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Event-based exploration and comparison on time-varying ensembles

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Abstract

We propose an event-based analysis system for comparison of several ensemble time-varying simulations. In this pipeline, users can customize the selection of events (i.e., the keyframes of the simulations) for each simulation on the timeline view. The associated rendered thumbnails are tiled in the rendered thumbnail view. The ticks on the timeline and the rendered thumbnails are connected by a link. Users are allowed to do 3D exploration on the render thumbnails and the high-resolution view on which the details can be displayed. Switching between different variables is supported to assist users in exploring the rendering of different ensemble variables or even combinations of variables. We apply our system into the deep water impact ensemble dataset. The system is proved to have the ability to help users better explore the simulations.

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References

  1. Abd-Almageed W (2008) Online, simultaneous shot boundary detection and key frame extraction for sports videos using rank tracing. In: 2008 15th IEEE international conference on image processing, pp 3200–3203

  2. Athawale T, Entezari A (2013) Uncertainty quantification in linear interpolation for isosurface extraction. IEEE Trans Vis Comput Graph 19(12):2723–2732

  3. Athawale T, Sakhaee E, Entezari A (2016) Isosurface visualization of data with nonparametric models for uncertainty. IEEE Trans Vis Comput Graph 22(1):777–786

  4. Bordoloi U, Kao DL, Shen H (2004) Visualization techniques for spatial probability density function data. Data Sci J 3:153–162

  5. Chen Y, Du X, Yuan X (2017) Ordered small multiple treemaps for visualizing time-varying hierarchical pesticide residue data. Vis Comput 33(6–8):1073–1084

  6. Demir I, Dick C, Westermann R (2014) Multi-charts for comparative 3d ensemble visualization. IEEE Trans Vis Comput Graph 20(12):2694–2703

  7. Inamura T, Tanie H, Nakamura Y (2003) Keyframe compression and decompression for time series data based on the continuous hidden Markov model. In: Proceedings of the of 2003 IEEE/RSJ international conference on intelligent robots and systems (IROS 2003) (Cat. No.03CH37453), vol 2, pp 1487–1492

  8. Kehrer J, Hauser H (2013) Visualization and visual analysis of multifaceted scientific data: a survey. IEEE Trans Vis Comput Graph 19(3):495–513

  9. Kumar G, Garland M (2006) Visual exploration of complex time-varying graphs. IEEE Trans Vis Comput Graph 12(5):805–812

  10. Li Y, Lu A, Ribarsky W, Wei C (2010) Automatic animation for time-varying data visualization. Comput Graph Forum 29(7):2271–2280

  11. Liu R, Guo H, Yuan X (2017) User-defined feature comparison for vector field ensembles. J Vis 20(2):217–229

  12. Lu A, Shen H (2008) Interactive storyboard for overall time-varying data visualization. In: Proceedings of 2008 IEEE Pacific visualization symposium, pp 143–150

  13. Luo A, Kao DT, Dungan JL, Pang A (2003) Visualizing spatial distribution data sets. In: Proceedings of symposium on visualization, pp 29–38

  14. Ma B, Entezari A (2019) An interactive framework for visualization of weather forecast ensembles. IEEE Trans Vis Comput Graph 25(1):1091–1101

  15. Obermaier H, Joy KI (2014) Future challenges for ensemble visualization. IEEE Comput Graph Appl 34(3):8–11

  16. Pfaffelmoser T, Westermann R (2013) Correlation visualization for structural uncertainty analysis. Int J Uncertain Quantif 3(2):171–186

  17. Pfaffelmoser T, Reitinger M, Westermann R (2011) Visualizing the positional and geometrical variability of isosurfaces in uncertain scalar fields. Comput Graph Forum 30(3):951–960

  18. Pöthkow K, Hege H (2011) Positional uncertainty of isocontours: condition analysis and probabilistic measures. IEEE Trans Vis Comput Graph 17(10):1393–1406

  19. Pöthkow K, Weber B, Hege H (2011) Probabilistic marching cubes. Comput Graph Forum 30(3):931–940

  20. Potter K, Wilson AT, Bremer P, Williams DN, Doutriaux CM, Pascucci V, Johnson CR (2009) Ensemble-vis: a framework for the statistical visualization of ensemble data. In: Proceedings of IEEE data mining workshops, pp 233–240

  21. Potter K, Kniss J, Riesenfeld RF, Johnson CR (2010) Visualizing summary statistics and uncertainty. Comput Graph Forum 29(3):823–832

  22. Potter K, Kirby M, Xiu D, Johnson CR (2012) Interactive visualization of probability and cumulative density functions. Int J Uncertain Quantif 2(4):397–412

  23. Rufiange S, McGuffin MJ (2013) Diffani: visualizing dynamic graphs with a hybrid of difference maps and animation. IEEE Trans Vis Comput Graph 19(12):2556–2565

  24. Sanyal J, Zhang S, Dyer J, Mercer A, Amburn P, Moorhead RJ (2010) Noodles: a tool for visualization of numerical weather model ensemble uncertainty. IEEE Trans Vis Comput Graph 16(6):1421–1430

  25. Shneiderman B (1996) The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings of IEEE symposium on visual languages. IEEE, pp 336–343

  26. Shu Q, Guo H, Liang J, Che L, Liu J, Yuan X (2016) Ensemblegraph: interactive visual analysis of spatiotemporal behaviors in ensemble simulation data. In: Proceedings of IEEE Pacific visualization symposium, pp 56–63

  27. Shu Q, Liu R, Hong F, Zhang J, Yuan X (2018) State-of-the-art of ensemble visualization. J Softw 29(2):506–523 (in Chinese)

  28. Süßmuth J, Winter M, Greiner G (2008) Reconstructing animated meshes from time-varying point clouds. Comput Graph Forum 27(5):1469–1476

  29. Tao J, Imre M, Wang C, Chawla NV, Guo H, Sever G, Kim SH (2019) Exploring time-varying multivariate volume data using matrix of isosurface similarity maps. IEEE Trans Vis Comput Graph 25(1):1236–1245

  30. Thompson DC, Levine JA, Bennett J, Bremer P, Gyulassy A, Pascucci V, Pébay PP (2011) Analysis of large-scale scalar data using hixels. In: Proceedings of IEEE LDAV symposium, pp 23–30

  31. Wang J, Hazarika S, Li C, Shen H (2019) Visualization and visual analysis of ensemble data: a survey. IEEE Trans Vis Comput Graph 25(9):2853–2872. https://doi.org/10.1109/TVCG.2018.2853721

  32. Whitaker RT, Mirzargar M, Kirby RM (2013) Contour boxplots: a method for characterizing uncertainty in feature sets from simulation ensembles. IEEE Trans Vis Comput Graph 19(12):2713–2722

  33. Zhou B, Chiang Y-J (2018) Key time steps selection for large-scale time-varying volume datasets using an information-theoretic storyboard. Comput Graph Forum 37(3):37–49

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Funding

This work is supported by National Numerical Windtunnel Project NNW2018-ZT6B12 and the National Program on Key Basic Research Project (973 Program) No. 2015CB352503.

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Correspondence to Xiaoru Yuan.

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Liu, C., Li, Y., Yang, C. et al. Event-based exploration and comparison on time-varying ensembles. J Vis 23, 111–123 (2020). https://doi.org/10.1007/s12650-019-00608-y

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Keywords

  • Science visualization
  • Ensemble data
  • Asteroid impacting