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Synthetic Business Process Simulation

Project description

SynBPS

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SynBPS is short for Synthetic Business Process Simulation. This framework is designed to simulate synthetic business processes. In a nutshell, this framework lets you run predictive process monitoring experiments across multiple business processes, specified by well-known parametric distributions. See more in the publication: Riess (2024) [pdf]

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Whats new: Version 1.1.1

  • Added support for process memory with HOMC of order > 4
  • Added Example notebooks in examples/ folder
  • Added ability for users to specify distribution parameters (memoryless process)
  • Fixed issues with seed value in processes with memory
  • Restructuring and separation of functions, based on their purpose:
    • Design for generating a DoE
    • Simulation for functions related to event-log generation
    • Dataprep for functions related to data-preparation for ML models (prefix-log, temporal splitting etc.,)
  • Other minor fixes

Please note: Version 1.1.0** introduces new parameters and different function locations. Users are therefore advised to refer to the slightly changed code examples in examples/ folder.

Getting Started

You can install SynBPS using pip:

pip install SynBPS

Once installed, you can:

Documentation

See the official documentation here.

Citation

If you use SynBPS, please cite the corresponding paper. The paper can be cited as:

@article{riess2024synbps,
	title={SynBPS: a parametric simulation framework for the generation of event-log data},
	author={Riess, Mike},
	journal={SIMULATION},
	pages={00375497241233326},
	year={2024},
	publisher={SAGE Publications Sage UK: London, England}
}

Contributing

If you would like to contribute to SynBPS, you are welcome to submit your suggestions, bug reports, or pull requests. Follow the guidelines to ensure smooth collaboration.

Thanks

Jacob Schreiber and Pomegranate team. Joachim Scholderer and Kristoffer Lien.

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