Proceedings of International Conference on Applied Innovation in IT
2022/03/09, Volume 10, Issue 1, pp.69-79

The Clustering and Fuzzy Logic Methods Complex for Big Data Processing


Larysa Globa, Rina Novogrudska, Andrii Liashenko


Abstract: Currently, telecom operators are facing a problem that is conditionally called "Big Data". The telecom industry is growing rapidly and dynamically, new technologies are emerging (IoT, M2M, D2D, P2P), new companies are using them, new information and communication services are being introduced to automate production processes, and so on. Methods of statistical analysis, A\B testing, data fusion and integration, Data Mining, machine learning, data visualization are used in the Big Data processing and analysis, but due to the fact that large amounts of Big Data are not structured, come in real-time with various delays related to bandwidth and network congestion, in each case the processes of processing and analysis of Big Data are extremely costly in terms of time and resources. As a result, telecom operators need not only to process large amounts of data but also to extract knowledge from them. However, the analytical processing of large data is characterized by blurred boundaries, which determine certain logical relationships between data. This

Keywords: Fuzzy Logic, Clustering Algorithms, Smart System, Statistical Numerical Data, Fuzzy Knowledge Bases,

DOI: 10.25673/76934

Download: PDF

References:

  1. Digital 2019: Global Internet use accelerates, [Online]. Available: https://wearesocial.com/blog/2019/01/digital-2019-global-internet-use-accelerates.
  2. M. Nathaz, W. James, Big data. Principles and best practices of scalable real-time data systems 1st Edition, 2015, pp. 185-192, [Online]. Available: https://www.amazon.com/Big-Data-Principlespractices-scalable/dp/1617290.
  3. E. Nada, Advances in Data Mining. Applications and Theoretical Aspects / E. Nada, E. Ahmed. // Big Data Analytics: A Literature Review Paper. – 14th Industrial Conference, ICDM 2014, St. Petersburg, Russia, July 16-20, 2014. Proceedings, Lecture Notes in Computer Science, Springer, pp. 214-227.
  4. Y. Buhaienko, L. S. Globa, A. Liashenko, and M. Grebinechenko, “Analysis of clustering algorithms for use in the universal data processing system”, in Proc. International scientific and technical conf. Open Semantic Technologies for Intelligent Systems (OSTIS-2020), Minsk, 2020, pp. 101-104.
  5. K. Hribernik, Z. Ghrairi, C. Hans, and D. Thoben, “Co-creating the Internet of Things - First experiences in the participatory design of Intelligent Products with Arduino”, in Proc. 17th International Conference on Concurrent Enterprising, Aachen, Germany, 2011, pp. 1-9.
  6. X. Lei, Z. Yan, and Y. ChunLi, “The application and implementation research of smart city in China”, in Proc. 2012 International Conference on System Science and Engineering(ICSSE), China, 2012, pp. 288-292.
  7. P. Fränti and S. Sieranoja, “How much can k-means be improved by using better initialization and repeats?”, Pattern Recognition, vol. 93, no. 2, pp. 95-112, 2019. doi:10.1016/j.patcog.2019.04.014.
  8. Concepts and Characteristics of Big Data Analytics, [Online]. Available: https://www.iunera.com/kraken/fabric/big-data/.
  9. M. Zgurovsky and Y. Zaychenko, “Big Data: Conceptual Analysis and Applications”, Springer Nature Switzerlend, 2020, pp. 1-42.
  10. A. Fahad, N. Alshatri, Z. Tari et al., “A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis”, IEEE Transactions on Emerging Topics in Computing, vol. 2, no. 3, pp. 267-279, Sept. 2014, doi: 10.1109/TETC.2014.2330519.
  11. S. Ghosh and S. Kumar Dubey, “Comparative Analysis of K-Means and Fuzzy CMeans Algorithms”, International Journal of Advanced Computer Science and Applications (IJACSA), vol. 4, no. 4, pp. 35-39, 2013.
  12. K. A. Abdul Nazeer and M.P. Sebastian, "Improving the Accuracy and Efficiency of the k-means Clustering Algorithm", in Proc. of the World Congress on Engineering 2009, London, 2009, ISBN: 978-988-17012-5-1.
  13. L. S. Globa, Y. M. Buhaienko, I. O. Ishchenko, and A. V. Liashenko, “Approach to determining the number of clusters in a data set”, in Proc. International scientific and technical conf. Open Semantic Technologies for Intelligent Systems (OSTIS-2019), Minsk, 2019, pp. 151-154.
  14. Initialization: Where do you start?, [Online]. Available: http://www.salientiastuff.com/k-meansclustering-part-2.html.
  15. A. Pegat, “Sushchnost teorii nechetkikh mnozhestv”, Nechetkoye modelirovaniye i upravleniye, 2015, (3th ed.), pp. 13-19, ISBN 3-7908-1385-0.
  16. F. Shevri, “Fuzzy logic”, Schneider Electric, 2009, vol. 31, pp. 1-30, [Online]. Available: https://profsector.com/media/catalogs/566dd6af08f6c.pdf
  17. A. V. Lyashenko, “The approach to building fuzzy logical rules for big data”, International scientific and technical conference: Modern challenges in telecommunications, 2020, [Online]. Available: http://conferenc.its.kpi.ua/proc/article/view/201702.
  18. A. Pegat, “Samoorganizuyushchiyesya i samonastraivayushchiyesya nechetkiye modeli”, Nechetkoye modelirovaniye i upravleniye, 2015, (3th ed.), pp. 506-520, ISBN 3-7908-1385-0.
  19. V. V. Kurdecha, I. O. Ishchenko, and A. H. Zakharchuk, "Data processing method in distributed network Internet of Things", Young Scientist, 2017, vol. 10, no. 50, pp. 75-81.
  20. Microservice architecture, [Online]. Available: https://itnan.ru/post.php?c=1&p=320962.
  21. .NET Microservices: Architecture for Containerized .NET Applications, [Online]. Available: https://docs.microsoft.com/ru-ru/dotnet/architecture/microservices/.


