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Applied Process Mining Module

Course under construction 🚧

The notebooks in this repository are part of an Applied Process Mining module. 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'

  • Lecture Notebooks

  • Assignment Notebooks

    • TBD

Block 3 - 'Conformance Checking'

  • Lecture Notebooks

  • Assignment Notebooks

    • TBD

Block 4 - 'Predictive Process Mining'

  • Lecture Notebooks

    • TBD

  • Assignment Notebooks

    • TBD

Installation & Usage

Using MyBinder

Simply click on the launch binder links for either the R or the Python notebook.s

Run locally

Docker

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

Jupyter

You should be able to run the Jupyter notebooks directly in a Jupyter environment. Please make sure to have installed the following requirements:

Python

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

R

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.