# Using Meta-learning to Recommend Process Discovery Methods

arXiv: https://arxiv.org/abs/2103.12874 · HTML: https://mlsanity.com/paper/2103.12874/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 31 references that are in the map.
- First posted on arXiv 23 Mar 2021.

## Builds on (most cited first, 2 of 2)

- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [Automated Discovery of Process Models from Event Logs: Review and Benchmark](https://mlsanity.com/paper/1705.02288/) (2017)

## Built on by (most cited first, 1 of 1)

- [Selecting Optimal Trace Clustering Pipelines with AutoML](https://mlsanity.com/paper/2109.00635/) (2021)

## Abstract (arXiv)

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.