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Experimental data for the paper "Analyzing and Predicting Verification of Data-Aware Process Models -- a Case Study with Spectrum Auctions"

Ordoni, Elaheh; Bach, Jakob ORCID iD icon; Fleck, Ann-Katrin ORCID iD icon

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).


Zugehörige Institution(en) am KIT Institut für Programmstrukturen und Datenorganisation (IPD)
Institut für Volkswirtschaftslehre (ECON)
Publikationstyp Forschungsdaten
Publikationsdatum 14.02.2022
Erstellungsdatum 10.02.2022
Identifikator DOI: 10.5445/IR/1000142949
KITopen-ID: 1000142949
Lizenz Creative Commons Namensnennung 4.0 International
Externe Relationen Forschungsdaten/Software
Schlagwörter formal verification, machine learning, model checking, spectrum auctions
Liesmich

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).
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