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GEDI
Generating Event Data with Intentional Features for Benchmarking Process Mining
Table of Contents
Requirements
- Miniconda
- Graphviz on your OS e.g. For MacOS:
brew install graphviz
brew install swig
- For smac:
conda install pyrfr swig
Installation
conda env create -f .conda.yml- Install Feature Extractor for Event Data (feeed) in the newly installed conda environment:
pip install feeed
Startup
conda activate gedi
python main.py -o config_files/options/baseline.json -a config_files/algorithm/experiment_test.json
Usage
Our pipeline offers several pipeline steps, which can be run sequentially or partially:
- feature_extraction
- generation
- benchmark
- evaluation_plotter
We also include two notebooks, which output experimental results as in our paper.
To run different steps of the GEDI pipeline, please adapt the .json accordingly.
conda activate gedi
python main.py -o config_files/options/baseline.json -a config_files/algorithm/<pipeline-step>.json
For reference of possible keys and values for each step, please see config_files/algorithm/experiment_test.json.
To run the whole pipeline please create a new .json file, specifying all steps you want to run and specify desired keys and values for each step.
References
The framework used by GEDI is taken directly from the original paper by Maldonado, Frey, Tavares, Rehwald and Seidl. If you would like to discuss the paper, or corresponding research questions on benchmarking process mining tasks please email the authors.