Document Open Access Logo

Visitation Graphs: Interactive Ensemble Visualization with Visitation Maps

Authors Anna-Pia Lohfink , Christoph Garth

PDF
Thumbnail PDF

File

OASIcs.iPMVM.2020.4.pdf
  • Filesize: 2.09 MB
  • 20 pages

Document Identifiers

Author Details

Anna-Pia Lohfink
  • Technische Universität Kaiserlautern, Germany
Christoph Garth
  • Technische Universität Kaiserlautern, Germany

Funding

This research was funded by the Deutsche Forschungsgemeinschaft (DFG – German Research Foundation) under contract 252408385 as part of IRTG 2057 Physical Modeling for Virtual Manufacturing.

Cite AsGet BibTex

Anna-Pia Lohfink and Christoph Garth. Visitation Graphs: Interactive Ensemble Visualization with Visitation Maps. In 2nd International Conference of the DFG International Research Training Group 2057 – Physical Modeling for Virtual Manufacturing (iPMVM 2020). Open Access Series in Informatics (OASIcs), Volume 89, pp. 4:1-4:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/OASIcs.iPMVM.2020.4

Abstract

Modern applications in computational science are increasingly focusing on understanding uncertainty in models and parameters in simulations. In this paper, we describe visitation graphs, a novel approximation technique for the well-established visualization of steady 2D vector field ensembles using visitation maps. Our method allows the efficient and robust computation of arbitrary visitation maps for vector field ensembles. A pre-processing step that can be parallelized to a high degree eschews the needs to store every ensemble member and to re-calculate every time the start position of the visitation map is changed. Tradeoffs between accuracy of generated visitation maps on one side and pre-processing time and storage requirements on the other side can be made. Instead of downsampling ensemble members to a storable size, coarse visitation graphs can be stored, giving more accurate visitation maps while still reducing the amount of data. Thus accurate visitation map creation is possible for ensembles where the traditional visitation map creation is prohibitive. We describe our approach in detail and demonstrate its effectiveness and utility on examples from Computational Fluid Dynamics.

Subject Classification

ACM Subject Classification
  • Human-centered computing → Visualization systems and tools
Keywords
  • Uncertain flow visualization
  • Ensemble visualization
  • Visitation maps
  • In-situ

