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Interoperable Decision Support System Based on Multivariate Time Series for Setup Data Processing and Visualization

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Intelligent Systems Design and Applications (ISDA 2019)

Abstract

Nowadays, interoperable decision support systems play a crucial role to improve production activity control in industrial Companies, and enable them to face the growing exigencies imposed by the arising Industry 4.0 era. In this paper an interoperable decision support system based on multivariate time series for setup data processing and visualization is put forward. The proposed system is described, in the context of a general architecture presented, and its application through an illustrative example from a stamping factory is analysed. Through the case study it is possible to realize about the importance of the proposed system, and its suitability of application to other companies, for instance in other industrial sectors and manufacturing environments.

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References

  1. Ferreira, L., Putnik, G.D., Lopes, N., Garcia, W., Cruz-Cunha, M.M., Castro, H., Varela, M.L.R., Martinho, J., Shah, V., Putnik, Z.: Disruptive data visualization towards zero-defects diagnostics. Procedia CIRP 67, 374–379 (2018)

    Article  Google Scholar 

  2. Vafaei, N., Ribeiro, R.A., Camarinha-Matos, L.M., Valera, L.R.: Normalization techniques for collaborative networks. Kybernetes (2019)

    Google Scholar 

  3. Varela, M.L., Putnik, G.D., Manupati, V.K., Rajyalakshmi, G., Trojanowska, J., Machado, J.: Integrated process planning and scheduling in networked manufacturing systems for I4. 0: a review and framework proposal. Wireless Netw. 1–13 (2019)

    Google Scholar 

  4. Reddy, M.S., Ratnam, C., Agrawal, R., Varela, M.L.R., Sharma, I., Manupati, V.K.: Investigation of reconfiguration effect on makespan with social network method for flexible job shop scheduling problem. Comput. Ind. Eng. 110, 231–241 (2017)

    Article  Google Scholar 

  5. Lu, Y.: Industry 4.0: a survey on technologies, applications and open research issues. J. Ind. Inform. Integr. 6, 1–10 (2017)

    Google Scholar 

  6. Herter, J., Ovtcharova, J.: A model based visualization framework for cross discipline collaboration in industry 4.0 scenarios. Procedia CIRP 57, 398–403 (2016). https://doi.org/10.1016/j.procir.2016.11.069

    Article  Google Scholar 

  7. Sturtevant, D.: Modular architectures make you agile in the long run. IEEE Softw. 35(1), 104–108 (2018). https://doi.org/10.1109/MS.2017.4541034

    Article  Google Scholar 

  8. Powell, T., Schneider, F.: JavaScript The Complete Reference, 3rd Edn. McGraw-Hill Professional (2012). ISBN-13: 978–0071741200

    Google Scholar 

  9. Griffith, C., Wells, L.: Electron: From Beginner to Pro. Apress Publishing (2017). https://doi.org/10.1007/978-1-4842-2826-5

  10. Van Wijk, J.J.: Views on visualization. IEEE Trans. Vis. Comput. Graph. 12(4), 421–432 (2006)

    Article  Google Scholar 

  11. Kuan, J.: Learning Highcharts 4. Packt Publishing (2015). ISBN-13 9781849519083

    Google Scholar 

  12. Sousa, E.A.F., Malheiro, T.E.Q., Bicho, E., Erlhagen, W., Santos, J.A., Pereira, A.F.: MUVTIME: a multivariate time series visualizer for behavioral science. In: 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp. 165–176 (2016)

    Google Scholar 

  13. Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: Craft Information Visualization, pp. 364–371 (2003)

    Google Scholar 

  14. Brewer, C., Harrower, M.B., Sheesley, D.H., Woodruff, A.: ColorBrewer (2003). http://colorbrewer.org

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Acknowledgements

This work is supported by FEDER Funds through the “Programa Operacional Factores de Competitividade - COMPETE” program and by National Funds through FCT “Fundação para a Ciência e a Tecnologia” under the project: FCOMP-01-0124-FEDER-PEst-OE/EEI/UI0760/2011, PEst-OE/EEI/UI0760/2014, and PEst2015-2020.

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Correspondence to M. L. R. Varela .

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Varela, M.L.R. et al. (2021). Interoperable Decision Support System Based on Multivariate Time Series for Setup Data Processing and Visualization. In: Abraham, A., Siarry, P., Ma, K., Kaklauskas, A. (eds) Intelligent Systems Design and Applications. ISDA 2019. Advances in Intelligent Systems and Computing, vol 1181. Springer, Cham. https://doi.org/10.1007/978-3-030-49342-4_53

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