2026-10-11T20:33:46Zhttps://www.radar-service.eu/oai/OAIHandler

10.35097/12982026-10-09T10:54:18Zradar4kit

https://dx.doi.org/10.35097/1298 Ordoni, Elaheh Bach, Jakob Fleck, Ann-Katrin Experimental data for the paper "Analyzing and Predicting Verification of Data-Aware Process Models -- a Case Study with Spectrum Auctions" Karlsruhe Institute of Technology 2023 Computer Science dataset Dataset info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode 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). 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). formal verification machine learning model checking spectrum auctions 10.5445/IR/1000142949 KITopen-DOI https://publikationen.bibliothek.kit.edu/1000142949 application/x-tar