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The European Physical Journal Special Topics

, Volume 227, Issue 14, pp 1741–1755 | Cite as

Multivariate visualization of particle data

  • Liang ZhouEmail author
  • Daniel WeiskopfEmail author
Review
  • 12 Downloads
Part of the following topical collections:
  1. Particle Methods in Natural Science and Engineering

Abstract

In this review paper, we review methods for interactive particle rendering techniques, multi-view particle visualization systems, multivariate visualization techniques, and methods for correlation visualizations. Visualization is vital for gaining insight into particle data. Multivariate particle data are generated to understand different aspects of the underlying physics. The visualization of multivariate particle data is typically performed in multiple linked view systems (multi-view systems) that render particles of interest that are selected by the user interactively with brushing-and-linking. To this end, the non-spatial aspects of particles are explored with multivariate visualization methods, e.g., scatter plots, scatter plot matrix, parallel coordinates, dimensional reduction and radial plots.

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References

  1. 1.
    A.R. Martin, M.O. Ward, High dimensional brushing for interactive exploration of multivariate data, in Proceedings of the 6th Conference on Visualization ‘95, VIS ‘95 (IEEE Computer Society, Washington, DC, USA, 1995), p. 271Google Scholar
  2. 2.
    S. Reinhardt, M. Huber, O. Dumitrescu, M. Krone, B. Eberhardt, D. Weiskopf, Visual debugging of SPH simulations, in 2017 21st International Conference Information Visualisation (IV), 11–14 July 2017 (IEEE, London, 2017), pp. 117–126Google Scholar
  3. 3.
    D.R. Lipşa, R.S. Laramee, S.J. Cox, J.C. Roberts, R. Walker, M.A. Borkin, H. Pfister, Comput. Graphics Forum 33, 2317 (2012)Google Scholar
  4. 4.
    S. Gumhold, Splatting illuminated ellipsoids with depth correction, in Proceedings of the Vision, Modeling, and Visualization Conference 2003 (VMV 2003), München, Germany, 19–21 November 2003 (2003), 245–252Google Scholar
  5. 5.
    G. Reina, T. Ertl, Hardware-accelerated glyphs for mono- and dipoles in molecular dynamics visualization, in EUROVIS 2005: Eurographics/IEEE VGTC Symposium on Visualization, edited by K. Brodlie, D. Duke, K. Joy (The Eurographics Association, 2005)Google Scholar
  6. 6.
    S. Grottel, M. Krone, C. Müller, G. Reina, T. Ertl, IEEE Trans. Visual. Comput. Graphics 21, 201 (2015)CrossRefGoogle Scholar
  7. 7.
    I. Wald, A. Knoll, G.P. Johnson, W. Usher, V. Pascucci, M.E. Papka, CPU ray tracing large particle data with balanced P-k-d trees, in 2015 IEEE Scientific Visualization Conference (SciVis), 25–30 October 2015 (IEEE, Chicago¸IL, USA, 2015), pp. 57–64Google Scholar
  8. 8.
    R. Fraedrich, S. Auer, R. Westermann, IEEE Trans. Visual. Comput. Graphics 16, 1533 (2010)CrossRefGoogle Scholar
  9. 9.
    M. Ihmsen, J. Orthmann, B. Solenthaler, A. Kolb, M. Teschner, SPH fluids in computer graphics, in Eurographics 2014 – State of the Art Reports, edited by S. Lefebvre, M. Spagnuolo (The Eurographics Association, 2014)Google Scholar
  10. 10.