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

References

  1. A. Agranovsky, D. Camp, C. Garth, E. W. Bethel, K. I. Joy, and H. Childs. Improved post hoc flow analysis via lagrangian representations. In 2014 IEEE 4th Symposium on Large Data Analysis and Visualization (LDAV), pages 67-75, November 2014. URL: https://doi.org/10.1109/LDAV.2014.7013206.
  2. W. Berger, H. Piringer, P. Filzmoser, and E. Gröller. Uncertainty-aware exploration of continuous parameter spaces using multivariate prediction. Computer Graphics Forum, 30(3):911-920, 2011. URL: https://doi.org/10.1111/j.1467-8659.2011.01940.x.
  3. Ryan A. Boller, Scott A. Braun, Jadrian Miles, and David H. Laidlaw. Application of uncertainty visualization methods to meteorological trajectories. Earth Science Informatics, 3(1):119-126, June 2010. URL: https://doi.org/10.1007/s12145-010-0052-5.
  4. Kai Bürger, Roland Fraedrich, Dorit Merhof, and Rüdiger Westermann. Instant visitation maps for interactive visualization of uncertain particle trajectories. In IS&T/SPIE Electronic Imaging, pages 82940P-82940P. International Society for Optics and Photonics, 2012. Google Scholar
  5. Hank Childs. Data exploration at the exascale. Supercomputing frontiers and innovations, 2(3):5-13, 2015. Google Scholar
  6. Jonathan Cox, Donald House, and Michael Lindell. Visualizing uncertainty in predicted hurricane tracks. International Journal for Uncertainty Quantification, 3(2):143-156, 2013. Google Scholar
  7. John Dill, Rae Earnshaw, David Kasik, John Vince, and Pak Chung Wong. Expanding the frontiers of visual analytics and visualization. Springer, 2012. Google Scholar
  8. Soumya Dutta, Chun-Ming Chen, Gregory Heinlein, Han-Wei Shen, and Jen-Ping Chen. In situ distribution guided analysis and visualization of transonic jet engine simulations. IEEE Transactions on Visualization and Computer Graphics, 23(1):811-820, 2017. Google Scholar
  9. Nathan Fabian, Kenneth Moreland, David Thompson, Andrew C Bauer, Pat Marion, Berk Gevecik, Michel Rasquin, and Kenneth E Jansen. The paraview coprocessing library: A scalable, general purpose in situ visualization library. In Large Data Analysis and Visualization (LDAV), 2011 IEEE Symposium on, pages 89-96. IEEE, 2011. Google Scholar
  10. D. Feng, L. Kwock, Y. Lee, and R. Taylor. Matching visual saliency to confidence in plots of uncertain data. IEEE Transactions on Visualization and Computer Graphics, 16(6):980-989, November 2010. URL: https://doi.org/10.1109/TVCG.2010.176.
  11. David Feng, Lester Kwock, Yueh Lee, and Russel M. Taylor. Linked exploratory visualizations for uncertain mr spectroscopy data. Proc.SPIE, 7530:7530-7530-12, 2010. URL: https://doi.org/10.1117/12.839818.
  12. Florian Ferstl, Kai Bürger, and Rüdiger Westermann. Streamline variability plots for characterizing the uncertainty in vector field ensembles. IEEE Transactions on Visualization and Computer Graphics, 22(1):767-776, 2016. Google Scholar
  13. N. Fout and K. L. Ma. Fuzzy volume rendering. IEEE Transactions on Visualization and Computer Graphics, 18(12):2335-2344, December 2012. URL: https://doi.org/10.1109/TVCG.2012.227.
  14. Yi Gu and Chaoli Wang. Transgraph: Hierarchical exploration of transition relationships in time-varying volumetric data. IEEE Transactions on Visualization and Computer Graphics, 17(12):2015-2024, 2011. Google Scholar
  15. Charles D Hansen, Min Chen, Christopher R Johnson, Arie E Kaufman, and Hans Hagen. Scientific visualization: uncertainty, multifield, biomedical, and scalable visualization. Springer, 2014. Google Scholar
  16. C. Heine, H. Leitte, M. Hlawitschka, F. Iuricich, L. De Floriani, G. Scheuermann, H. Hagen, and C. Garth. A survey of topology-based methods in visualization. Computer Graphics Forum, 35(3):643-667, 2016. URL: https://doi.org/10.1111/cgf.12933.
  17. Marcel Hlawatsch, Philipp Leube, Wolfgang Nowak, and Daniel Weiskopf. Flow radar glyphs - static visualization of unsteady flow with uncertainty. IEEE Transactions on Visualization and Computer Graphics, 17(12):1949-1958, 2011. Google Scholar
  18. M. Hummel, H. Obermaier, C. Garth, and K. I. Joy. Comparative visual analysis of lagrangian transport in cfd ensembles. IEEE Transactions on Visualization and Computer Graphics, 19(12):2743-2752, December 2013. URL: https://doi.org/10.1109/TVCG.2013.141.
  19. Heike Jänicke and Gerik Scheuermann. Visual analysis of flow features using information theory. IEEE Computer Graphics and Applications, 30(1):40-49, 2010. Google Scholar
  20. M. Jarema, J. Kehrer, and R. Westermann. Comparative visual analysis of transport variability in flow ensembles. Journal of WSCG., 24(1):25-34, 2016. URL: http://hdl.handle.net/11025/21642.
  21. D. K. Jones. Tractography gone wild: Probabilistic fibre tracking using the wild bootstrap with diffusion tensor mri. IEEE Transactions on Medical Imaging, 27(9):1268-1274, September 2008. URL: https://doi.org/10.1109/TMI.2008.922191.
  22. Derek K. Jones and Carlo Pierpaoli. Confidence mapping in diffusion tensor magnetic resonance imaging tractography using a bootstrap approach. Magnetic Resonance in Medicine, 53(5):1143-1149, 2005. URL: https://doi.org/10.1002/mrm.20466.
