Probabilistic Declarative Process Mining

This doctoral research integrates Probabilistic Logic Programming (PLP) with Declarative Process Mining (DPM) to address uncertainty in business process management. The research will have to address assumptions that are traditionally made in binary DPM but do not always hold in real cases, or are ignored in real world logs: (i) a process model able to perfectly distinguish between positive and negative examples; (ii) noise in the log, at the event level, or at the trace level; (iii) log incompleteness: for a variety of reasons some events could have not been recorded in the log (“missing events”); (iv) clarity and understandability of a declarative model, which might be undermined by the large number of its constraints.