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Big Data-Driven Industry 4.0 Service Engineering Large-Scale Trials

2022-04Capítulo de livro Acesso aberto
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dc.contributor.authorLázaro, Oscar
dc.contributor.authorAlonso, Jesús
dc.contributor.authorFigueiras, Paulo
dc.contributor.authorCosta, Ruben
dc.contributor.authorGraça, Diogo
dc.contributor.authorGarcia, Gisela
dc.contributor.authorCanepa, Alessandro
dc.contributor.authorCalefato, Caterina
dc.contributor.authorVallini, Marco
dc.contributor.authorFournier, Fabiana
dc.contributor.authorHazout, Nathan
dc.contributor.authorSkarbovsky, Inna
dc.contributor.authorPoulakidas, Athanasios
dc.contributor.authorSipsas, Konstantinos
dc.contributor.institutionUNINOVA-Instituto de Desenvolvimento de Novas Tecnologias
dc.coverage.spatialCham
dc.date.accessioned2023-03-09T22:14:55Z
dc.date.available2023-03-09T22:14:55Z
dc.date.issued2022-04
dc.description
dc.description.abstractIn the last few years, the potential impact of big data on the manufacturing industry has received enormous attention. This chapter details two large-scale trials that have been implemented in the context of the lighthouse project Boost 4.0. The chapter introduces the Boost 4.0 Reference Model, which adapts the more generic BDVA big data reference architectures to the needs of Industry 4.0. The Boost 4.0 reference model includes a reference architecture for the design and implementation of advanced big data pipelines and the digital factory service development reference architecture. The engineering and management of business network track and trace processes in high-end textile supply are explored with a focus on the assurance of Preferential Certification of Origin (PCO). Finally, the main findings from these two large-scale piloting activities in the area of service engineering are discussed.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent25
dc.format.extent4192917
dc.identifier.doi10.1007/978-3-030-78307-5_17
dc.identifier.isbn978-3-030-78306-8
dc.identifier.isbn978-3-030-78307-5
dc.identifier.otherPURE: 55160311
dc.identifier.otherPURE UUID: ce50c0c0-8ef5-405c-bb8f-127fab577a82
dc.identifier.urihttp://hdl.handle.net/10362/150247
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/780732/EU
dc.relationBig Data Value Spaces for COmpetitiveness of European COnnected Smart FacTories 4.0
dc.subjectReference architecture
dc.subjectISO 20547
dc.subjectISO/IEC/IEEE 42010
dc.subjectDIN 27070
dc.subjectSovereignty
dc.subjectData spaces
dc.subjectTrack & Trace
dc.subjectBlockchain
dc.subjectFIWARE
dc.subjectVirtual commissioning
dc.subjectTestbed
dc.subjectTrial
dc.subjectBusiness networks 4.0
dc.subjectSUMA 4.0
dc.subjectIntralogistics
dc.titleBig Data-Driven Industry 4.0 Service Engineering Large-Scale Trialsen
dc.title.subtitleThe Boost 4.0 Experienceen
dc.typebook part
degois.publication.firstPage373
degois.publication.lastPage397
degois.publication.titleTechnologies and Applications for Big Data Value
dspace.entity.typePublication
oaire.awardNumber780732
oaire.awardTitleBig Data Value Spaces for COmpetitiveness of European COnnected Smart FacTories 4.0
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/780732/EU
oaire.fundingStreamH2020
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.nameEuropean Commission
rcaap.rightsopenAccess
relation.isProjectOfPublicationd7adc4a2-8c96-463e-8aaf-f4c8f9c765fe
relation.isProjectOfPublication.latestForDiscoveryd7adc4a2-8c96-463e-8aaf-f4c8f9c765fe

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