Advertisement

Cloud Computing Approach for Intelligent Visualization of Multidimensional Data

  • Jolita Bernatavičienė
  • Gintautas Dzemyda
  • Olga KurasovaEmail author
  • Virginijus Marcinkevičius
  • Viktor Medvedev
  • Povilas Treigys
Chapter
Part of the Springer Optimization and Its Applications book series (SOIA, volume 107)

Abstract

In this paper, a Cloud computing approach for intelligent visualization of multidimensional data is proposed. Intelligent visualization enables to create visualization models based on the best practices and experience. A new Cloud computing-based data mining system DAMIS is introduced for the intelligent data analysis including data visualization methods. It can assist researchers to handle large amounts of multidimensional data when executing resource-expensive and time-consuming data mining tasks by considerably reducing the information load. The application of DAMIS is illustrated by the visual analysis of medical streaming data.

Keywords

intelligent visualization cloud computing dimensionality reduction medical streaming data 

References

  1. 1.
    Bernatavičienė, J., Dzemyda, G., Kurasova, O., Marcinkevičius, V., Medvedev, V.: The problem of visual analysis of multidimensional medical data. In: Törn, A., Žilinskas, J. (eds.) Models and Algorithms for Global Optimization. Optimization and Its Applications, vol. 4, pp. 277–298. Springer, New York (2007). doi:10.1007/978-0-387-36721-7∖_17CrossRefGoogle Scholar
  2. 2.
    Bernatavičienė, J., Dzemyda, G., Bazilevičius, G., Medvedev, V., Marcinkevičius, V., Treigys, P.: Method for visual detection of similarities in medical streaming data. Int. J. Comput. Commun. Control 10 (1), 8–21 (2015). doi:10.15837/ijccc.2015.1.1310Google Scholar
  3. 3.
    Berthold, M.R., Hand, D.J. (eds.): Intelligent Data Analysis: An Introduction, 2nd edn. Springer, Berlin (2003). doi:10.1007/ 978-3-540-48625-1Google Scholar
  4. 4.
    Berthold, M.R., Cebron, N., Dill, F., Gabriel, T.R., Kötter, T., Meinl, T., Ohl, P., Sieb, C., Thiel, K., Wiswedel, B.: KNIME: The Konstanz information Miner. In: Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin (2007). doi:10.1007/ 978-3-540-78246-9∖_38Google Scholar
  5. 5.
    Borg, I., Groenen, P.: Modern Multidimensional Scaling: Theory and Applications. Springer, New York (2005). doi:10.1007/0-387-28981-XMathSciNetzbMATHGoogle Scholar
  6. 6.
    Demšar, J., Curk, T., Erjavec, A., Gorup, C., Hočevar, T., Milutinovič, M., Možina, M., Polajnar, M., Toplak, M., Starič, A., Štajdohar, M., Umek, L., Žagar, L., Žbontar, J., Žitnik, M., Zupan, B.: Orange: data mining toolbox in Python. J. Mach. Learn. Res. 14, 2349–2353 (2013)zbMATHGoogle Scholar
  7. 7.
    Dzemyda, G., Kurasova, O.: Heuristic approach for minimizing the projection error in the integrated mapping. Eur. J. Oper. Res. 171 (3), 859–878 (2006). doi:10.1016/j.ejor.2004.09.011MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Dzemyda, G., Kurasova, O., Medvedev, V.: Dimension reduction and data visualization using neural networks. In: Maglogiannis, I., Karpouzis, K., Wallace, M., Soldatos, J. (eds.) Emerging Artificial Intelligence Applications in Computer Engineering. Frontiers in Artificial Intelligence and Applications, vol. 160, pp. 25–49. IOS Press, Amsterdam (2007)Google Scholar
  9. 9.
    Dzemyda, G., Marcinkevičius, V., Medvedev, V.: Large-scale multidimensional data visualization: a web service for data mining. In: Abramowicz, W., Llorente, I., Surridge, M., Zisman, A., Vayssière, J. (eds.) Towards a Service-Based Internet. Lecture Notes in Computer Science, vol. 6994, pp. 14–25. Springer, Berlin/Heidelberg (2011). doi:10. 1007/978-3-642-24755-2_2Google Scholar
  10. 10.
    Dzemyda, G., Marcinkevičius, V., Medvedev, V.: Web application for large-scale multidimensional data visualization. Math. Model. Anal. 16 (2), 273–285 (2011). doi:10.3846/13926292.2011.580381CrossRefGoogle Scholar
  11. 11.
    Dzemyda, G., Kurasova, O., Žilinskas, J.: Multidimensional Data Visualization: Methods and Applications. Springer, Berlin (2013). doi:10. 1007/978-1-4419-0236-8Google Scholar
  12. 12.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11 (1), 10–18 (2009). doi:10.1145/1656274.1656278CrossRefGoogle Scholar
  13. 13.
    Hofmann, M., Klinkenberg, R.: RapidMiner: Data Mining Use Cases and Business Analytics Applications. Chapman and Hall/CRC, Boca Raton (2013)Google Scholar
  14. 14.
    Jolliffe, I.: Principal Component Analysis. Springer, Berlin (1986). doi:10.1007/b98835MathSciNetzbMATHGoogle Scholar
  15. 15.
    Kohonen, T.: Overture. In: Self-Organizing Neural Networks: Recent Advances and Applications, pp. 1–12. Springer, New York (2002)Google Scholar
  16. 16.
    Kranjc, J., Podpecan, V., Lavrac, N.: Clowdflows: A cloud based scientific workflow platform. In: Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science, vol. 7524, pp. 816–819. Springer, Berlin/Heidelberg (2012). doi:10.1007/ 978-3-642-33486-3∖_54Google Scholar
  17. 17.
    Kranjc, J., Smailovič, J., Podpečan, V., Grčar, M., Žnidaršič, M., Lavrač, N.: Active learning for sentiment analysis on data streams: methodology and workflow implementation in the ClowdFlows platform. Inf. Process. Manag. 51 (2), 187–203 (2014). doi:10.1016/j.ipm.2014.04. 001CrossRefGoogle Scholar
  18. 18.
    Kurasova, O., Molytė, A.: Quality of quantization and visualization of vectors obtained by neural gas and self-organizing map. Informatica 22 (1), 115–134 (2011)MathSciNetGoogle Scholar
  19. 19.
    Mao, J., Jain, A.K.: Artificial neural networks for feature extraction and multivariate data projection. IEEE Trans. Neural Netw. 6 (2), 296–317 (1995). doi:10.1109/72.363467CrossRefGoogle Scholar
  20. 20.
    Massimo, B., Giuseppe, L., Castellani, M., Cavuoti, S., D’Abrusco, R., Laurino, O.: DAME: a distributed web based framework for knowledge discovery in databases. Memorie Soc. Astron. Ital. Suppl. 19, 324–329 (2012)Google Scholar
  21. 21.
    Medvedev, V., Dzemyda, G., Kurasova, O., Marcinkevičius, V.: Efficient data projection for visual analysis of large data sets using neural networks. Informatica 22 (4), 507–520 (2011)zbMATHGoogle Scholar
  22. 22.
    Podpečan, V., Zemenova, M., Lavrač, N.: Orange4WS environment for service-oriented data mining. Comput. J. 55, 82–98 (2012). doi:10. 1093/comjnl/bxr077Google Scholar
  23. 23.
    Ye, N.: The Handbook of Data Mining. LEA, New Jersey/London (2003)Google Scholar
  24. 24.
    Žilinskas, A., Žilinskas, J.: Two level minimization in multidimensional scaling. J. Glob. Optim. 38 (4), 581–596 (2007). doi:10.1007/ s10898-006-9097-xMathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Žilinskas, A., Žilinskas, J.: A hybrid method for multidimensional scaling using city-block distances. Math. Meth. Oper. Res. 68 (3), 429–443 (2008). doi:10.1007/s00186-008-0238-5MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Žilinskas, A., Žilinskas, J.: Branch and bound algorithm for multidimensional scaling with city-block metric. J. Glob. Optim. 43 (2-3), 357–372 (2009). doi:10.1007/s10898-008-9306-xMathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jolita Bernatavičienė
    • 1
  • Gintautas Dzemyda
    • 1
  • Olga Kurasova
    • 1
    Email author
  • Virginijus Marcinkevičius
    • 1
  • Viktor Medvedev
    • 1
  • Povilas Treigys
    • 1
  1. 1.Institute of Mathematics and InformaticsVilnius UniversityVilniusLithuania

Personalised recommendations