Abstract | ||
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People, devices, infrastructures and sensors can constantly communicate exchanging data and generating new data that trace many of these exchanges. This leads to vast volumes of data collected at ever increasing velocities and of different variety, a phenomenon currently known as Big Data. In particular, recent developments in Information and Communications Technologies are pushing the fourth industrial revolution, Industry 4.0, being data generated by several sources like machine controllers, sensors, manufacturing systems, among others. Joining volume, variety and velocity of data, 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. Thereby, this paper addresses this key challenge, proposing and implementing a Big Data Analytics architecture, using a multinational organisation (Bosch Car Multimedia – Braga) as a case study. In this work, all the data lifecycle, from collection to analysis, is handled, taking into consideration the different data processing speeds that can exist in the real environment of a factory (batch or stream). |
Year | DOI | Venue |
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2017 | 10.1016/j.ijinfomgt.2017.07.012 | International Journal of Information Management |
Keywords | Field | DocType |
Big Data,Industry 4.0,Big Data analytics,Big Data architecture,Bosch | Data science,Data architecture,Data processing,Architecture,Multinational corporation,Factory,Data analysis,Knowledge management,Engineering,Industry 4.0,Big data | Journal |
Volume | Issue | ISSN |
37 | 6 | 0268-4012 |
Citations | PageRank | References |
14 | 0.69 | 12 |
Authors | ||
8 |
Name | Order | Citations | PageRank |
---|---|---|---|
Maribel Yasmina Santos | 1 | 146 | 35.41 |
Jorge Oliveira E Sá | 2 | 27 | 4.32 |
Carina Andrade | 3 | 23 | 3.57 |
Francisca Vale Lima | 4 | 14 | 0.69 |
Eduarda Costa | 5 | 15 | 1.06 |
Carlos Costa | 6 | 38 | 9.15 |
Bruno Martinho | 7 | 14 | 0.69 |
João Galvão | 8 | 14 | 0.69 |