26623

PM4Py-GPU: a High-Performance General-Purpose Library for Process Mining

Alessandro Berti, Minh Phan Nghia, Wil M.P. van der Aalst
Process and Data Science Group, RWTH Aachen, Aachen, Germany
arXiv:2204.04898 [cs.DB], (11 Apr 2022)

@misc{https://doi.org/10.48550/arxiv.2204.04898,

   doi={10.48550/ARXIV.2204.04898},

   url={https://arxiv.org/abs/2204.04898},

   author={Berti, Alessandro and Nghia, Minh Phan and van der Aalst, Wil M. P.},

   keywords={Databases (cs.DB), FOS: Computer and information sciences, FOS: Computer and information sciences},

   title={PM4Py-GPU: a High-Performance General-Purpose Library for Process Mining},

   publisher={arXiv},

   year={2022},

   copyright={Creative Commons Attribution 4.0 International}

}

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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