Verification of Data-Value-Aware Processes and a Case Study on Spectrum Auctions

Ordoni, Elaheh; Mülle, Jutta A.; Böhm, Klemens

doi:10.1109/cbi49978.2020.00027

Cited by 2 publications

(4 citation statements)

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“…data-aware process models. With such techniques applied to auctions, one can detect the lowest possible revenue of an auctioneer or the lowest efficiency of an auction design [10], to give examples. However, respective techniques tend to be computationally expensive: First, the state space grows exponentially with the number of data objects.…”

Section: ) Auction Theorymentioning

confidence: 99%

“…Authors in [10] studied the verification of data-aware process models that support modification of data values. However, their approach only allows verifying spectrum auctions with at most three bidders and products.…”

Section: B Verification Of Process Modelsmentioning

confidence: 99%

“…Here, the idea is to determine the values of data objects necessary for verification and combine all the unnecessary values into an abstracted one. The problem is that such techniques might yield an incorrect result [10]. This outcome may happen when process elements such as activities modify the value of data objects, e.g., an activity that increases the price of a product.…”

Section: B Verification Of Process Modelsmentioning

confidence: 99%

“…Given the reduced process models, we transform them to Petri Nets with the rules from [10]. We use plain Petri Nets as a target for the mapping because of the availability of efficient analysis techniques [46].…”

Section: Transformation Of Process Models To Petri Netsmentioning

confidence: 99%

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Analyzing and Predicting Verification of Data-Aware Process Models–A Case Study With Spectrum Auctions

2022

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Verification techniques play an essential role in detecting undesirable behaviors in many applications like spectrum auctions. By verifying an auction design, one can detect the least favorable outcomes, e.g., the lowest revenue of an auctioneer. However, verification may be infeasible in practice, given the vast size of the state space on the one hand and the large number of properties to be verified on the other hand. To overcome this challenge, we leverage machine-learning techniques. In particular, we create a dataset by verifying properties of a spectrum auction first. Second, we use this dataset to analyze and predict outcomes of the auction and characteristics of the verification procedure. To evaluate the usefulness of machine learning in the given scenario, we consider prediction quality and feature importance. In our experiments, we observe that prediction models can capture relationships in our dataset well, though one needs to be careful to obtain a representative and sufficiently large training dataset. While the focus of this article is on a specific verification scenario, our analysis approach is general and can be adapted to other domains. INDEX TERMSFormal verification, Machine learning, Model checking, Spectrum auctions I. INTRODUCTION A. MOTIVATION

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Section: ) Auction Theorymentioning

confidence: 99%

Section: B Verification Of Process Modelsmentioning

confidence: 99%

Section: B Verification Of Process Modelsmentioning

confidence: 99%

Section: Transformation Of Process Models To Petri Netsmentioning

confidence: 99%

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