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2022 | OriginalPaper | Buchkapitel

Bosch’s Industry 4.0 Advanced Data Analytics: Historical and Predictive Data Integration for Decision Support

verfasst von : João Galvão, Diogo Ribeiro, Inês Machado, Filipa Ferreira, Júlio Gonçalves, Rui Faria, Guilherme Moreira, Carlos Costa, Paulo Cortez, Maribel Yasmina Santos

Erschienen in: Research Challenges in Information Science

Verlag: Springer International Publishing

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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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Metadaten
Titel
Bosch’s Industry 4.0 Advanced Data Analytics: Historical and Predictive Data Integration for Decision Support
verfasst von
João Galvão
Diogo Ribeiro
Inês Machado
Filipa Ferreira
Júlio Gonçalves
Rui Faria
Guilherme Moreira
Carlos Costa
Paulo Cortez
Maribel Yasmina Santos
Copyright-Jahr
2022
DOI
https://doi.org/10.1007/978-3-031-05760-1_34