A big data system supporting Bosch Braga Industry 4.0 strategy
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Abstract 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).
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Data warehouses and distributed file systems are ideal mediums for storing data for big data systems [32,48,49] that receive data from multiple data sources.
“Data warehouses and distributed file systems are ideal mediums for storing data for big data systems [32,48,49] that receive data from multiple data sources.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [32]
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References 40 · Cited by 207
- Authors
- Santos, M. Y., Oliveira e Sá, J., Andrade, C., Vale Lima, F., Costa, E., Costa, C., Martinho, B., & Galvão, J.
- Affiliations
- University of Minho (PT)
- Corresponding author
- Galvão, João
- Venue
- International Journal of Information Management
- Publisher
- Elsevier BV
- Published
- 2017-12
- Volume
- 37
- Issue
- 6
- Pages
- 750-760
- ISSN
- 0268-4012 (ISSN-L)
- OpenAlex
- W2746007326
- Semantic Scholar
- bd6e0eded16cfe1526330c31971e6b92f968bae0
- Cited by
- 207
- References
- 40
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 15 · 2023: 23 · 2022: 29 · 2021: 36 · 2020: 29 · 2019: 34 · 2018: 22 · 2017: 1
- FWCI
- 25.82
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- International Journal of Information Management (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- RepositóriUM (Universidade do Minho) (submittedVersion, cc-by)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion)
- Funders
- COMPETE
- FCT (Fundação para a Ciência e Tecnologia)
- Portugal Incentive System for Research and Technological Development
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Keywords
- big data systems, Industry 4.0, factory of the future, data lifecycle, Bosch Car Multimedia, production data integration, sensor data, sustainable innovation
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