Towards a systematic data harmonization to enable AI application in the process industry

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dc.contributor.authorWiedau, Michael
dc.contributor.authorTolksdorf, Gregor
dc.contributor.authorOeing, Jonas
dc.contributor.authorKockmann, Norbert
dc.date.accessioned2022-03-04T13:57:35Z
dc.date.available2022-03-04T13:57:35Z
dc.date.issued2021-11-15
dc.description.abstractCurrent methods of artificial intelligence may often proof ineffective in the process industry, usually because of insufficient data availability. In this contribution, we investigate how data standards can contribute to fulfill the data availability requirements of machine learning methods. We give an overview of AI use cases relevant in the process industry, name related requirements and discuss known standards in the context of implicit vs. explicit data. We conclude with a roadmap sketching how to bring the results of this contribution into practical application.en
dc.identifier.urihttp://hdl.handle.net/2003/40765
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-22622
dc.language.isoende
dc.relation.ispartofseriesChemie - Ingenieur - Technik;93(12)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectArtificial intelligenceen
dc.subjectData integrationen
dc.subjectKEEN projecten
dc.subjectOntologyen
dc.subjectProcess industryen
dc.subject.ddc660
dc.titleTowards a systematic data harmonization to enable AI application in the process industryen
dc.typeTextde
dc.type.publicationtypearticlede
dcterms.accessRightsopen access
eldorado.secondarypublicationtruede
eldorado.secondarypublication.primarycitationWiedau, M., Tolksdorf, G., Oeing, J. and Kockmann, N. (2021), Towards a Systematic Data Harmonization to Enable AI Application in the Process Industry. Chemie Ingenieur Technik, 93: 2105-2115. https://doi.org/10.1002/cite.202100203de
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1002/cite.202100203de

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