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

Alessandro Berti, Sebastiaan J. van Zelst, Wil van der Aalst

arXiv:1905.06169v1cs.SE

TL;DR

Process-mining tools offered limited algorithmic customization, extensibility, and support for large-scale experimentation. The paper introduces PM4Py, a Python framework integrated with data-science libraries, and reports adoption through educational use, academic projects, and public distribution.

  • Problem

    Existing process-mining tools and RapidProM provide limited support for customizable algorithms, custom integration, and large-scale experimentation.

    Source: §I · p. 1

  • Method

    PM4Py uses Python and its ecosystem to provide an extendable, customizable process-mining framework with modular architecture and standardized factory methods.

    Source: §I · p. 1, §II · p. 2

  • Results

    PM4Py 1.0 was used by 200 students, supported two academic projects, and was distributed through PIP with documentation and a collaborative repository.

    Source: §IV · p. 4, §V · p. 4

  • Takeaways & Limitations

    PM4Py provides a growing library of process-mining techniques with installation, documentation, examples, and collaborative code contributions available to users.

    Source: §V · p. 4

Abstract

from arXiv · show

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.

I. INTRODUCTION

PM4Py is proposed to address process-mining tools’ limited support for extensibility, algorithmic customization, and large-scale experimentation. It uses Python’s data-science ecosystem to support a more collaborative and rigorously tested development environment.

I. INTRODUCTION

  • Existing process-mining tools emphasize graphical usability, but this hampers large-scale scientific experimentation.RapidProM supports repeated experiments but lacks easy algorithmic customization and integration of custom algorithms.Source: §I · p. 1
  • PM4Py is proposed as an easily extendable framework for algorithmic customization and large-scale process-mining experiments.The framework is implemented in Python, which is widely used across classic data science and machine-learning research.Source: §I · p. 1
  • PM4Py lowers the barrier for algorithmic development and customization compared with existing academic tools.The stated comparison includes ProM, RapidProM, and Apromore.Source: §I · p. 1
  • The framework integrates process-mining algorithms with algorithms from state-of-the-art Python data-science packages.This integration is presented as a central focal point of PM4Py.Source: §I · p. 1
  • PM4Py aims to support collaboration, documentation, and algorithmic stability through shared code, rich user support, and rigorous testing.These are listed as focal points of the library.Source: §I · p. 2

II. ARCHITECTURE AND FEATURES

PM4Py’s architecture separates process objects, algorithms, and visualizations to improve code reuse and large-scale experimentation. Factory methods standardize algorithm access while supporting variants and backward-compatible extension.

II. ARCHITECTURE AND FEATURES

  • PM4Py adopts architectural guidelines intended to maximize code understanding, reuse, and execution of large-scale experiments.These guidelines organize the library’s design around modularity and standardized access.Source: §II · p. 2
  • Objects, algorithms, and visualizations are placed in separate packages.The object package stores and converts process-mining objects, while the algorithm package contains discovery, conformance, and enhancement methods.Source: §II · p. 2
  • Factory methods provide a single standardized access point for algorithms using input objects such as event data and parameter objects.The Alpha Miner factory exposes classic and plus variants.Source: §II · p. 2
  • Factory methods support algorithm extension while maintaining backward compatibility.They typically accept an algorithm variant name and shared parameters.Source: §II · p. 2

A. Object Management

PM4Py represents event data through logs, streams, and data frames, with conversion utilities connecting formats. It also supports several additional process-mining object types.

A. Object Management

  • An event log is a collection of events describing activities performed for different process instances.PM4Py treats event data as a primary source for process mining.Source: A. Object Management · p. 2
  • Event logs represent lists of traces, with each trace containing events stored as key-value maps.This structure organizes events by process case.Source: A. Object Management · p. 2
  • Event streams represent a single list of key-value-map events that has not yet been organized into cases.Streams differ from event logs in their lack of case organization.Source: A. Object Management · p. 2
  • Conversion utilities transform event data between formats, and pandas data frames support efficient processing of larger event data.PM4Py also supports heuristic nets, accepting Petri nets, process trees, and transition systems.Source: A. Object Management · p. 2

B. Algorithms

PM4Py provides mainstream process-mining algorithms and analysis functions, including discovery, conformance checking, model evaluation, filtering, case statistics, graphing, and social-network analysis.

B. Algorithms

  • PM4Py supports process discovery through Alpha(+) Miner and Inductive Miner.These algorithms are listed among the library’s mainstream process-mining techniques.Source: B. Algorithms · p. 2
  • Conformance checking is supported through token-based replay and alignments.The library also provides functions for measuring model fitness, precision, generalization, and simplicity.Source: B. Algorithms · p. 2
  • PM4Py supports filtering by time frame, case performance, trace endpoints, variants, attributes, and paths.These filters operate across several event-log and trace characteristics.Source: B. Algorithms · p. 2
  • Case management provides statistics on variants and cases, while graphs summarize case duration, events over time, and numeric-attribute distributions.These functions provide complementary descriptive analyses of event data.Source: B. Algorithms · p. 2
  • Social Network Analysis includes handover-of-work, working-together, and subcontracting networks, with Pyvis enabling dynamic web-based visualization.The Handover of Work metric is illustrated using Pyvis visualization.Source: B. Algorithms · p. 2, Figure 4 · p. 3

C. Visualizations

PM4Py uses separate Python packages for visualization libraries and supports multiple static, web-based, and graph-oriented representations.

III. EXAMPLES

The paper introduces representative PM4Py examples, including process discovery with the Alpha Miner.

  • The examples section presents uses of PM4Py.Source: §III · p. 3
  • Figure 5 shows PM4Py performing process discovery with the Alpha Miner.Source: Figure 5 · p. 3

A. Process Discovery

The process-discovery example imports an XES log, applies the Alpha Miner, lays out the resulting graph, and displays the process model; related figures document PM4Py usage statistics.

B. Conformance Checking

The conformance-checking example applies alignments between a log object and a process model and displays each aligned trace's result.

IV. MATURITY OF THE TOOL

PM4Py showed early maturity through educational use, publicly available academic projects, integrations, usage tracking, and certification.

  • PM4Py 1.0 was used by 200 students in an Introduction to Data Science course at RWTH Aachen University.Source: §IV · p. 4
  • Two academic projects using PM4Py were publicly available, covering probabilistic automata for compliance checking and prefix alignments for streaming event data.Source: §IV · p. 4
  • PM4Py was integrated into bupaR for alignments and Inductive Miner models, while a Vue.JS data analytics web interface used the library.Source: §IV · p. 4
  • Google Analytics and PIP statistics were reported to track access to the PM4Py website and downloads of the library.Source: §IV · p. 4
  • The library received maximum-score XES certification, and issues were managed through GitHub.Source: §IV · p. 4

V. CONCLUSION

PM4Py provides a growing Python library for process mining, with installation, documentation, and collaboration support for its users.

  • PM4Py supports a rapidly growing set of process mining techniques, including discovery, conformance checking, and enhancement.Source: §V · p. 4
  • The library can be installed with the command pip install pm4py.Source: §V · p. 4
  • Extensive documentation and a collaborative GitHub ecosystem support user guidance, problem reporting, and code contributions.Source: §V · p. 4

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