This Article 
 Bibliographic References 
 Add to: 
Visual Analysis of Flow Features Using Information Theory
January/February 2010 (vol. 30 no. 1)
pp. 40-49
Heike Jänicke, Swansea University
Gerik Scheuermann, University of Leipzig
Unsteady scientific data is one of the top challenges in visualization, because a huge amount of information must be displayed. ϵ-machines are an information-theoretic concept; they compress the dynamics in the data set to a finite-state machine, in which nodes represent local flow patterns and edges represent transitions between them. Several enhancements to the fundamental ϵ-machine representation can help users identify interesting time intervals, analyze the evolution of unusual local dynamics, and track features over time. Automatically abstracting information from the original data is a first step toward knowledge-assisted visualization. Successive findings from analysis can help provide subsequent users with knowledge gained in earlier research, resulting in a knowledge-assisted system for the analysis of unsteady-flow features based on information theory. This article is part of a special issue on knowledge-assisted visualization.

1. E. Roeckner et al., The Atmospheric General Circulation Model ECHAM 5, Part I: Model Description, tech. report 349, Max Planck Inst. for Meteorology, 2003.
2. D. Salomon, Data Compression: The Complete Reference, Springer, 2000.
3. E. Gansner and Y. Koren, "Improved Circular Layouts," Graph Drawing, LNCS 4372, Springer, 2007, pp. 386–398.
1. J.P. Crutchfield and K. Young, "Inferring Statistical Complex-ity," Physical Rev. Letters, vol. 63, 1989, pp. 105–108.
2. C.R. Shalizi et al., "Automatic Filters for the Detection of Coherent Structures in Spatiotemporal Systems," Physical Rev. E, vol. 73, no. 3, 2006.
3. H. Jänicke and G. Scheuermann, "Steady Visualization of the Dynamics in Fluids Using ∊-machines," Computers & Graphics, vol. 33, no. 5, 2009, pp. 597–606.
4. H. Jänicke et al., "Automatic Detection and Visualization of Distinctive Structures in 3D Unsteady Multi-fields," Computer Graphics Forum, vol. 27, no. 3, 2008, pp. 767–774.
5. H. Jänicke et al., "Multifield Visualization Using Local Statistical Complexity," IEEE Trans. Visualization and Computer Graphics, vol. 13, no. 6, 2007, pp. 1384–1391.
1. M. Chen et al., "Data, Information, and Knowledge in Visu-alization," IEEE Computer Graphics and Applications, vol. 29, no. 1, 2009, pp. 12–19.

Index Terms:
scientific visualization, information theory, flow features, time-dependent data, computer graphics, graphics and multimedia
Heike Jänicke, Gerik Scheuermann, "Visual Analysis of Flow Features Using Information Theory," IEEE Computer Graphics and Applications, vol. 30, no. 1, pp. 40-49, Jan.-Feb. 2010, doi:10.1109/MCG.2010.17
Usage of this product signifies your acceptance of the Terms of Use.