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

doi:10.1007/978-3-030-78307-5_17

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

UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-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 language	English
Title of host publication	Technologies and Applications for Big Data Value
Editors	Edward Curry, Sören Auer, Arne J. Berre, Andreas Metzger, Maria S. Perez, Sonja Zillner
Place of Publication	Cham
Publisher	Springer
Pages	373-397
Number of pages	25
ISBN (Electronic)	978-3-030-78307-5
ISBN (Print)	978-3-030-78306-8
DOIs	https://doi.org/10.1007/978-3-030-78307-5_17
Publication status	Published - 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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10.1007/978-3-030-78307-5_17Licence: CC BY

Big Data-Driven Industry 4.0 ServiceFinal published version, 4 MBLicence: CC BY

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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

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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.",

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",

author = "Oscar L{\'a}zaro and Jes{\'u}s Alonso and Paulo Figueiras and Ruben Costa and Diogo Gra{\c c}a and Gisela Garcia and Alessandro Canepa and Caterina Calefato and Marco Vallini and Fabiana Fournier and Nathan Hazout and Inna Skarbovsky and Athanasios Poulakidas and Konstantinos Sipsas",

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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, AJ Berre, A Metzger, MS Perez & S Zillner (eds), Technologies and Applications for Big Data Value. Springer, Cham, pp. 373-397. https://doi.org/10.1007/978-3-030-78307-5_17

Big Data-Driven Industry 4.0 Service Engineering Large-Scale Trials: The Boost 4.0 Experience. / Lázaro, Oscar; Alonso, Jesús; Figueiras, Paulo et al.
Technologies and Applications for Big Data Value. ed. / Edward Curry; Sören Auer; Arne J. Berre; Andreas Metzger; Maria S. Perez; Sonja Zillner. Cham: Springer, 2022. p. 373-397.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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AU - Costa, Ruben

AU - Graça, Diogo

AU - Garcia, Gisela

AU - Canepa, Alessandro

AU - Calefato, Caterina

AU - Vallini, Marco

AU - Fournier, Fabiana

AU - Hazout, Nathan

AU - Skarbovsky, Inna

AU - Poulakidas, Athanasios

AU - Sipsas, Konstantinos

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KW - Blockchain

KW - FIWARE

KW - Virtual commissioning

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KW - Business networks 4.0

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A2 - Zillner, Sonja

PB - Springer

CY - Cham

ER -

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

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Keywords

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