Skip to main content
SpringerLink
Log in

Using arced axes in parallel coordinates geometry for high dimensional BigData visual analytics in cloud computing

  • Published:
Computing Aims and scope Submit manuscript

Abstract

With the rapid growth of data in size and complexity, that are available on shared cloud computing platform, the threat of malicious activities and computer crimes has increased accordingly. Thus, investigating efficient data visualization techniques for visual analytics of such big data and visual intrusion detection over data intensive cloud computing is urgently required. In this paper, we first propose a new parallel coordinates visualization method that uses arced-axis for high-dimensional data representation. This new geometrical scheme can be efficiently used to identify the main features of network attacks by displaying recognizable visual patterns. In addition, with the aim of visualizing the clear and detailed structure of the dataset according to the contribution of each attribute, we propose a meaningful layout for the new method based on singular value decomposition algorithm, which possesses statistical property and can overcome the curse of dimensionality. Finally, we design a prototype system for network scan detection, which is based on our visualization approach. The experiments have shown that our approach is effective in visualizing multivariate datasets and detecting attacks from a variety of networking patterns, such as the features of DDoS attacks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Zhang X, Liu C, Nepal S, Pandey S, Chen J (2013) A privacy leakage upper-bound constraint based approach for cost-effective privacy preserving of intermediate datasets in cloud. IEEE Trans Parallel Distrib Syst 24(6):1192–1202

    Article  Google Scholar 

  2. Zhang X, Yang LT, Liu C, Chen J (2014) A scalable two-phase top-down specialization approach for data anonymization using mapreduce on cloud. IEEE Trans Parallel Distrib Syst 25(2):363–373

    Google Scholar 

  3. Zhang X, Liu C, Nepal S, Chen J (2013) An efficient quasi-identifier index based approach for privacy preservation over incremental data sets on cloud. J Comput Syst Sci 79(5):542–555

    Article  MATH  MathSciNet  Google Scholar 

  4. Claessen JH, van Wijk JJ (2011) Flexible linked axes for multivariate data visualization. IEEE Trans Vis Comput Graph 17(12):2310–2316

    Article  Google Scholar 

  5. Inselberg A (1985) The plane with parallel coordinates. Vis Comput 1(2):69–91

    Article  MATH  Google Scholar 

  6. Wegman E (1990) Hyperdimensional data analysis using parallel coordinates. J Am Stat Assoc 85(411):664–675

    Article  Google Scholar 

  7. Dasgupta A, Kosara R (2010) Pargnostics: screen-space metrics for parallel coordinates. IEEE Trans Vis Comput Graph 16(6):1017–1026

    Article  Google Scholar 

  8. Huh M-H, Park DY (2008) Enhancing parallel coordinate plots. J Korean Stat Soc 37(2):129–133

    Article  MATH  MathSciNet  Google Scholar 

  9. Zhou H, Yuan X, Qu H, Cui W, Chen B (2008) Visual clustering in parallel coordinates. Comput Graph Forum 27(3):1047–1054

    Article  Google Scholar 

  10. Zhou H, Cui W, Qu H, Wu Y, Yuan X, Zhuo W (2009) Splatting the lines in parallel coordinates. Comput Graph Forum 28(3):759–766

    Article  Google Scholar 

  11. Dang TN, Wilkinson L (2010) A stacking graphic elements to avoid over-plotting. IEEE Trans Vis Comput Graph 16(6):1044–1052

    Article  Google Scholar 

  12. Artero AO, de Oliveira MCF, Levkowitz H (2004) Uncovering clusters in crowded parallel coordinates visualizations. In: Proceedings of IEEE symposium on information visualization, INFOVIS 2004, pp 81–88

  13. Yuan X, Guo P, Xiao H, Zhou H, Qu H (2009) Scattering points in parallel coordinates. IEEE Trans Vis Comput Graph 15(6):1001–1008

    Article  Google Scholar 

  14. Peng W, Ward MO, Rundensteiner EA (2004) Clutter reduction in multi-dimensional data visualization using dimension reordering. In: Proceedings of IEEE symposium on information visualization, INFOVIS 2004, pp 89–96

  15. Artero AO, de Oliveira MCF, Levkowitz H (2006) Enhanced high dimensional data visualization through dimension reduction and attribute arrangement. In: Proceedings of The tenth international conference on information visualization, IV 2006, pp 707–712

  16. Ferdosi BJ, Roerdink JB (2011) Visualizing high-dimensional structures by dimension ordering and filtering using subspace analysis. Comput Graph Forum 30(3):1121–1130

    Article  Google Scholar 

  17. Claessen JH, Van Wijk JJ (2011) Flexible linked axes for multivariate data visualization. IEEE Trans Vis Comput Graph 17(12):2310–2316

    Article  Google Scholar 

  18. Tominski C, Abello J, Schumann H (2004 ) Axes-based visualizations with radial layouts. In: Proceedings of the ACM symposium on applied, computing, pp 1242–1247

  19. Hauser H, Ledermann F, Doleisch H (2002) Angular brushing of extended parallel coordinates. In: Proceedings of IEEE symposium on information visualization, INFOVIS 2002, pp 127–130

  20. Dimsdale B (1984) Conic transformations and projectivities. Technical Report, 6320-2753

  21. Golub G, Van Loan C (1996) Matrix computations, vol 3. Johns Hopkins University Press, Baltimore

    Google Scholar 

  22. Simek K (2003) Properties of singular value decomposition based dynamical model of gene expression data. Int J Appl Math Comput Sci 13(3):337–346

    MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Software, Tianjin University, Tianjin, People’s Republic of China

    Mao Lin Huang

  2. Faculty of Engineering and IT, University of Technology, Sydney, Australia

    Mao Lin Huang, Liang Fu Lu & Xuyun Zhang

  3. Mathematics Department, Tianjin University, Tianjin, People’s Republic of China

    Liang Fu Lu

Authors
  1. Mao Lin Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liang Fu Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xuyun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Mao Lin Huang or Liang Fu Lu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Huang, M.L., Lu, L.F. & Zhang, X. Using arced axes in parallel coordinates geometry for high dimensional BigData visual analytics in cloud computing. Computing 97, 425–437 (2015). https://doi.org/10.1007/s00607-014-0383-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-014-0383-z

Keywords

Mathematics Subject Classification

Search

Navigation