https://github.com/mikeriess/synbps
A simulation framework for synthetic event-log data, based on theoretical processes.
https://github.com/mikeriess/synbps
event-log python simulation-framework
Last synced: about 1 month ago
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A simulation framework for synthetic event-log data, based on theoretical processes.
- Host: GitHub
- URL: https://github.com/mikeriess/synbps
- Owner: Mikeriess
- License: mit
- Created: 2023-12-22T19:35:20.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-28T21:22:47.000Z (over 1 year ago)
- Last Synced: 2025-11-28T02:05:15.660Z (3 months ago)
- Topics: event-log, python, simulation-framework
- Language: Python
- Homepage: https://pypi.org/project/synbps
- Size: 5.12 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# SynBPS
[](https://pepy.tech/project/synbps) [](https://synbps.readthedocs.io/en/latest/?badge=latest)
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)](https://journals.sagepub.com/doi/abs/10.1177/00375497241233326) [[pdf](https://journals.sagepub.com/doi/pdf/10.1177/00375497241233326?casa_token=h9BOK2WWdQQAAAAA:t46xt6_qhz651cLzDVktuPnr3ku-eRaWNk9vECyHEAZsl3OtUHCffCZncn48XI0BprdrZM8VcBT3)]
## 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](https://github.com/Mikeriess/SynBPS/blob/main/examples/simulation_e2e_example.ipynb))
- 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](https://github.com/Mikeriess/SynBPS/blob/main/examples/simulation_e2e_example.ipynb) for a short demo of SynBPS.
- Or simply generate a single event-log using the example code in the [Event-log example notebook](https://github.com/Mikeriess/SynBPS/blob/main/examples/event_log_example.ipynb). 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](https://github.com/Mikeriess/SynBPS/blob/main/examples/event_log_example_custom_dist.ipynb).
## Documentation
See the [official documentation here](https://synbps.readthedocs.io/en/latest/).
## 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](https://github.com/Mikeriess/SynBPS/blob/main/src/contributing.md) to ensure smooth collaboration.
## Thanks
Jacob Schreiber and Pomegranate team. Joachim Scholderer and Kristoffer Lien.