This demo paper presents an extension of the Scikit-learn library tailored for Process Mining applications. By integrating Process Mining capabilities into the well-known Scikit-learn, this work enables the establishment of standardized preprocessing procedures and learning workflows for researchers and practitioners. The significance of this library stems from the unaddressed challenge of reproducibility within the Process Mining community, coupled with the absence of benchmarking resources. The library is publicly accessible on GitHub, facilitating widespread adoption and collaboration in the field: https://github.com/raseidi/skpm.
A Scikit-learn Extension Dedicated to Process Mining Purposes / R.S. Oyamada, G. MARQUES TAVARES, S. Barbon Junior, P. Ceravolo (CEUR WORKSHOP PROCEEDINGS). - In: CoopIS-D 2023 : CoopIS Demonstration Track 2023 / [a cura di] F. Mannhardt, N. Assy. - [s.l] : CEUR-WS, 2023 Oct 01. - pp. 11-15 (( convegno Demonstration Track co-located with the International Conference on Cooperative Information Systems tenutosi a Groningen nel 2023.
A Scikit-learn Extension Dedicated to Process Mining Purposes
R.S. Oyamada
;G. MARQUES TAVARES;P. Ceravolo
2023
Abstract
This demo paper presents an extension of the Scikit-learn library tailored for Process Mining applications. By integrating Process Mining capabilities into the well-known Scikit-learn, this work enables the establishment of standardized preprocessing procedures and learning workflows for researchers and practitioners. The significance of this library stems from the unaddressed challenge of reproducibility within the Process Mining community, coupled with the absence of benchmarking resources. The library is publicly accessible on GitHub, facilitating widespread adoption and collaboration in the field: https://github.com/raseidi/skpm.File | Dimensione | Formato | |
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