Process Mining for Python (PM4Py): Bridging the Gap Between Process- and Data Science
Alessandro Berti, et al.
Informasi Umum
Kode
23.24.003
Klasifikasi
005.133 - Special programming techniques-specific programming language
Jenis
E-Article
Subjek
Programming Language, Data Mining, Python,
No. Rak
Tel-U Gedung Manterawu Lantai 5 : Rak 2
Tel-U Purwokerto : Rak 2
Dilihat
254 kali
Informasi Lainnya
Abstraksi
Abstract—Process mining, i.e., a sub-field of data science focusing on the analysis of event data generated during the execution of (business) processes, has seen a tremendous change over the past two decades. Starting off in the early 2000’s, with limited to no tool support, nowadays, several software tools, i.e., both open-source, e.g., ProM and Apromore, and commercial, e.g., Disco, Celonis, ProcessGold, etc., exist. The commercial process mining tools provide limited support for implementing custom algorithms. Moreover, both commercial and open-source process mining tools are often only accessible through a graphical user interface, which hampers their usage in large-scale experimental settings. Initiatives such as RapidProM provide process mining support in the scientific workflow-based data science suite RapidMiner. However, these offer limited to no support for algorithmic customization. In the light of the aforementioned, in this paper, we present a novel process mining library, i.e., Process Mining for Python (PM4Py), that aims to bridge this gap, providing integration with state-of-the-art data science libraries, e.g., pandas, numpy, scipy and scikit-learn. We provide a global overview of the architecture and functionality of PM4Py, accompanied by some representative examples of its usage.
Koleksi & Sirkulasi
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Pengarang
| Nama | Alessandro Berti, et al. |
| Jenis | Perorangan |
| Penyunting | |
| Penerjemah |
Penerbit
| Nama | Aachen University |
| Kota | Munich |
| Tahun | 2023 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 0,00 |
| Jenis | Non-Sirkulasi |
