ISVC 2011: Advances in Visual Computing pp 508-517 | Cite as

An Extensible Interactive 3D Visualization Framework for N-Dimensional Datasets Used in Heterogeneous Software Display Environments

  • Nathaniel Rossol
  • Irene Cheng
  • John Berezowski
  • Iqbal Jamal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6938)

Abstract

Although many automated techniques exist to mine large N-dimensional databases, understanding the results is nontrivial. Data visualization can provide perceptual insights leading to the understanding of the results as well as the raw data itself. A particular application domain where theCode="" use of high-dimensional interactive data visualization has proven useful is in the exploratory analysis of disease spread through populations, especially in the case of livestock epidemics. However, designing effective visualization tools for domain practitioners presents many challenges that have not been resolved by traditional interactive high-dimensional data visualization frameworks. To address these issues, we introduce a novel visualization system developed in conjunction with a livestock health surveillance network for interactive 3D visualization of high-dimensional data. Among the key features of the system is an XML framework for deployment of any high-dimensional data visualization tool to multiple heterogeneous display environments, including 3D stereoscopic displays and mobile devices.

Keywords

Visualization Tool Data Visualization Visualization System Data Mining Algorithm Visual Encode 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. 1.
    Compieta, P., Di-Martino, S., Bertolotto, M., Ferrucci, F., Kechadi, T.: Exploratory Spatio-Temporal Data Mining and Visualization. J. Vis. Lang. Comput. 18(3), 255–279 (2007)CrossRefGoogle Scholar
  2. 2.
    Wood, J., Dykes, J., Slingsby, A., Clarke, K.: Interactive Visual Exploration of a Large Spatio-temporal Dataset: Reflections on a Geovisualization Mashup. IEEE Transactions on Visualization and Computer Graphics 13(6), 1176–1183 (2007)CrossRefGoogle Scholar
  3. 3.
    Livnat, Y., Agutter, J., Moon, S., Foresti, S.: Visual correlation for situational awareness. In: IEEE Symposium on Information Visualization, pp. 95–102 (2005)Google Scholar
  4. 4.
    Lawton, G.: Users Take a Close Look at Visual Analytics. IEEE Computer Magazine 42(2), 19–22 (2009)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Thomas, J.J., Cook, K.A.: Illuminating the Path: The Research and Development Agenda for Visual Analytics. IEEE CS Press, Los Alamitos (2005)Google Scholar
  6. 6.
    Ferreira de Oliveira, M.C., Levkowitz, H.: From Visual Data Exploration to Visual Data Mining: A Survey. IEEE Transactions on Visualization and Computer Graphics 9(3), 378–394 (2003)CrossRefGoogle Scholar
  7. 7.
    Gross, M.H., Sprenger, T.C., Finger, J.: Visualizing Information on a Sphere. In: Proc. IEEE Information Visualization 1997, pp. 11–16 (1997)Google Scholar
  8. 8.
    Jorgensen, E.: Calibration of a Monte Carlo Simulation Model of Disease Spread in Slaughter Pig Units. Computers and Electronics in Agriculture 25(3), 245–259 (2000)CrossRefGoogle Scholar
  9. 9.
    Guo, D.: Visual Analytics of Spatial Interaction Patterns for Pandemic Decision Support. International Journal of Geographical Information Science 28(8), 859–877 (2007)CrossRefGoogle Scholar
  10. 10.
    Ghoniem, M., Fekete, J.D., Castagliola, P.: On the Readability of Graphs Using Node-Link and Matrix-Based Representations: A Controlled Experiment and Statistical Analysis. Information Visualization 4(2), 114–135 (2005)CrossRefGoogle Scholar
  11. 11.
    Heath, F.M., Vernon, M.C., Webb, C.R.: Construction of Networks with Intrinsic Temporal Structure from UK Cattle Movement Data. BMC Veterinary Research, 4 (2008) Google Scholar
  12. 12.
    Bailey-Kellogg, C., Ramakrishnan, N., Marathe, M.V.: Mining and Visualizing Spatial Interaction Patterns for Pandemic Response. ACM SIGKDD Explorations Newsletter, 80–82 (2006)Google Scholar
  13. 13.
    Beshers, C.G., Feiner, S.K.: Visualizing n-Dimensional Virtual Worlds within n-Vision. ACM SIGGRAPH Computer Graphics 24(2), 37–38 (1990)CrossRefGoogle Scholar
  14. 14.
    Gehegan, M., Hardisty, F., Demsar, U., Takatsuka, M.: GeoVISTA Studio: Reusability by Design. In: Hall, G.B., Leahy, M.G. (eds.) Advances in Geographic Information Science: Open Source Approaches in Spatial Data Handling, vol. 2, pp. 201–220 (2008)Google Scholar
  15. 15.
    Proulx, P., Chien, L., Harper, R., Schroh, D., Kapler, T., Jonker, D., Wright, W.: nSpace and GeoTime: A VAST 2006 Case Study. IEEE Computer Graphics and Applications 27(5), 46–56 (2007)CrossRefGoogle Scholar
  16. 16.
  17. 17.
    Ma, W.Y., Bedner, I., Chang, G., Kuchinsky, A., Zhang, H.: Framework for Adaptive Content Delivery in Heterogeneous Network Environments. In: Proc. SPIE, vol. 3969(86) (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nathaniel Rossol
    • 1
  • Irene Cheng
    • 1
  • John Berezowski
    • 2
  • Iqbal Jamal
    • 3
  1. 1.Computing Science DepartmentUniversity of AlbertaCanada
  2. 2.Alberta Agriculture and Rural DevelopmentGovernment of AlbertaCanada
  3. 3.AQL Management Consulting Inc.Canada

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