Abstract
Industry 4.0, characterized by the development of automation and data exchanging technologies, has contributed to an increase in the volume of data, generated from various data sources, with great speed and variety. Organizations need to collect, store, process, and analyse this data in order to extract meaningful insights from these vast amounts of data. By overcoming these challenges imposed by what is currently known as Big Data, organizations take a step towards optimizing business processes. This paper proposes a Big Data Analytics architecture as an artefact for the integration of historical data - from the organizational business processes - and predictive data - obtained by the use of Machine Learning models -, providing an advanced data analytics environment for decision support. To support data integration in a Big Data Warehouse, a data modelling method is also proposed. These proposals were implemented and validated with a demonstration case in a multinational organization, Bosch Car Multimedia in Braga. The obtained results highlight the ability to take advantage of large amounts of historical data enhanced with predictions that support complex decision support scenarios.
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Acknowledgements
This work has been supported by FCT – Fundação para a Ciên-cia e Tecnologia within the Project Scope: UIDB/00319/2020, the Doctoral scholarships PD/BDE/135100/2017 and PD/BDE/135105/2017, and European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project nº 039479; Funding Reference: POCI-01-0247-FEDER-039479]. The authors also wish to thank the automotive electronics company staff involved with this project for providing the data and valuable domain feedback. This paper uses icons made by Freepik, from www.flaticon.com.
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Galvão, J. et al. (2022). Bosch’s Industry 4.0 Advanced Data Analytics: Historical and Predictive Data Integration for Decision Support. In: Guizzardi, R., Ralyté, J., Franch, X. (eds) Research Challenges in Information Science. RCIS 2022. Lecture Notes in Business Information Processing, vol 446. Springer, Cham. https://doi.org/10.1007/978-3-031-05760-1_34
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