  Process discovery methods have obtained remarkable achievements in Process
Mining, delivering comprehensible process models to enhance management
capabilities. However, selecting the suitable method for a specific event log
highly relies on human expertise, hindering its broad application. Solutions
based on Meta-learning (MtL) have been promising for creating systems with
reduced human assistance. This paper presents a MtL solution for recommending
process discovery methods that maximize model quality according to
complementary dimensions. Thanks to our MtL pipeline, it was possible to
recommend a discovery method with 92% of accuracy using light-weight features
that describe the event log. Our experimental analysis also provided
significant insights on the importance of log features in generating
recommendations, paving the way to a deeper understanding of the discovery
algorithms.
