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A Review on the Development of Big Data Analytics and Effective Data Visualization Techniques in the Context of Massive and Multidimensional Data

Affiliations

  • School of Computing and Engineering, Vellore Institute of Technology, Katpadi - 632014, Vellore, Tamil Nadu, India

Abstract


Objectives: Data visualization, the use of images to represent information, is now becoming properly appreciated due to the benefits it can bring to business. This paper focuses on the general background of data visualization and visualization techniques. Methods: Data visualization has the prospective to assist humans in analysing and comprehending large volumes of data, and to detect patterns, clusters and outliers that are not obvious using non-graphical forms of presentation. For this reason, data visualizations have an important role to play in a diverse range of applied problems, including data exploration and mining, Information retrieval and intelligence analysis. In real time various techniques have been used of which Geometric projection techniques, Iconographic display techniques, Pixel-oriented, Hierarchical techniques, Graph-based techniques are discussed. Findings: The major difficultly in big data visualization is to preserve any of the original dimensional information. The taxonomy detailed here show that the local and global structure of the data can be visualized in an interactive manner and has a massive advantage.

Keywords

Parallel Coordinates, Star Coordinates, Visual Analytics, Visualization Techniques.

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References


  • Keim DA, Mansmann F, Schneidewind J, Ziegler H. Challenges in visual data analysis. 10th International Conference on Information Visualization; London, England. 2006 Jul 5. p. 9–16.
  • Wong PC, Bergeron RD. 30 years of multidimensional multivariate visualization. Scientific Visualization. 1994 May 1; 3–33.
  • Card SK, Mackinlay JD, Shneiderman B. Readings in information visualization: Using vision to think. Morgan Kaufmann; 1999.
  • Keim DA. Information visualization and visual data mining. IEEE Transactions on Visualization and Computer Graphics. 2002 Jan; 8(1):1–8.
  • Cleveland WS. Visualizing data. Hobart Press; 1993 Sep 1.
  • Alpern B, Carter L. The hyper box. Proceedings of IEEE, Conference on Visualization (Visualization’91); CA, USA.1991 Oct 22. p. 133–9.
  • Mihalisin T, Gawlinski E, Timlin J, Schwegler J. Visualizing a scalar field on an N-dimensional lattice. Proceedings of the 1st Conference on Visualization’90; San Francisco, CA. 1990 Oct 23. p. 255–62.
  • Van Wijk JJ, Van Liere R. Hyper Slice: Visualization of scalar functions of many variables. Proceedings of the 4th Conference on Visualization’93; DC, USA.1993 Oct 25. p. 119–25.
  • Chernoff H. The use of faces to represent points in K-dimensional space graphically. Journal of the American Statistical Association. 1973 Jun 1; 68(342):361–8.
  • Pickett RM, Grinstein GG. Iconographic displays for visualizing multidimensional data. Proceedings of the IEEE Conference on Systems, Man and Cybernetics; 1988 Aug 8. p. 519.
  • Beddow J. Shape coding of multidimensional data on a microcomputer display. Proceedings of the 1st Conference on Visualization’90; San Francisco, CA. 1990 Oct 23. p. 238–46.
  • Levkowitz H. Color icons: Merging colour and texture perception for integrated visualization of multiple parameters. Proceedings of the 2nd Conference on Visualization’91; San Francisco, CA. 1991 Oct 22. p. 164–70.
  • Mihalisin T, Gawlinski E, Timlin J, Schwegler J. Visualizing a scalar field on an n-dimensional lattice. Proceedings of the 1st Conference on Visualization’90; San Francisco, CA. 1990 Oct 23. p. 255–62.
  • LeBlanc J, Ward MO, Wittels N. Exploring n-dimensional databases. Proceedings of the 1st Conference on Visualization’90; San Francisco, CA. 1990 Oct 23. p. 230–7.
  • Inselberg A. The plane with parallel coordinates. The Visual Computer. 1985 Aug 1; 1(2):69–91.
  • Inselberg A, Dimsdale B. Parallel coordinates: A tool for visualizing multi-dimensional geometry. IEEE Proceedings of the 1st Conference on Visualization; 1990. San Francisco CA. p. 361–75.
