Data Trading Similarity Signature An Extended Data Trading Framework for Human and Non-Human Actors

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dc.contributor.authorLawrenz, Sebastian
dc.contributor.authorPoschmann, Hendrik
dc.contributor.authorRausch , Andreas
dc.contributor.authorStein , Vera
dc.date.accessioned2021-12-24T18:04:06Z
dc.date.available2021-12-24T18:04:06Z
dc.date.issued2022-01-04
dc.description.abstractFair and secure data trading is one of the most prominent challenges of the 21st century. This paper presents a second iteration of an approach to develop a data marketplace concept by checking consumer requirements. The main problem we identified is data quality and the question: Would a dataset fulfill the consumer requirements? Starting from an approach that uses a binary response set to answer the question of whether requirements are met, we concluded that a description of consumer requirements needs to be quantitatively comparable. The novel approach presented here identifies similarities between datasets and consumer requirements. It forms a unique, fingerprint-like similarity signature for each dataset, which can be interpreted by both human and non-human actors. The approach is deducted and designed by using the Design Science Research Methodology and discussed critically in the end.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2022.600
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/79937
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectSocial Shopping: The Good, the Bad, and the Ugly
dc.subjectautoencoder
dc.subjectdata marketplace
dc.subjectdata quality
dc.subjectdata trading
dc.subjectdesign science
dc.titleData Trading Similarity Signature An Extended Data Trading Framework for Human and Non-Human Actors
dc.type.dcmitext

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