Process Mining for Python (PM4Py): Bridging the Gap Between Process- and Data Science

TL;DR

PM4Py is a new Python library that enhances process mining by integrating with popular data science tools, enabling customizable algorithms and scalable analysis of event data.

Contribution

The paper introduces PM4Py, a comprehensive Python library that bridges process mining and data science, supporting algorithm customization and large-scale experimentation.

Findings

Provides integration with pandas, numpy, scipy, scikit-learn

Offers architecture and functionality overview of PM4Py

Includes example applications demonstrating its capabilities

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.