Abstract
Flow visualization techniques are vastly used to visualize scientific data among many fields including meteorology, computational fluid dynamics, medical visualization and aerodynamics. In this paper, we employ flow visualization techniques in conjunction with conventional network visualization methods to represent geographic network traffic data. The proposed visualization system integrates two visualization techniques, flow visualization and node-link diagram. While flow visualization emphasizes on general trends, node-link diagram visualization concentrates on the detailed analysis of the data. A usability study with multiple experiments is performed to evaluate the success of our approach.
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References
Becker, R.A., Eick, S.G., Wilks, A.R.: Visualizing network data. IEEE Trans. Visual Comput. Graphics 1(1), 16–28 (1995)
Breitkreutz, B.J., Stark, C., Tyers, M., et al.: Osprey: a network visualization system. Genome Biol. 4(3), R22 (2003)
Cabral, B., Leedom, L.C.: Imaging vector fields using line integral convolution. In: Proceedings of the 20th Annual Conference on Computer Graphics and Interactive Techniques, pp. 263–270. ACM (1993)
Dykes, J., MacEachren, A.M., Kraak, M.J.: Exploring Geovisualization. Elsevier, Amsterdam (2005)
Engin, B., Bozkaya, B., Balcisoy, S.: Introducing level of detail to 3d thematic maps. In: ICA GeoViz (2009)
Fayyad, U.M., Wierse, A., Grinstein, G.G.: Information Visualization in Data Mining and Knowledge Discovery. Morgan Kaufmann, San Francisco (2002)
Hoppe, H.: Smooth view-dependent level-of-detail control and its application to terrain rendering. In: Visualization 1998, Proceedings, pp. 35–42. IEEE (1998)
Huffaker, B., Nemeth, E., Claffy, K.: Otter: A general-purpose network visualization tool. In: Proceedings of the 9th Annual Conference of the Internet Society (1999)
Laramee, R.S., Hauser, H., Doleisch, H., Vrolijk, B., Post, F.H., Weiskopf, D.: The state of the art in flow visualization: Dense and texture-based techniques. In: Computer Graphics Forum, vol. 23, pp. 203–221. Wiley Online Library (2004)
Luebke, D.P.: Level of Detail for 3D Graphics. Morgan Kaufmann, Boston (2003)
Scheuermann, G., Burbach, H., Hagen, H.: Visualizing planar vector fields with normal component using line integral convolution. In: Proceedings of the Conference on Visualization 1999: Celebrating Ten Years, pp. 255–261. IEEE Computer Society Press (1999)
Shen, H.W., Kao, D.L.: A new line integral convolution algorithm for visualizing time-varying flow fields. IEEE Trans. Visual Comput. Graphics 4(2), 98–108 (1998)
Shneiderman, B., Aris, A.: Network visualization by semantic substrates. IEEE Trans. Visual Comput. Graphics 12(5), 733–740 (2006)
Stalling, D., Hege, H.C.: Fast and resolution independent line integral convolution. In: Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques, pp. 249–256. ACM (1995)
Van Wijk, J.J.: Spot noise texture synthesis for data visualization. In: ACM Siggraph Computer Graphics, vol. 25, pp. 309–318. ACM (1991)
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Kaya, B., Balcisoy, S. (2016). Multi-Resolution Visualisation of Geographic Network Traffic. In: De Paolis, L., Mongelli, A. (eds) Augmented Reality, Virtual Reality, and Computer Graphics. AVR 2016. Lecture Notes in Computer Science(), vol 9768. Springer, Cham. https://doi.org/10.1007/978-3-319-40621-3_4
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DOI: https://doi.org/10.1007/978-3-319-40621-3_4
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