  • Keim DA, Kriegel HP. VisDB: Database exploration using multidimensional visualization. IEEE Computer Graphics and Applications. 1994 Sep; 14(5):40–9.
  • Andrews DF. Plots of high-dimensional data. Biometrics. 1972 Mar 1; 28(1):125–36.
  • Friedman JH, Tukey JW. A projection pursuit algorithm for exploratory data analysis. IEEE Transactions on Computers. 1974; 23(9):981–890.
  • Huber PJ. Projection pursuit. The annals of Statistics. 1985 Jun 1; 13(2):435–75.
  • Wegman EJ. Hyperdimensional data analysis using parallel coordinates. Journal of the American Statistical Association. 1990 Sep 1; 85(411):664–75.
  • Furnas GW, Buja A. Prosection views: Dimensional inference through sections and projections. Journal of Computational and Graphical Statistics. 1994 Dec 1; 3(4):323–53.
  • Wright W. Research report: Information animation applications in the capital markets. Proceedings on Information Visualization; 1995 Oct 30. p. 19–25.
  • Hoffman P, Grinstein G, Marx K, Grosse I, Stanley E. DNA visual and analytic data mining. Proceedings on Visualization’97; USA. 1997 Oct 24. p. 437–41.
  • Kandogan E. Star coordinates: A multi-dimensional visualization technique with uniform treatment of dimensions. Proceedings of the IEEE Information Visualization Symposium; 2000. p. 22.
  • Kandogan E. Visualizing multi-dimensional clusters, trends and outliers using star coordinates. Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; NY, USA. 2001 Aug 26. p. 107–16.
  • Pickett RM. Visual analysis of texture in the detection and recognition of objects. Picture Processing and Psychopictorics. 1970; p. 289–308.
  • Tufte ER, Graves-Morris PR. The visual display of quantitative information. Cheshire, CT: Graphics Press; 1983 Oct.
  • Ankerst M, Keim DA, Kriegel HP. Circle segments: A technique for visually exploring large multidimensional data sets. Proceedings of Visualization; San Francisco, CA. 1996. p. 1–4.
  • Keim DA, Kriegel HP. VisDB: Database exploration using multidimensional visualization. IEEE Computer Graphics and Applications. 1994 Sep; 14(5):40–9.
  • Keim DA, Ankerst M, Kriegel HP. Recursive pattern: A technique for visualizing very large amounts of data. Proceedings of the 6th Conference on Visualization’95; GA. 1995 Oct 29. p. 279.
  • LeBlanc J, Ward MO, Wittels N. Exploring n-dimensional databases. Proceedings of the 1st Conference on Visualization’90; San Francisco, CA. 1990 Oct 23. p. 230–7.
  • Feiner SK, Beshers C. Worlds within worlds: Metaphors for exploring n-dimensional virtual worlds. Proceedings of the 3rd Annual ACM SIGGRAPH Symposium on User Interface Software and Technology; NY, USA. 1990 Aug 1. p. 76–83.
  • Robertson GG, Mackinlay JD, Card SK. Cone trees: Animated 3D visualizations of hierarchical information. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; NY, USA. 1991 Apr 27. p. 189–94.
  • Shneiderman B. Tree visualization with tree-maps: 2-d space-filling approach. ACM Transactions on Graphics (TOG). 1992 Jan 2; 11(1):92–9.
  • Johnson BS. Treemaps: Visualizing hierarchical and categorical data [Doctoral Dissertation]. USA: University of Maryland at College Park; 1993.
  • Rekimoto J, Green M. The information cube: Using transparency in 3D information visualization. Proceedings of the 3rd Annual Workshop on Information Technologies and Systems (WITS’93); Canada. 1993 Dec 5. p. 125–32.
  • Eick SG, Wills GJ. Navigating large networks with hierarchies. Proceedings of IEEE Conference on Visualization; San Jose, CA. 1993 Oct 25. p. 204–10.
  • Rusu A, Santiago C, Jianu R. Real-time space-efficient synchronized tree-based web visualization and design. 2006.
  • Artero AO, de Oliveira MC. Viz3d: Effective exploratory visualization of large multidimensional data sets. Proceedings of 17th Brazilian Symposium on Computer Graphics and Image Processing; 2004 Oct 17. p. 340–7.
  • Cooprider ND, Burton RP. Extension of star coordinates into three dimensions. Electronic Imaging. 2007 Jan 28; 64950.
  • Shaik JS, Yeasin M. Visualization of high dimensional data using an automated 3D star co-ordinate system. International Joint Conference on Neural Networks (IJCNN’06); 2006 Jul 16. p. 1339–46.
  • Shaik J, Yeasin M. Selection of best projection from 3D star coordinate projection space using energy minimization and topology preserving mapping. International Joint Conference on Neural Networks (IJCNN). Orlando, FL. 2007 Aug 12. p. 2604–9.