    HOME

       - Call for Papers
       - Paper Submission
       - For authors
       - Important Dates
       - Conference Committee
       - Editorial Board
       - Reviewers
       - Last Proceedings


    PROCEEDINGS

       - Volume 12, Issue 1 (ICAIIT 2024)        - Volume 11, Issue 2 (ICAIIT 2023)
       - Volume 11, Issue 1 (ICAIIT 2023)
       - Volume 10, Issue 1 (ICAIIT 2022)
       - Volume 9, Issue 1 (ICAIIT 2021)
       - Volume 8, Issue 1 (ICAIIT 2020)
       - Volume 7, Issue 1 (ICAIIT 2019)
       - Volume 7, Issue 2 (ICAIIT 2019)
       - Volume 6, Issue 1 (ICAIIT 2018)
       - Volume 5, Issue 1 (ICAIIT 2017)
       - Volume 4, Issue 1 (ICAIIT 2016)
       - Volume 3, Issue 1 (ICAIIT 2015)
       - Volume 2, Issue 1 (ICAIIT 2014)
       - Volume 1, Issue 1 (ICAIIT 2013)


    PAST CONFERENCES

       ICAIIT 2024
         - Photos
         - Reports

       ICAIIT 2023
         - Photos
         - Reports

       ICAIIT 2021
         - Photos
         - Reports

       ICAIIT 2020
         - Photos
         - Reports

       ICAIIT 2019
         - Photos
         - Reports

       ICAIIT 2018
         - Photos
         - Reports

    ETHICS IN PUBLICATIONS

    ACCOMODATION

    CONTACT US

 

DOI: http://dx.doi.org/10.25673/115729


        

         Proceedings of the International Conference on Applied Innovations in IT by Anhalt University of Applied Sciences is licensed under CC BY-SA 4.0


                                                   This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License


           ISSN 2199-8876
           Publisher: Edition Hochschule Anhalt
           Location: Anhalt University of Applied Sciences
           Email: leiterin.hsb@hs-anhalt.de
           Phone: +49 (0) 3496 67 5611
           Address: Building 01 - Red Building, Top floor, Room 425, Bernburger Str. 55, D-06366 Köthen, Germany

        site traffic counter

Creative Commons License
Except where otherwise noted, all works and proceedings on this site is licensed under Creative Commons Attribution-ShareAlike 4.0 International License.