Advertisement

FeatureFlow: exploring feature evolution for time-varying volume data

  • Zhihui Bai
  • Yubo TaoEmail author
  • Hai Lin
Regular Paper
  • 9 Downloads

Abstract

Time-varying volume data generated from scientific simulations are generally temporal and contain dynamic and complex features. The evolution of these features is important to understand the phenomena hidden in the data. In this paper, we introduce FeatureFlow, which is a novel visualization technique revealing feature evolution based on a hierarchical river metaphor. FeatureFlow decomposes the entire feature evolution into multiple levels and exploits an evolution measure to quantify the changes of the features. FeatureFlow visually summarizes the hierarchical evolution, the evolution value, and associated attributes to intuitively display the complex 4D spatial-temporal feature evolution in 2D. In addition, FeatureFlow converts each river into a string based on the serial ordering of evolutionary events and supports evolutionary pattern-matching queries. Experiments on three time-varying volume data sets and feedback from two domain experts demonstrate the utility of FeatureFlow in effectively helping users understand and explore feature evolution in time-varying volume data.

Graphic abstract

Keywords

Time-varying volume data Feature evolution Hierarchical river metaphor Pattern-matching query 

Notes

Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments. This work was supported by the National Key Research & Development Program of China (2017YFB0202203), National Natural Science Foundation of China (61672452 and 61890954), and NSFC-Guangdong Joint Fund (U1611263).

Supplementary material

12650_2019_578_MOESM1_ESM.mp4 (9.4 mb)
Supplementary material 1 (mp4 9668 KB)

References

  1. Bremer P, Weber GH, Tierny J, Pascucci V, Day MS, Bell JB (2009) A topological framework for the interactive exploration of large scale turbulent combustion. In: 2009 fifth IEEE international conference on e-science, pp 247–254.  https://doi.org/10.1109/e-Science.2009.42
  2. Bremer PT, Weber G, Tierny J, Pascucci V, Day M, Bell J (2011) Interactive exploration and analysis of large-scale simulations using topology-based data segmentation. IEEE Trans Vis Comput Graph 17(9):1307–1324CrossRefGoogle Scholar
  3. Cui W, Liu S, Tan L, Shi C, Song Y, Gao ZJ, Qu H, Tong X (2011) Textflow: towards better understanding of evolving topics in text. IEEE Trans Vis Comput Graph 17(12):2412–2421CrossRefGoogle Scholar
  4. Eades P, Kelly D (1986) Heuristics for reducing crossings in 2-layered networks. Ars Combinatoria 21A:89–98zbMATHGoogle Scholar
  5. Fruchterman TM, Reingold EM (1991) Graph drawing by force-directed placement. Softw Pract Exp 21(11):1129–1164CrossRefGoogle Scholar
  6. Graphviz—graph visualization software. http://www.graphviz.org/. Accessed 23 Oct 2018
  7. Havre S, Hetzler E, Whitney P, Nowell L (2002) Themeriver: visualizing thematic changes in large document collections. IEEE Trans Vis Comput Graph 8(1):9–20CrossRefGoogle Scholar
  8. Laney D, Mascarenhas A, Miller P, Pascucci V et al (2006) Understanding the structure of the turbulent mixing layer in hydrodynamic instabilities. IEEE Trans Vis Comput Graph 12(5):1053–1060CrossRefGoogle Scholar
  9. Liu S, Wu Y, Wei E, Liu M, Liu Y (2013) Storyflow: tracking the evolution of stories. IEEE Trans Vis Comput Graph 19(12):2436–2445CrossRefGoogle Scholar
  10. Lukasczyk J, Aldrich G, Steptoe M, Favelier G, Gueunet C, Tierny J, Maciejewski R, Hamann B, Leitte H (2017a) Viscous fingering: a topological visual analytic approach. Appl Mech Mater 869(8):9–19CrossRefGoogle Scholar
  11. Lukasczyk J, Weber GH, Maciejewski R, Garth C, Leitte H (2017b) Nested tracking graphs. Comput Graph Forum (EuroVis 2017) 36(3):12–22CrossRefGoogle Scholar
  12. Moreland K (2009) Diverging color maps for scientific visualization. In: Proceedings of the 5th international symposium on advances in visual computing: part II. ISVC '09, Las Vegas, Nevada. Springer-Verlag, Berlin, Heidelberg, pp 92–103.  https://doi.org/10.1007/978-3-642-10520-3_9
  13. Munroe R (2009) Xkcd# 657: Movie narrative charts. https://xkcd.com/657/. Accessed 1 Aug 2019
  14. Namevoyager. http://babynamewizard.com/namevoyager/lnv0105.html. Accessed 31 Oct 2018
  15. Sato Y, Westin CF, Bhalerao A, Nakajima S, Shiraga N, Tamura S, Kikinis R (2000) Tissue classification based on 3D local intensity structures for volume rendering. IEEE Trans Vis Comput Graph 6(2):160–180CrossRefGoogle Scholar
  16. Sauber N, Theisel H, Seidel HP (2006) Multifield-graphs: an approach to visualizing correlations in multifield scalar data. IEEE Trans Vis Comput Graph 12(5):917–924CrossRefGoogle Scholar
  17. Silver D, Wang X (1997) Tracking and visualizing turbulent 3D features. IEEE Trans Vis Comput Graph 3(2):129–141CrossRefGoogle Scholar
  18. Widanagamaachchi W, Christensen C, Pascucci V, Bremer P (2012) Interactive exploration of large-scale time-varying data using dynamic tracking graphs. In: IEEE symposium on large data analysis and visualization (LDAV), pp 9–17.  https://doi.org/10.1109/LDAV.2012.6378962
  19. Widanagamaachchi W, Jacques A, Wang B, Crosman E, Bremer PT, Pascucci V, Horel J (2017) Exploring the evolution of pressure-perturbations to understand atmospheric phenomena. In: IEEE pacific visualization symposium (PacificVis), pp. 101–110Google Scholar

Copyright information

© The Visualization Society of Japan 2019

Authors and Affiliations

  1. 1.State Key Lab of CAD&CGZhejiang UniversityHangzhouChina

Personalised recommendations