The traces of past process executions are maintained in many contexts, since they constitute a strategic source of information. Different tasks on such data can be supported. In particular, we focus on process model discovery, by proposing an approach that helps the analyst in identifying a good balance between overfitting and underfitting. To achieve such a goal, we have designed SIM (Semantic Interactive Miner), an innovative interactive and incremental tool, which starts from a non-generalized model, and provides the user with a path retrieval facility to analyse the current model, and with semantic abstractions to build increasingly more generalized models (through the selective merging of retrieved paths). Additionally, the tool exploits the path retrieval facility and an indexing strategy to support efficient trace retrieval. As a consequence, our framework represents the first literature contribution able to integrate in a synergic approach process model discovery, path retrieval, and trace retrieval.

We experimentally compare our tool to two well-known process mining algorithms, namely inductive miner (Leemans, Fahland, and van der Aalst, 2013) and heuristic miner (Weijters, van der Aalst, and de Medeiros, 2006). The comparison enlights the main innovative aspect of our approach, i.e., its ability to facilitate the analyst in directly using her/his domain knowledge to lead process model discovery, a feature that can be extremely advantageous in knowledge-rich applications, such as the medical ones.