  23. Sriram Lakshminarasimhan, Neil Shah, Stephane Ethier, Scott Klasky, Rob Latham, Rob Ross, and Nagiza F. Samatova. Compressing the incompressible with isabela: In-situ reduction of spatio-temporal data. In Emmanuel Jeannot, Raymond Namyst, and Jean Roman, editors, Euro-Par 2011 Parallel Processing, pages 366-379, Berlin, Heidelberg, 2011. Springer Berlin Heidelberg. Google Scholar
  24. S. Li, N. Marsaglia, C. Garth, J. Woodring, J. Clyne, and H. Childs. Data reduction techniques for simulation, visualization and data analysis. Computer Graphics Forum, 37(6):422-447, 2018. URL: https://doi.org/10.1111/cgf.13336.
  25. Jay F Lofstead, Scott Klasky, Karsten Schwan, Norbert Podhorszki, and Chen Jin. Flexible io and integration for scientific codes through the adaptable io system (adios). In Proceedings of the 6th international workshop on Challenges of large applications in distributed environments, pages 15-24. ACM, 2008. Google Scholar
  26. A. L. Love, A. Pang, and D. L. Kao. Visualizing spatial multivalue data. IEEE Computer Graphics and Applications, 25(3):69-79, May 2005. URL: https://doi.org/10.1109/MCG.2005.71.
  27. Jun Ma, Chaoli Wang, and Ching-Kuang Shene. Flowgraph: A compound hierarchical graph for flow field exploration. In 2013 IEEE Pacific Visualization Symposium (PacificVis), pages 233-240. IEEE, 2013. Google Scholar
  28. Mahsa Mirzargar, Ross T Whitaker, and Robert M Kirby. Curve boxplot: Generalization of boxplot for ensembles of curves. IEEE Transactions on Visualization and Computer Graphics, 20(12):2654-2663, 2014. Google Scholar
  29. Boonthanome Nouanesengsy, Teng-Yok Lee, and Han-Wei Shen. Load-balanced parallel streamline generation on large scale vector fields. IEEE Transactions on Visualization and Computer Graphics, 17(12):1785-1794, 2011. Google Scholar
  30. M. Otto, T. Germer, and H. Theisel. Uncertain topology of 3d vector fields. In 2011 IEEE Pacific Visualization Symposium, pages 67-74, March 2011. URL: https://doi.org/10.1109/PACIFICVIS.2011.5742374.
  31. Mathias Otto, Tobias Germer, Hans-Christian Hege, and Holger Theisel. Uncertain 2d vector field topology. Computer Graphics Forum, 29(2):347-356, 2010. URL: https://doi.org/10.1111/j.1467-8659.2009.01604.x.
  32. Tobias Pfaffelmoser, Matthias Reitinger, and Rüdiger Westermann. Visualizing the positional and geometrical variability of isosurfaces in uncertain scalar fields. Computer Graphics Forum, 30(3):951-960, 2011. URL: https://doi.org/10.1111/j.1467-8659.2011.01944.x.
  33. K. Pothkow and H. C. Hege. Positional uncertainty of isocontours: Condition analysis and probabilistic measures. IEEE Transactions on Visualization and Computer Graphics, 17(10):1393-1406, October 2011. URL: https://doi.org/10.1109/TVCG.2010.247.
  34. Dave Pugmire, Hank Childs, Christoph Garth, Sean Ahern, and Gunther H. Weber. Scalable computation of streamlines on very large datasets. In Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis, SC '09, pages 16:1-16:12, New York, NY, USA, 2009. ACM. URL: https://doi.org/10.1145/1654059.1654076.
  35. Jibonananda Sanyal, Song Zhang, Jamie Dyer, Andrew Mercer, Philip Amburn, and Robert Moorhead. Noodles: A tool for visualization of numerical weather model ensemble uncertainty. IEEE Transactions on Visualization and Computer Graphics, 16(6):1421-1430, 2010. Google Scholar
  36. Dominic Schneider, Jan Fuhrmann, Wieland Reich, and Gerik Scheuermann. A Variance Based FTLE-Like Method for Unsteady Uncertain Vector Fields, pages 255-268. Springer Berlin Heidelberg, Berlin, Heidelberg, 2012. URL: https://doi.org/10.1007/978-3-642-23175-9_17.
  37. L. Sevilla-Lara and E. Learned-Miller. Distribution fields for tracking. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 1910-1917, June 2012. URL: https://doi.org/10.1109/CVPR.2012.6247891.
  38. Andrzej Szymczak. Morse connection graphs for piecewise constant vector fields on surfaces. Computer Aided Geometric Design, 30(6):529-541, 2013. Google Scholar
  39. Chaoli Wang and Jun Tao. Graphs in scientific visualization: A survey. Computer Graphics Forum, 36(1):263-287, 2017. URL: https://doi.org/10.1111/cgf.12800.
  40. Ross T Whitaker, Mahsa Mirzargar, and Robert M Kirby. Contour boxplots: A method for characterizing uncertainty in feature sets from simulation ensembles. IEEE Transactions on Visualization and Computer Graphics, 19(12):2713-2722, 2013. Google Scholar
  41. Brad Whitlock, Jean M. Favre, and Jeremy S. Meredith. Parallel in situ coupling of simulation with a fully featured visualization system. In Proceedings of the 11th Eurographics Conference on Parallel Graphics and Visualization, EGPGV '11, pages 101-109, Aire-la-Ville, Switzerland, Switzerland, 2011. Eurographics Association. URL: https://doi.org/10.2312/EGPGV/EGPGV11/101-109.
  42. Lijie Xu and Han-Wei Shen. Flow web: A graph based user interface for 3d flow field exploration. In IS&T/SPIE Electronic Imaging, pages 75300F-75300F. International Society for Optics and Photonics, 2010. Google Scholar
  43. Björn Zehner, Norihiro Watanabe, and Olaf Kolditz. Visualization of gridded scalar data with uncertainty in geosciences. Computers & Geosciences, 36(10):1268-1275, 2010. URL: https://doi.org/10.1016/j.cageo.2010.02.010.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact