Experimental data for the paper "analyzing and predicting verification of data-aware process models -- a case study with spectrum auctions"

doi:doi:10.35097/1298

Experimental data for the paper "analyzing and predicting verification of data-aware process models -- a case study with spectrum auctions"

Abstract: These are the experimental data for the paper

Ordoni, Elaheh, Jakob Bach, and Ann-Katrin Fleck. "Analyzing and Predicting Verification of Data-Aware Process Models--A Case Study With Spectrum Auctions"

published by IEEE Access in 2022. You can find the paper here and the code here. See the README for details.

From the raw experimental data, we also extracted and pre-processed a smaller dataset that is suitable for training prediction models. This prediction dataset is available under the name Auction Verification in the UCI Machine Learning Repository. TechnicalRemarks: These are the experimental data for the paper

Ordoni, Elaheh, Jakob Bach, and Ann-Katrin Fleck. "Analyzing and Predicting Verification of Data-Aware Process Models -- a Case Study with Spectrum Auctions"

Check our GitHub repository for the code and instructions to reproduce the experiments.

result[0-5].csv: The output of the iterative verification procedure, input to prepare_dataset.py (which pre-processes and consolidates the dataset).
auction_verification_large.csv: The output of prepare_dataset.py (consolidated dataset), input to run_experiments.py (the experimental pipeline).
prediction_results.csv: The output of run_experiments.py (full numeric experimental results), input to run_evaluation.py (which prints statistics and creates the plots for the paper).

BibTex:

@dataset{Ordoni_Elaheh_and_Bach_Jakob_and_Fleck_Ann-Katrin_2023,
    abstract = {Abstract: These are the experimental data for the paper

> Ordoni, Elaheh, Jakob Bach, and Ann-Katrin Fleck. "Analyzing and Predicting Verification of Data-Aware Process Models--A Case Study With Spectrum Auctions"

published by [*IEEE Access*](https://ieeeaccess.ieee.org/) in 2022.
You can find the paper [here](https://www.doi.org/10.1109/ACCESS.2022.3154445) and the code [here](https://github.com/Jakob-Bach/Analyzing-Auction-Verification).
See the `README` for details.

From the raw experimental data, we also extracted and pre-processed a smaller dataset that is suitable for training prediction models.
This prediction dataset is available under the name `Auction Verification` in the [UCI Machine Learning Repository](https://archive-beta.ics.uci.edu/ml/datasets/auction+verification).
TechnicalRemarks: These are the experimental data for the paper

> Ordoni, Elaheh, Jakob Bach, and Ann-Katrin Fleck. "Analyzing and Predicting Verification of Data-Aware Process Models -- a Case Study with Spectrum Auctions"

Check our [GitHub repository](https://github.com/Jakob-Bach/Analyzing-Auction-Verification) for the code and instructions to reproduce the experiments.

- `result[0-5].csv`: The output of the iterative verification procedure, input to `prepare_dataset.py` (which pre-processes and consolidates the dataset).
- `auction_verification_large.csv`: The output of `prepare_dataset.py` (consolidated dataset), input to `run_experiments.py` (the experimental pipeline).
- `prediction_results.csv`: The output of `run_experiments.py` (full numeric experimental results), input to `run_evaluation.py` (which prints statistics and creates the plots for the paper).},
    author = {Ordoni, Elaheh and Bach, Jakob and Fleck, Ann-Katrin},
    doi = {10.35097/1298},
    institution = {RADAR},
    keyword = {'formal verification', 'machine learning', 'model checking', 'spectrum auctions'},
    title = {Experimental data for the paper "analyzing and predicting verification of data-aware process models -- a case study with spectrum auctions"},
    url = {https://service.tib.eu/ldmservice/vdataset/rdr-doi-10-35097-1298},
    year = {2023}
}