Process Mining Module - PDEng program Data Science
- Responsible Group: Process Analytics group at Eindhoven University of Technology
- Responsible Lecturer: Dr. Felix Mannhardt (@fmannhardt)
- Last Update: 28th April 2021
Course under construction
The notebooks in this repository are part of the Process Mining module of the JADS PDEng program on Data Science. In total there are currently 3 lectures and 3 hands-on exercises in this repository. The collection of notebooks is a living document and subject to change. Each lecture and exercise is accompanied by a notebook in both R and Python using the Process Mining frameworks bupaR and PM4Py, respectively.
Table of Contents
Block 1 - 'Event Logs and Process Visualization'
Block 2 - 'Process Discovery'
Block 3 - 'Process Mining Applications'
Installation & Usage
Simply click on the
launch binder links for either the R or the Python notebook.s
Simply build a Docker image with the provided Dockerfile:
docker build -t fmannhardt/course-processmining-intro .
And start the Docker container running Jupyter on port 8787:
docker run -p 8888:8888 fmannhardt/course-processmining-intro
You should be able to run the Jupyter notebooks directly in a Jupyter environment. Please make sure to have installed the following requirements:
pip install pandas pm4py plotline
Make sure to install GraphViz for the visualization. On Windows with Chocolately this should work:
choco install graphviz
Consult the PM4Py documentation for further details: https://pm4py.fit.fraunhofer.de/install
install.packages(c("IRkernel", "tidyverse", "bupaR", "processanimateR", "petrinetR"))
Depending on your system configuration, it can be tricky to make the
IRkernel known to Jupyter. Please follow the instructions here: https://github.com/IRkernel/IRkernel
As a hint, you may need to open the R console from an Anaconda console and perform
IRkernel::installspec() in case you are using conda environment.