DLR-SC / bacardi-prov-model

A reference implementation for the BACARDI PROV data model.

Welcome to the BACARDI PROV Data Model! 👋

A reference implementation for the BACARDI PROV Data Model.

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️🏗️ ️Installation

Clone the project and install using pip from the project root directory:

pip install .

⚡ Getting started

The BACARDI PROV data model has been designed according to W3C PROV specification. The reference implementation uses the Python library prov.

Task Model

Currently, only the task model is defined and documented in docs/task.md. It specifies how provenance of a task in BACARDI must be recorded according to the W3C PROV standard. The reference implementation can be found in the task module.

🚀‍ Execute Examples

Once installed, the example scripts can be executed on the command line. All scripts create a directory example-output in the current working directory and generate their content into it. You can execute the scripts as follows:

# Generates provenance bundle using the extended task model
task-bundle

# Generates provenance bundle using the simplified task model
simple-task-bundle

# Generates two provenance bundles using the simplified task model
multi-task-bundle

✨ How to cite

If you use the BACARDI PROV data model in a scientific publication, we would appreciate citing the following paper:

• M. Stoffers, M. Meinel, B. Hofmann and A. Schreiber, "Integrating Provenance-Awareness into the Space Debris Processing System BACARDI," 2022 IEEE Aerospace Conference (AERO), 2022, pp. 1-12, doi: 10.1109/AERO53065.2022.9843783.

Bibtex entry:

@INPROCEEDINGS{9843783,
  author={Stoffers, Martin and Meinel, Michael and Hofmann, Benjamin and Schreiber, Andreas},
  booktitle={2022 IEEE Aerospace Conference (AERO)}, 
  title={Integrating Provenance-Awareness into the Space Debris Processing System BACARDI}, 
  year={2022},
  volume={},
  number={},
  pages={1-12},
  doi={10.1109/AERO53065.2022.9843783}
}

You can also cite specific releases published on Zenodo:

✏️ References

Papers that refer to the BACARDI PROV Data Model:

• Stoffers, Martin and Meinel, Michael and Hofmann, Benjamin and Fiedler, Hauke (2022) A use case study on provenance-based data assessments for mission critical software systems. In: 73rd International Astronautical Congress (IAC 2022). 73rd International Astronautical Congress (IAC 2022), 18.-22. Sep. 2022, Paris, France. (In Press)

📝 License

Please see the file LICENSE.md for further information about how the content is licensed.