The traces of process executions are a strategic source of information, from which a model of the process can be mined. In our recent work, we have proposed SIM (semantic interactive miner), an innovative process mining tool to discover the process model incrementally: it supports the interaction with domain experts, who can selectively merge parts of the model to achieve compactness, generalization, and reduced redundancy. We now propose a substantial extension of SIM, making it able to exploit (both automatically and interactively) pre-encoded taxonomic knowledge about the refinement (ISA relations) and composition (part-of relations) of process activities, as is available in many domains. The extended approach allows analysts to move from a process description where activities are reported at the ground level to more user-interpretable/compact descriptions, in which sets of such activities are abstracted into the “macro-activities” subsuming them or constituted by them. An experimental evaluation based on a real-world setting (stroke management) illustrates the advantages of our approach.

Clinical guidelines, which represent the normative process models for healthcare organizations, are typically available in a textual, unstructured form. This issue hampers the application of classical conformance-checking algorithms to the medical domain, which take in input of a formalized and computer-interpretable description of the process. In this paper, (i) we propose overcoming this problem by taking advantage of a Large Language Model (LLM), in order to extract normative rules from textual guidelines; (ii) we then check and quantify the conformance of the patient event log with respect to such rules. Additionally, (iii) we adopt the approach as a means for evaluating the quality of the models mined by different process discovery algorithms from the event log, by comparing their conformance to the rules. We have tested our work in the domain of stroke. As regards conformance checking, we have proved the compliance of four Northern Italy hospitals to a general rule for diagnosis timing and to two rules that refer to thrombolysis treatment, and have identified some issues related to other rules, which involve the availability of magnetic resonance instruments. As regards process model discovery evaluation, we have assessed the superiority of Heuristic Miner with respect to other mining algorithms on our dataset. It is worth noting that the easy extraction of rules in our LLM-assisted approach would make it quickly applicable to other fields as well.