Analyzing event logs generated during the execution of digital processes, organizations can monitor the behavior of dysfunctional or unspecified processes. For achieving the most refined results, high-quality and up-to-date process models are required. However, the selection of the proper process discovery algorithm is often addressed by human experts that can relate quality criteria, event logs behavior, and discovery techniques. Exploiting a meta-learning approach, we created a procedure that identifies the optimal discovery technique based on a user-defined balance of quality metrics. Our experiments exploited 1091 event logs representing extensive possible business process behaviors. Given a set of available algorithms, we obtained an F-score of 0.76 for recommending the discovery algorithm that maximizes quality criteria. Moreover, our method supports a more in-depth investigation of the process discovery problem by mapping log behavior and discovery techniques.
