Mikeriess / SynBPS

A simulation framework for synthetic event-log data, based on theoretical processes.

SynBPS

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]

Whats new: Version 1.1.3

• Added support for process memory with HOMC of order > 4
• Added Example notebooks in examples/ folder
• Added ability to specify distribution parameters (memoryless process)
• Added ability to specify the dataprep function manually (see e2e example notebook)
• 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.,)
• Updated readthedocs documentation with version 1.1.0+ syntax changes.
• 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:

• Run a simulation experiment with your own models using the End-to-end example notebook for a short demo of SynBPS.
• Or simply generate a single event-log using the example code in the Event-log example notebook. This code example also lets you integrate the power of SynBPS into your own custom code pipeline (for advanced users).
• For the memoryless process, you can also specify the parameters of the distributions manually as shown in the Custom distribution Event-log example notebook.

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.