Big Data-Driven Industry 4.0 Service Engineering Large-Scale Trials: The Boost 4.0 Experience

Oscar Lázaro, Jesús Alonso, Paulo Figueiras, Ruben Costa, Diogo Graça, Gisela Garcia, Alessandro Canepa, Caterina Calefato, Marco Vallini, Fabiana Fournier, Nathan Hazout, Inna Skarbovsky, Athanasios Poulakidas, Konstantinos Sipsas

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

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Abstract

In 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.
Original languageEnglish
Title of host publicationTechnologies and Applications for Big Data Value
EditorsEdward Curry, Sören Auer, Arne J. Berre, Andreas Metzger, Maria S. Perez, Sonja Zillner
Place of PublicationCham
PublisherSpringer
Pages373-397
Number of pages25
ISBN (Electronic)978-3-030-78307-5
ISBN (Print)978-3-030-78306-8
DOIs
Publication statusPublished - Apr 2022

Keywords

  • Reference architecture
  • ISO 20547
  • ISO/IEC/IEEE 42010
  • DIN 27070
  • Sovereignty
  • Data spaces
  • Track & Trace
  • Blockchain
  • FIWARE
  • Virtual commissioning
  • Testbed
  • Trial
  • Business networks 4.0
  • SUMA 4.0
  • Intralogistics

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