Utilize este identificador para referenciar este registo: http://hdl.handle.net/10362/150247
Título: Big Data-Driven Industry 4.0 Service Engineering Large-Scale Trials
Autor: Lázaro, Oscar
Alonso, Jesús
Figueiras, Paulo
Costa, Ruben
Graça, Diogo
Garcia, Gisela
Canepa, Alessandro
Calefato, Caterina
Vallini, Marco
Fournier, Fabiana
Hazout, Nathan
Skarbovsky, Inna
Poulakidas, Athanasios
Sipsas, Konstantinos
Palavras-chave: 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
Data: Abr-2022
Editora: Springer
Citação: Lázaro, O., Alonso, J., Figueiras, P., Costa, R., Graça, D., Garcia, G., Canepa, A., Calefato, C., Vallini, M., Fournier, F., Hazout, N., Skarbovsky, I., Poulakidas, A., & Sipsas, K. (2022). Big Data-Driven Industry 4.0 Service Engineering Large-Scale Trials: The Boost 4.0 Experience. In E. Curry, S. Auer, A. J. Berre, A. Metzger, M. S. Perez, & S. Zillner (Eds.), Technologies and Applications for Big Data Value (pp. 373-397). Springer. https://doi.org/10.1007/978-3-030-78307-5_17
Resumo: 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.
Peer review: yes
URI: http://hdl.handle.net/10362/150247
DOI: https://doi.org/10.1007/978-3-030-78307-5_17
ISBN: 978-3-030-78306-8
978-3-030-78307-5
Aparece nas colecções:Home collection (FCT)

Ficheiros deste registo:
Ficheiro Descrição TamanhoFormato 
Big_Data_Driven_Industry_4.0_Service.pdf4,09 MBAdobe PDFVer/Abrir


FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpace
Formato BibTex MendeleyEndnote 

Todos os registos no repositório estão protegidos por leis de copyright, com todos os direitos reservados.