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

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.

References in corpus (1)

• RapidProM: Mine Your Processes and Not Just Your Data

Cited by in corpus (19)

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• Adversarial System Variant Approximation to Quantify Process Model Generalization
• SIMPT: Process Improvement Using Interactive Simulation of Time-aware Process Trees
• Efficient Construction of Behavior Graphs for Uncertain Event Data
• Probability Estimation of Uncertain Process Trace Realizations
• Detecting and Understanding Branching Frequency Changes in Process Models
• CoCoMoT: Conformance Checking of Multi-Perspective Processes via SMT (Extended Version)
• Privacy-Preserving Directly-Follows Graphs: Balancing Risk and Utility in Process Mining
• SaCoFa: Semantics-aware Control-flow Anonymization for Process Mining
• The Role of Time and Data: Online Conformance Checking in the Manufacturing Domain
• Efficient Time and Space Representation of Uncertain Event Data
• PROVED: A Tool for Graph Representation and Analysis of Uncertain Event Data
• Increasing Scalability of Process Mining using Event Dataframes: How Data Structure Matters