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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 18

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

Towards Big Data Solutions for Industrial Tomography Data Processing

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DOI: http://dx.doi.org/10.15439/2019F310

Citation: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 18, pages 427–431 ()

Full text

Abstract. This paper presents an overview of what Big Data can bring to the modern industry. Through following the history of contemporary Big Data frameworks the authors observe that the tools available have reached sufficient maturity so as to be usable in an industrial setting. The authors propose the concept of a system for collecting, organising, processing and analysing experimental data obtained from measurements using process tomography. Process tomography is used for noninvasive flow monitoring and data acquisition. The measurement data are collected, stored and processed to identify process regimes and process threats. Further general examples of solutions that aim to take advantage of the existence of such tools are presented as proof of viability of such approach. As the first step in the process of creating the proposed system, a scalable, distributed, containerisation-based cluster has been constructed, with consumer-grade hardware.

References

  1. H. Lasi, P. Fettke, H.-G. Kemper, T. Feld, and M. Hoffmann, “Industry 4.0,” Business & Information Systems Engineering, vol. 6, no. 4, pp. 239–242, Aug 2014. http://dx.doi.org/10.1007/s12599-014-0334-4
  2. V. C. Gungor, G. P. Hancke et al., “Industrial wireless sensor networks: Challenges, design principles, and technical approaches.” IEEE Trans. Industrial Electronics, vol. 56, no. 10, pp. 4258–4265, 2009. http://dx.doi.org/10.1109/TIE.2009.2015754
  3. S. Ghemawat, H. Gobioff, and S.-T. Leung, “The Google file system,” p. 29, 2003.
  4. A. Romanowski, “Big data-driven contextual processing methods for electrical capacitance tomography,” IEEE Transactions on Industrial Informatics, vol. 15, no. 3, pp. 1609–1618, 2019. http://dx.doi.org/10.1109/TII.2018.2855200
  5. J. Dean and S. Ghemawat, “Mapreduce: simplified data processing on large clusters,” Communications of the ACM, vol. 51, no. 1, pp. 107–113, 2008. http://dx.doi.org/10.1145/1327452.1327492
  6. K. Shvachko, H. Kuang, S. Radia, and R. Chansler, “The hadoop distributed file system,” in Mass storage systems and technologies (MSST), 2010 IEEE 26th symposium on. IEEE, 2010. http://dx.doi.org/10.1109/MSST.2010.5496972 pp. 1–10.
  7. M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoica, “Spark: Cluster computing with working sets.” HotCloud, vol. 10, no. 10-10, p. 95, 2010.
  8. V. K. Vavilapalli, A. C. Murthy, C. Douglas, S. Agarwal, M. Konar, R. Evans, T. Graves, J. Lowe, H. Shah, S. Seth et al., “Apache hadoop yarn: Yet another resource negotiator,” in Proceedings of the 4th annual Symposium on Cloud Computing. ACM, 2013. http://dx.doi.org/10.1145/2523616.2523633 p. 5.
  9. P. Basanta-Val, N. C. Audsley, A. J. Wellings, I. Gray, and N. Fernández- García, “Architecting time-critical big-data systems,” IEEE Transactions on Big Data, vol. 2, no. 4, pp. 310–324, 2016. http://dx.doi.org/10.1109/TB-DATA.2016.2622719
  10. K. Grudzien, A. Romanowski, D. Sankowski, and R. A. Williams, “Gravitational granular flow dynamics study based on tomographic data processing,” Particulate Science and Technology, vol. 26, no. 1, pp. 67–82, 2007. http://dx.doi.org/10.1080/02726350701759373
  11. T. Rymarczyk, “Using electrical impedance tomography to monitoring flood banks,” International Journal of Applied Electromagnetics and Mechanics, vol. 45, no. 1-4, pp. 489–494, 2014. http://dx.doi.org/10.3233/JAE-141868
  12. K. Grudzien, A. Romanowski, and R. A. Williams, “Application of a bayesian approach to the tomographic analysis of hopper flow,” Particle & Particle Systems Characterization, vol. 22, no. 4, pp. 246–253, 2005. http://dx.doi.org/10.1002/ppsc.200500951
  13. S. Opałka, B. Stasiak, D. Szajerman, and A. Wojciechowski, “Multi-channel convolutional neural networks architecture feeding for effective eeg mental tasks classification,” Sensors, vol. 18, no. 10, 2018. http://dx.doi.org/10.3390/s18103451
  14. A. Romanowski, “Contextual processing of electrical capacitance tomography measurement data for temporal modeling of pneumatic conveying process,” in Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, ser. Annals of Computer Science and Information Systems, vol. 15. IEEE, 2018. http://dx.doi.org/10.15439/2018F171 pp. 283–286.
  15. A. Nowak, M. Wozniak, M. Pieprzowski, and A. Romanowski, “Towards amblyopia therapy using mixed reality technology,” in 2018 Federated Conference on Computer Science and Information Systems (FedCSIS), 2018. http://dx.doi.org/10.15439/2018F335 pp. 279–282.
  16. M. Skuza and A. Romanowski, “Sentiment analysis of twitter data within big data distributed environment for stock prediction,” in 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), 2015. http://dx.doi.org/10.15439/2015F230 pp. 1349–1354.
  17. A. Romanowski, M. Skuza, P. Wozniak, K. Grudzien, and Z. Chaniecki, “Big data computational environment for tomography measurement data,” Process Tomography WCIPT7, Poland, 2013.
  18. A. Romanowski, K. Grudzien, Z. Chaniecki, and P. Woźniak, “Contextual processing of ect measurement information towards detection of process emergency states,” in 13th International Conference on Hybrid Intelligent Systems (HIS). IEEE, 2013. http://dx.doi.org/10.1109/HIS.2013.6920448 pp. 291–297.
  19. T. Rymarczyk and J. Sikora, “Applying industrial tomography to control and optimization flow systems,” Open Physics, vol. 16, p. 46, Jun. 2018. http://dx.doi.org/10.1515/phys-2018-0046
  20. C. Chen, P. W. Woźniak, A. Romanowski, M. Obaid, T. Jaworski, J. Kucharski, K. Grudzień, S. Zhao, and M. Fjeld, “Using crowdsourcing for scientific analysis of industrial tomographic images,” ACM Trans. Intell. Syst. Technol., vol. 7, no. 4, pp. 52:1–52:25, Jul. 2016. http://dx.doi.org/10.1145/2897370
  21. W. Felter, A. Ferreira, R. Rajamony, and J. Rubio, “An updated performance comparison of virtual machines and linux containers,” in 2015 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), March 2015. http://dx.doi.org/10.1109/ISPASS.2015.7095802 pp. 171–172.
  22. I. Jelliti, A. Romanowski, and K. Grudzień, “Design of crowdsourcing system for analysis of gravitational flow using x-ray visualization,” in Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, ser. Annals of Computer Science and Information Systems, M. Ganzha, L. Maciaszek, and M. Paprzycki, Eds., vol. 8. IEEE, 2016. http://dx.doi.org/10.15439/2016F288 pp. 1613–1619.
  23. P. Kucharski, K. Pagacz, A. Szadkowska, W. Młynarski, A. Romanowski, and W. Fendler, “Resistance to data loss of glycemic variability measurements in long-term continuous glucose monitoring,” Diabetes Technology & Therapeutics, vol. 20, no. 12, pp. 833–842, 2018. http://dx.doi.org/10.1089/dia.2018.0247