Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/45638

TitleA big data analytics architecture for industry 4.0
Author(s)Santos, Maribel Yasmina
Sá, Jorge Oliveira e
Costa, Carlos
Galvão, João
Andrade, Carina
Martinho, Bruno
Lima, Francisca Vale
Costa, Eduarda
KeywordsIndustry 4.0
Big Data
Big Data Architecture
Big Data Analytics
Issue dateApr-2017
PublisherSpringer Verlag
JournalAdvances in Intelligent Systems and Computing
CitationSantos, Maribel Yasmina, Jorge Oliveira e Sá, Carlos Costa, João Galvão, Carina Andrade, Bruno Martinho, Francica Vale Lima and Eduarda Costa, “A Big Data Analytics Architecture for Industry 4.0”, In: Rocha Á., Correia A., Adeli H., Reis L., Costanzo S. (Eds.), Recent Advances in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol 570, ISBN: 978-3-319-56537-8, Springer, Cham (DOI: 10.1007/978-3-319-56538-5_19).
Abstract(s)In an era in which people, devices, infrastructures and sensors can constantly communicate exchanging data and, also, generating new data that traces many of these exchanges, vast volumes of data is generated giving the context for the emergence of the Big Data concept. In particular, recent devel‐ opments in Information and Communications Technology (ICT) are pushing the fourth industrial revolution, Industry 4.0, being data generated by several sources like machine controllers, sensors, manufacturing systems, among others. Joining the volume and variety of data, arriving at high velocity, with Industry 4.0, makes the opportunity to enhance sustainable innovation in the Factories of the future. In this, the collection, integration, storage, processing and analysis of data is a key challenge, being Big Data systems needed to link all the entities and data needs of the factory. In this context, this paper proposes a Big Data Analytics architecture that includes layers dedicated to deal with all data needs, from collec‐ tion to analysis and distribution.
TypeConference paper
URIhttp://hdl.handle.net/1822/45638
ISBN978-3-319-56537-8
DOI10.1007/978-3-319-56538-5_19
ISSN2194-5357
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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