Binary Discovery of Declarative Business Processes with ASP Preferences

Process discovery techniques focus on learning a process model starting from a given set of logged traces. The majority of the discovery approaches, however, only consider one set of examples to learn from, i.e., the log itself. Some recent works on declarative process discovery, instead, advocated the usefulness of taking into account two different sets of traces (a.k.a. positive and negative examples), with the goal of learning a set of constraints that is able to discriminate which trace belongs to which set. In this paper we recall our recent work on the discovery of process models from positive and negative examples, with the goal of learning a set of declarative constraints that is able to discriminate which trace belongs to which set, also taking into account user preferences on activities and constraint templates to be used to build the final set of constraints. The approach is grounded in a logic-based framework that provides a sound and formal meaning to the notion of expert preferences.