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Big Data on Machine to Machine Integration’s Requirement Analysis Within Industry 4.0

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Technological Innovation for Industry and Service Systems (DoCEIS 2019)

Abstract

One of the foundations for Industry 4.0 is the integration of various industrial elements (i.e. sensors, machines, and services) so that these devices can decide in a relatively autonomous way the level of integration which will be adopted. Thus, it is important to understand how the communication Machine to Machine is effectively realized and how these data can be explored and used to enhance the manufacturing process. The exchange of information between machines in the industrial process represents a potential to acquire and analyze a mass of data characterized as “big data”, which can be perceived as an opportunity to discuss the paradigms of the industrial systems. Therefore, the purpose of this research is to identify the requirements for the Machine to Machine communication and the use of this data/information for more complexes analyzes using big data and analytics techniques. The KAOS methodology was utilized to model these requirements.

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Acknowledgement

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal no Nível Superior - Brazil (CAPES), Fundação de Amparo à Pesquisa do Estado de São Paulo - Brazil (FAPESP), and Conselho Nacional de Desenvolvimento Científico e Tecnológico - Brazil (CNPq).

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Correspondence to Felipe A. Coda , Rafael M. Salles , Henrique A. Vitoi , Marcosiris A. O. Pessoa , Diolino J. Santos Filho , Fabrício Junqueira or Paulo E. Miyagi .

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Coda, F.A. et al. (2019). Big Data on Machine to Machine Integration’s Requirement Analysis Within Industry 4.0. In: Camarinha-Matos, L., Almeida, R., Oliveira, J. (eds) Technological Innovation for Industry and Service Systems. DoCEIS 2019. IFIP Advances in Information and Communication Technology, vol 553. Springer, Cham. https://doi.org/10.1007/978-3-030-17771-3_21

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  • DOI: https://doi.org/10.1007/978-3-030-17771-3_21

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-17771-3

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