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2022 | OriginalPaper | Chapter

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

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

Published in: Research Challenges in Information Science

Publisher: 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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Literature
1.
go back to reference Wang, L., Alexander, C.A.: Machine learning in big data. Int. J. Math. Eng. Manag. Sci. 1, 52–66 (2016) Wang, L., Alexander, C.A.: Machine learning in big data. Int. J. Math. Eng. Manag. Sci. 1, 52–66 (2016)
2.
go back to reference Alswedani, S., Saleh, M.: Big data analytics: importance, challenges, categories, techniques, and tools. J. Adv. Trends Comput. Sci. Eng. 9, 5384–5392 (2020)CrossRef Alswedani, S., Saleh, M.: Big data analytics: importance, challenges, categories, techniques, and tools. J. Adv. Trends Comput. Sci. Eng. 9, 5384–5392 (2020)CrossRef
3.
go back to reference Alsghaier, H.: The importance of big data analytics in business: a case study. Am. J. Softw. Eng. Appl. 6, 111–115 (2017) Alsghaier, H.: The importance of big data analytics in business: a case study. Am. J. Softw. Eng. Appl. 6, 111–115 (2017)
4.
go back to reference Rialti, R., Marzi, G., Caputo, A., Mayah, K.A.: Achieving strategic flexibility in the era of big data: the importance of knowledge management and ambidexterity. Manag. Decis. 58, 1585–1600 (2020) Rialti, R., Marzi, G., Caputo, A., Mayah, K.A.: Achieving strategic flexibility in the era of big data: the importance of knowledge management and ambidexterity. Manag. Decis. 58, 1585–1600 (2020)
5.
go back to reference Gao, R.X., Wang, L., Helu, M., Teti, R.: Big data analytics for smart factories of the future. CIRP Ann. 69, 668–692 (2020)CrossRef Gao, R.X., Wang, L., Helu, M., Teti, R.: Big data analytics for smart factories of the future. CIRP Ann. 69, 668–692 (2020)CrossRef
6.
go back to reference Papageorgiou, L., Eleni, P., Raftopoulou, S., Mantaiou, M., Megalooikonomou, V., Vlachakis, D.: Genomic big data hitting the storage bottleneck. EMBnet J. 24, e910 (2018)CrossRef Papageorgiou, L., Eleni, P., Raftopoulou, S., Mantaiou, M., Megalooikonomou, V., Vlachakis, D.: Genomic big data hitting the storage bottleneck. EMBnet J. 24, e910 (2018)CrossRef
7.
go back to reference Chavalier, M., El Malki, M., Kopliku, A., Teste, O., Tournier, R.: Document-oriented data warehouses: models and extended cuboids, extended cuboids in oriented document. In: Proceedings - Conference on Research Challenges in Information Science, August 2016 Chavalier, M., El Malki, M., Kopliku, A., Teste, O., Tournier, R.: Document-oriented data warehouses: models and extended cuboids, extended cuboids in oriented document. In: Proceedings - Conference on Research Challenges in Information Science, August 2016
8.
go back to reference Cuzzocrea, A., Song, I.Y., Davis, K.C.: Analytics over large-scale multidimensional data: the big data revolution! In: Conference on Information and Knowledge Management (2011) Cuzzocrea, A., Song, I.Y., Davis, K.C.: Analytics over large-scale multidimensional data: the big data revolution! In: Conference on Information and Knowledge Management (2011)
9.
go back to reference Santos, M.Y., Costa, C.: Big data: concepts, warehousing and analytics. River (2020) Santos, M.Y., Costa, C.: Big data: concepts, warehousing and analytics. River (2020)
12.
go back to reference Elshawi, R., Sakr, S., Talia, D., Trunfio, P.: Big data systems meet machine learning challenges: towards big data science as a service. Big Data Res. 14, 1–11 (2018)CrossRef Elshawi, R., Sakr, S., Talia, D., Trunfio, P.: Big data systems meet machine learning challenges: towards big data science as a service. Big Data Res. 14, 1–11 (2018)CrossRef
