Zoom Preditivo: Predição de Interação na Visualização de Dados na I4.0 Utilizando o Princípio de Inovação
Resumo
Atualmente, a quarta revolução industrial, chamada de Indústria 4.0, traz um profundo impacto tanto para os produtores quanto para os consumidores através da geração massiva de dados. Métodos atuais de análise de dados atuam reativamente à interação dos usuários e, consequentemente, não oferecem rápida disponibilidade de informação. O presente trabalho propõe um modelo preditivo para reduzir processamento e tempo de resposta para exploração visual deste grande volume de dados. Utilizando os conceitos de divisão e conquista e de inovação, juntamente com medidas de entropia, o modelo proposto proativamente identifica intervalos para serem explorados em dados temporais.
Palavras-chave:
Indústria 4.0, Predição, Visualização de Dados
Referências
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Shneiderman, B. and Plaisant, C. (2019). Interactive visual event analytics: Opportunities and challenges. Computer, 52(1):27–35. Cited By :1.
Xu, L. D. and Duan, L. (2019). Big data for cyber physical systems in industry 4.0: a survey. Enterprise Information Systems, 13(2):148–169. Cited By :75.
Zhou, K., Liu, T., and Zhou, L. (2016). Industry 4.0: Towards future industrial opportunities and challenges. In 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2015, pages 2147–2152. Cited By :265.
Gabarda, S. and Cristóbal, G. (2010). Detection of events in seismic time series by time-frequency methods. IET Signal Processing, 4(4):413–420. Cited By :26.
Im, J. ., Villegas, F. G., and McGuffln, M. J. (2013). Visreduce: Fast and responsive incremental information visualization of large datasets. In Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013, pages 25–32. Cited By :21.
Lasi, H., Fettke, P., Kemper, H. ., Feld, T., and Hoffmann, M. (2014). Industry 4.0. Business and Information Systems Engineering, 6(4):239–242. Cited By :892.
Lu, Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6:1–10. Cited By :546.
Nedel1, E., Begnini, M., Melchiades, Vinicius, L., Schreiber1, Paredes, C. D., Crovato1, and Righi1, R. d. R. (2019). Compressiot: an optimized compression model for dis- playing a high volume of iot data in web environments. Computer.
Rajkumar, R., Lee, I., Sha, L., and Stankovic, J. (2010). Cyber-physical systems: The next computing revolution. In Proceedings - Design Automation Conference, pages 731–736. Cited By :982.
Ribeiro, L. (2017). Cyber-physical production systems’ design challenges. In IEEE International Symposium on Industrial Electronics, pages 1189–1194. Cited By :16.
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(4):623–656. Cited By :19081.
Shneiderman, B. and Plaisant, C. (2019). Interactive visual event analytics: Opportunities and challenges. Computer, 52(1):27–35. Cited By :1.
Xu, L. D. and Duan, L. (2019). Big data for cyber physical systems in industry 4.0: a survey. Enterprise Information Systems, 13(2):148–169. Cited By :75.
Zhou, K., Liu, T., and Zhou, L. (2016). Industry 4.0: Towards future industrial opportunities and challenges. In 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2015, pages 2147–2152. Cited By :265.
Publicado
18/07/2021
Como Citar
POZENATO, Luis A. Z.; RIGHI, Rodrigo da R.; CROVATO, Cesar D. P.; COSTA, Cristiano A. da; RODRIGUES, Vinícius F..
Zoom Preditivo: Predição de Interação na Visualização de Dados na I4.0 Utilizando o Princípio de Inovação. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 13. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 152-161.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2021.16013.