    M.O. Ward, XmdvTool: integrating multiple methods for visualizing multivariate data, in Proceedings of the IEEE Visualization Conference (Washington, DC, 1994), pp. 326–333Google Scholar
  11. 11.
    L. Zhou, C. Hansen, Transfer function design based on user selected samples for intuitive multivariate volume exploration, in 2013 IEEE Pacific Visualization Symposium (PacificVis), 27 February–1 March 2013 (IEEE, Sydney, NSW, Australia, 2013)73–80Google Scholar
  12. 12.
    S. Bachthaler, D. Weiskopf, IEEE Trans. Visual. Comput. Graphics 14, 1428 (2008)CrossRefGoogle Scholar
  13. 13.
    H. Piringer, R. Kosara, H. Hauser, Interactive focus+context visualization with linked 2D/3D scatterplots, in Proceedings Second International Conference on Coordinated and Multiple Views in Exploratory Visualization, 2004, 13 July 2004 (IEEE, London, 2004), pp. 49–60Google Scholar
  14. 14.
    H. Doleisch, M. Mayer, M. Gasser, R. Wanker, H. Hauser, Case study: visual analysis of complex, time-dependent simulation results of a diesel exhaust system, in Eurographics/EEE VGTC Symposium on Visualization, edited by O. Deussen, C. Hansen, D. Keim, D. Saupe(The Eurographics Association, 2004)Google Scholar
  15. 15.
    O. Rubel, Prabhat , K. Wu, H. Childs, J. Meredith, C.G.R. Geddes, E. Cormier-Michel, S. Ahern, G.H. Weber, P. Messmer, H. Hagen, B. Hamann, E.W. Bethel, High performance multivariate visual data exploration for extremely large data, in SC ‘08: Proceedings of the 2008 ACM/IEEE Conference on Supercomputing, 15–21 November 2008 (IEEE, Austin, TX, USA, 2008), pp. 1–12Google Scholar
  16. 16.
    L. Linsen, T.V. Long, P. Rosenthal, S. Rosswog, IEEE Trans. Visual. Comput. Graphics 14, 1483 (2008)CrossRefGoogle Scholar
  17. 17.
    V. Molchanov, A. Fofonov, L. Linsen, Comput. Graphics Forum 32, 301 (2013)CrossRefGoogle Scholar
  18. 18.
    M. Ward, G. Grinstein, D. Keim, Multivariate density estimation: theory, practice, and visualization, 2nd edn. (A. K. Peters, Ltd, Natick, MA, USA, 2015)Google Scholar
  19. 19.
    D.A. Keim, IEEE Trans. Visual. Comput. Graphics 8, 1 (2002)CrossRefGoogle Scholar
  20. 20.
    N. Elmqvist, P. Dragicevic, J. Fekete, IEEE Trans. Visual. Comput. Graphics 14, 1539 (2008)CrossRefGoogle Scholar
  21. 21.
    A. Inselberg, Visual Comput. 1, 69 (1985)CrossRefGoogle Scholar
  22. 22.
    E.J. Wegman, J. Am. Stat. Assoc. 85, 664 (1990)CrossRefGoogle Scholar
  23. 23.
    J. Heinrich, D. Weiskopf, State of the art of parallel coordinates, in Eurographics 2013 – State of the Art Reports, edited by M. Sbert, L. Szirmay-Kalos(The Eurographics Association, 2013)Google Scholar
  24. 24.
    A. Inselberg, Parallel coordinates: visual multidimensional geometry and its applications (Springer, Berlin, 2009)Google Scholar
  25. 25.
    X. Kuang, H. Zhang, S. Zhao, M. McGuffin, Comput. Graphics Forum 31, 1365 (2012)CrossRefGoogle Scholar
  26. 26.
    R. Netzel, J. Vuong, U. Engelke, S. O’Donoghue, D. Weiskopf, J. Heinrich, Visual Inf. 1, 118 (2017)CrossRefGoogle Scholar
  27. 27.
    J.H.T. Claessen, J.J. van Wijk, IEEE Trans. Visual. Comput. Graphics 17, 2310 (2011)CrossRefGoogle Scholar
  28. 28.
    E. Kandogan, Star coordinates: a multi-dimensional visualization technique with uniform treatment of dimensions, in Proceedings of the IEEE Information Visualization Symposium, Late Breaking Hot Topics (Citeseer, 2000), pp. 9–12Google Scholar
