Abstract
Open-source process mining provides many algorithms for the analysis of event data which could be used to analyze mainstream processes (e.g., O2C, P2P, CRM). However, compared to commercial tools, they lack the performance and struggle to analyze large amounts of data. This paper presents PM4Py-GPU, a Python process mining library based on the NVIDIA RAPIDS framework. Thanks to the dataframe columnar storage and the high level of parallelism, a significant speed-up is achieved on classic process mining computations and processing activities.
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We thank the Alexander von Humboldt (AvH) Stiftung for supporting our research.
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Berti, A., Nghia, M.P., van der Aalst, W.M.P. (2022). PM4Py-GPU: A High-Performance General-Purpose Library for Process Mining. In: Guizzardi, R., Ralyté, J., Franch, X. (eds) Research Challenges in Information Science. RCIS 2022. Lecture Notes in Business Information Processing, vol 446. Springer, Cham. https://doi.org/10.1007/978-3-031-05760-1_49
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DOI: https://doi.org/10.1007/978-3-031-05760-1_49
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