  • Chen K, Liu L. Clustermap: Labeling clusters in large datasets via visualization. Proceedings of the 13th ACM International Conference on Information and Knowledge Management; GA. 2004 Nov 13. p. 285–93.
  • Teoh ST, Ma KL. StarClass: Interactive visual classification using star coordinates. SDM 2003 Jan 1. p. 178–85.
  • Sun Y, Tang J, Tang D, Xiao W. Advanced star coordinates. The Ninth International Conference on Web-Age Information Management (WAIM’08); 2008 Jul 20. p. 165–70.
  • Dhillon IS, Modha DS, Spangler WS. Visualizing class structure of multidimensional data. Computing Science and Statistics. 1998 May 13;488–93.
  • Dhillon IS, Modha DS, Spangler WS. Class visualization of high-dimensional data with applications. Computational Statistics and Data Analysis. 2002 Nov 28; 41(1):59–90.
  • Toronen P, Kolehmainen M, Wong G, Castren E. Analysis of gene expression data using self-organizing maps. FEBS Letters. 1999 May 21; 451(2):142–6.
  • Faloutsos C, Lin KI. FastMap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets. ACM; 1995 Jun 1. p. 1–12.
  • Yin H. ViSOM - A novel method for multivariate data projection and structure visualization. IEEE Transactions on Neural Networks. 2002 Jan; 13(1):237–43.
  • Aftab Z, Tuaseef H. Enhancing pixel oriented visualization by merging circle view and circle segment visualization techniques. Multi-Disciplinary Trends in Artificial Intelligence. 2012 Dec 26; 7694:101–9.
  • Tatu A, Albuquerque G, Eisemann M, Schneidewind J, Theisel H, Magnor M, Keim D. Combining automated analysis and visualization techniques for effective exploration of high-dimensional data. IEEE Symposium on Visual Analytics Science and Technology (VAST); Atlantic City, NJ. 2009 Oct 12. p. 59–66.
  • Graham M, Kennedy J. Using curves to enhance parallel coordinate visualisations. IV Proceedings of 7th International Conference on Information Visualization; 2003 Jul 16. p. 10–6.
  • Moustafa R, Wegman E. Multivariate continuous data-parallel coordinates. Graphics of Large Datasets. 2006;143–55.
  • Zhou H, Yuan X, Qu H, Cui W, Chen B. Visual clustering in parallel coordinates. Computer Graphics Forum. 2008 May 1; 27(3):1047–54.
  • Yang L. Visualizing frequent item sets, association rules and sequential patterns in parallel coordinates. Computational Science and Its Applications - ICCSA; Heidelberg. 2003 May 18. p. 21–30.
  • Yang J, Peng W, Ward MO, Rundensteiner EA. Interactive hierarchical dimension ordering, spacing and filtering for exploration of high dimensional datasets. IEEE Symposium on Information Visualization (INFOVIS); DC, USA. 2003 Oct 19. p. 105–12.
  • Kosara R, Bendix F, Hauser H. Parallel sets: Interactive exploration and visual analysis of categorical data. IEEE Transactions on Visualization and Computer Graphics. 2006; 12(4):558–68.
  • Siirtola H, Laivo T, Heimonen T, Raiha KJ. Visual perception of parallel coordinate visualizations. 13th International Conference on Information Visualisation; Barcelona. 2009 Jul 15. p. 3–9.
  • Johansson J, Treloar R, Jern M. Integration of unsupervised clustering, interaction and parallel coordinates for the exploration of large multivariate data. IV Proceedings of 8th International Conference on Information Visualisation; 2004 Jul 14. p. 52–7.
  • Bertini E, Dell’Aquila L, Santucci G. Springview: Cooperation of radviz and parallel coordinates for view optimization and clutter reduction. Proceedings of 3rd International Conference on Coordinated and Multiple Views in Exploratory Visualization; DC, USA. 2005 Jul 5. p. 22–9.
  • Parthiban P, Selvakumar S. Big data architecture for capturing, storing, analysing and visualizing of web server logs. Indian Journal of Science and Technology. 2016 Jan 17; 9(4).
  • Puri GD, Haritha D. Survey Big Data Analytics, Applications and Privacy Concerns. Indian Journal of Science and Technology. 2016 May 18; 9(17).
  • Sapna S, Kumar MP. Diagnosis of disease from clinical big data using neural network. Indian Journal of Science and Technology. 2015 Sep 1; 8(24):1.
  • Koteeswaran S, Visu P, Kannan E. Enhancing JS–MR based data visualisation using YARN. Indian Journal of Science and Technology. 2015 Apr 1; 8(11).

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