  29. 29.
    P. Hoffman, G. Grinstein, K. Marx, I. Grosse, E. Stanley, DNA visual and analytic data mining, in Proceedings Visualization ‘97 (Cat. No. 97CB36155), 24 October 1997 (IEEE, Phoenix, AZ, USA, 1997), pp. 437–441Google Scholar
  30. 30.
    J.B. Tenenbaum, V.D. Silva, J.C. Langford, Science 290, 2319 (2000)ADSCrossRefGoogle Scholar
  31. 31.
    S.T. Roweis, L.K. Saul, Science 290, 2323 (2000)ADSCrossRefGoogle Scholar
  32. 32.
    M. Belkin, P. Niyogi, Neural Comput. 15, 1373 (2003)CrossRefGoogle Scholar
  33. 33.
    L. van der Maaten, G.E. Hinton, J. Mach. Learn. Res. 9, 2579 (2008)Google Scholar
  34. 34.
    J.A. Lee, M. Verleysen, Nonlinear dimensionality reduction (Springer-Verlag New York, New York, NY, USA, 2007)Google Scholar
  35. 35.
    W.S. Cleveland, S.J. Devlin, E. Grosse, J. Econ. 37, 87 (1988)CrossRefGoogle Scholar
  36. 36.
    H. Nguyen, P. Rosen, Improved identification of data correlations through correlation coordinate plots, in Proceedings of the International Conference on Information Visualization Theory and Applications (IVAPP) (SciTePress, 2016), Vol. 2, pp. 60–71Google Scholar
  37. 37.
    Y.H. Chan, C.D. Correa, K.L. Ma, Flow-based scatterplots for sensitivity analysis, in Proceedings of the IEEE Symposium on Visual Analytics Science and Technology (VAST), 25–26 October 2010 (IEEE, Salt Lake City, UT, USA, 2010), pp. 43–50Google Scholar
  38. 38.
    Y.H. Chan, C.D. Correa, K.L. Ma, IEEE Trans. Visual. Comput. Graphics 19, 1768 (2013)CrossRefGoogle Scholar
  39. 39.
    H. Sanftmann, D. Weiskopf, Comput. Graphics Forum 28, 751 (2009)CrossRefGoogle Scholar
  40. 40.
    H. Nguyen, P. Rosen, IEEE Trans. Visual. Comput. Graphics 24, 1301 (2018)CrossRefGoogle Scholar
  41. 41.
    L. Zhou, D. Weiskopf, IEEE Trans. Visual. Comput. Graphics 24, 1997 (2018)CrossRefGoogle Scholar
  42. 42.
    B.W. Silverman, Density estimation for statistics and data analysis (Chapman & Hall, London, 1986)Google Scholar
  43. 43.
    M. Novotny, H. Hauser, IEEE Trans. Visual. Comput. Graphics 12, 893 (2006)CrossRefGoogle Scholar
  44. 44.
    D.W. Scott, Multivariate density estimation and visualization (Springer, Berlin, Heidelberg, 2012), pp. 549–569Google Scholar
  45. 45.
    J. Heinrich, D. Weiskopf, IEEE Trans. Visual. Comput. Graphics 15, 1531 (2009)CrossRefGoogle Scholar
  46. 46.
    D.J. Lehmann, H. Theisel, IEEE Trans. Visual. Comput. Graphics 16, 1291 (2010)CrossRefGoogle Scholar
  47. 47.
    D.J. Lehmann, H. Theisel, IEEE Trans. Visual. Comput. Graphics 17, 1912 (2011)CrossRefGoogle Scholar
  48. 48.
    L. Zhou, C.D. Hansen, IEEE Trans. Visual. Comput. Graphics 22, 2051 (2016)CrossRefGoogle Scholar
  49. 49.
    L. Zhou, D. Weiskopf, Contrast enhancement based on viewing distance, in Proceedings of the 11th International Symposium on Visual Information Communication and Interaction, VINCI ‘18, Växjö, Sweden, 13–15 August 2018 (ACM, New York, NY, USA, 2018), pp. 25–32Google Scholar

Copyright information

© EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Visualization Research Center (VISUS), University of StuttgartStuttgartGermany

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