate the approach’s feasibility but highlight scalability challenges due to exponential complexity, that will be addressed in future work.