SemDaServ: A Systematic Approach for Semantic Data Specification of AI-Based Smart Service Systems

dc.contributor.authorPreidel, Maurice
dc.contributor.authorStark, Rainer
dc.date.accessioned2021-07-09T13:21:43Z
dc.date.available2021-07-09T13:21:43Z
dc.date.issued2021-06-01
dc.date.updated2021-07-03T15:03:12Z
dc.description.abstractTo develop smart services to successfully operate as a component of smart service systems (SSS), they need qualitatively and quantitatively sufficient data. This is especially true when using statistical methods from the field of artificial intelligence (AI): training data quality directly determines the quality of resulting AI models. However, AI model quality is only known when AI training can take place. Additionally, the creation of not yet available data sources (e.g., sensors) takes time. Therefore, systematic specification is needed alongside SSS development. Today, there is a lack of systematic support for specifying data relevant to smart services. This gap can be closed by realizing the systematic approach SemDaServ presented in this article. The research approach is based on Blessing’s Design Research Methodology (literature study, derivation of key factors, success criteria, solution functions, solution development, applicability evaluation). SemDaServ provides a three-step process and five accompanying artifacts. Using domain knowledge for data specification is critical and creates additional challenges. Therefore, the SemDaServ approach systematically captures and semantically formalizes domain knowledge in SysML-based models for information and data. The applicability evaluation in expert interviews and expert workshops has confirmed the suitability of SemDaServ for data specification in the context of SSS development. SemDaServ thus offers a systematic approach to specify the data requirements of smart services early on to aid development to continuous integration and continuous delivery scenarios.en
dc.description.sponsorshipDFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berlinde
dc.identifier.eissn2076-3417
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/13392
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-12176
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc600 Technik, Technologiede
dc.subject.othersmart servicesen
dc.subject.otherdata specificationen
dc.subject.otherdomain knowledgeen
dc.subject.otherinformation needsen
dc.subject.otherdata needsen
dc.subject.otherknowledge needsen
dc.subject.otherdata qualityen
dc.subject.othersmart service systems engineeringen
dc.titleSemDaServ: A Systematic Approach for Semantic Data Specification of AI-Based Smart Service Systemsen
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.articlenumber5148en
dcterms.bibliographicCitation.doi10.3390/app11115148en
dcterms.bibliographicCitation.issue11en
dcterms.bibliographicCitation.journaltitleApplied Sciencesen
dcterms.bibliographicCitation.originalpublishernameMDPIen
dcterms.bibliographicCitation.originalpublisherplaceBaselen
dcterms.bibliographicCitation.volume11en
tub.accessrights.dnbfreeen
tub.affiliationFak. 5 Verkehrs- und Maschinensysteme>Inst. Werkzeugmaschinen und Fabrikbetrieb>FG Industrielle Informationstechnikde
tub.affiliation.facultyFak. 5 Verkehrs- und Maschinensystemede
tub.affiliation.groupFG Industrielle Informationstechnikde
tub.affiliation.instituteInst. Werkzeugmaschinen und Fabrikbetriebde
tub.publisher.universityorinstitutionTechnische Universität Berlinen
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