Using Meta-learning to Recommend Process Discovery Methods
This work addresses the challenge of method selection in process mining for practitioners, though it is incremental as it applies existing meta-learning techniques to a specific domain.
The paper tackles the problem of selecting suitable process discovery methods for event logs by proposing a meta-learning solution that recommends methods with 92% accuracy using light-weight features, reducing reliance on human expertise.
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.