Alternativer Identifier:
(KITopen-DOI) 10.5445/IR/1000142949
Verwandter Identifier:
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Ersteller/in:
Ordoni, Elaheh [Ordoni, Elaheh]

Bach, Jakob https://orcid.org/0000-0003-0301-2798 [Bach, Jakob]

Fleck, Ann-Katrin https://orcid.org/0000-0001-8842-8906 [Fleck, Ann-Katrin]
Beitragende:
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Titel:
Experimental data for the paper "Analyzing and Predicting Verification of Data-Aware Process Models -- a Case Study with Spectrum Auctions"
Weitere Titel:
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Beschreibung:
(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).
(Technical Remarks) 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).
Schlagworte:
formal verification
machine learning
model checking
spectrum auctions
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Sprache:
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Erstellungsjahr:
Fachgebiet:
Computer Science
Objekttyp:
Dataset
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kitopen
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Archivierungsdatum:
2023-06-21
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56,6 MB
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kitopen
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