13.
go back to reference Syafrudin, M., Alfian, G., Fitriyani, N.L., Rhee, J.: Performance analysis of IoT-based sensor, big data processing, and machine learning model for real-time monitoring system in automotive manufacturing. Sensors 18, 2946 (2018)CrossRef Syafrudin, M., Alfian, G., Fitriyani, N.L., Rhee, J.: Performance analysis of IoT-based sensor, big data processing, and machine learning model for real-time monitoring system in automotive manufacturing. Sensors 18, 2946 (2018)CrossRef
14.
go back to reference Lee, J., Ardakani, H.D., Yang, S., Bagheri, B.: Industrial big data analytics and cyber-physical systems for future maintenance & service innovation. Procedia CIRP 38, 3–7 (2015)CrossRef Lee, J., Ardakani, H.D., Yang, S., Bagheri, B.: Industrial big data analytics and cyber-physical systems for future maintenance & service innovation. Procedia CIRP 38, 3–7 (2015)CrossRef
15.
go back to reference Baldominos, A., Albacete, E., Saez, Y., Isasi, P.: A scalable machine learning online service for big data real-time analysis. In: 2014 IEEE Computational Intelligence in Big Data (2014) Baldominos, A., Albacete, E., Saez, Y., Isasi, P.: A scalable machine learning online service for big data real-time analysis. In: 2014 IEEE Computational Intelligence in Big Data (2014)
16.
go back to reference Krishnamoorthy, R., Udhayakumar, K.: Futuristic bigdata framework with optimization techniques for wind energy resource assessment and management in smart grid. In: 2021 7th International Conference on Electrical Energy Systems (ICEES), pp. 507–514 (2021) Krishnamoorthy, R., Udhayakumar, K.: Futuristic bigdata framework with optimization techniques for wind energy resource assessment and management in smart grid. In: 2021 7th International Conference on Electrical Energy Systems (ICEES), pp. 507–514 (2021)
17.
go back to reference Montoya-Torres, J.R., Moreno, S., Guerrero, W.J., Mejía, G.: Big data analytics and intelligent transportation systems. IFAC-PapersOnLine 54, 216–220 (2021)CrossRef Montoya-Torres, J.R., Moreno, S., Guerrero, W.J., Mejía, G.: Big data analytics and intelligent transportation systems. IFAC-PapersOnLine 54, 216–220 (2021)CrossRef
18.
go back to reference Cai, L., Zhu, Y.: The challenges of data quality and data quality assessment in the big data era. Data Sci. J. 14, 1683–1470 (2015) Cai, L., Zhu, Y.: The challenges of data quality and data quality assessment in the big data era. Data Sci. J. 14, 1683–1470 (2015)
19.
go back to reference Dehghani, Z.: How to move beyond a monolithic data lake to a distributed data mesh (2019) Dehghani, Z.: How to move beyond a monolithic data lake to a distributed data mesh (2019)
24.
go back to reference Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 413–422 (2008) Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 413–422 (2008)
25.
go back to reference Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006)MathSciNetCrossRef Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006)MathSciNetCrossRef
26.
go back to reference Alla, S., Adari, S.K.: Traditional Methods of Anomaly Detection. Apress, Berkeley (2019) Alla, S., Adari, S.K.: Traditional Methods of Anomaly Detection. Apress, Berkeley (2019)
Metadata
Title
Bosch’s Industry 4.0 Advanced Data Analytics: Historical and Predictive Data Integration for Decision Support
Authors
João Galvão
Diogo Ribeiro
Inês Machado
Filipa Ferreira
Júlio Gonçalves
Rui Faria
Guilherme Moreira
Carlos Costa
Paulo Cortez
Maribel Yasmina Santos
Copyright Year
2022
DOI
https://doi.org/10.1007/978-3-031-05760-1_34

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