Ensemble Visualization of Bottlenecks in Planar Flow Networks

Article Preview

Abstract:

The problem of finding bottlenecks in flow networks often appears in real world applicationslike production planning, factory layout, flow related physical approaches and even cyber security. This work introduces intuitive visual mechanisms to enable domain experts and users to visually analyzestable regions of a network and identify critical transitions. Those transitions form a varyingbottleneck front for different configurations of network restraints. To tackle this problem, this workenhances the comparability of different network configurations by utilizing ensemble visualizationtechniques. The effectiveness of this approach is demonstrated by showing how this enables users toevaluate the progress of different bottlenecks and individual regions in a flow network.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

234-243

Citation:

Online since:

August 2017

Export:

* - Corresponding Author

[1] B. Bach, E. Pietriga, and J. -D. Fekete. Graphdiaries: Animated transitions and temporal navigation for dynamic networks. IEEE Transactions on Visualization Computer Graphics, 20(5): 740- 754, (2014).

DOI: 10.1109/tvcg.2013.254

Google Scholar

[2] L. Borisjuk, M. Hajirezaei, C. Klukas, H. Rolletschek, and F. Schreiber. Integrating data from biological experiments into metabolic networks with the DBE information system. In Silico Biology, 5(2): 93-102, (2004).

Google Scholar

[3] I. Boyandin, E. Bertini, and D. Lalanne. A qualitative study on the exploration of temporal changes in flow maps with animation and small-multiples. Computer Graphics Forum, 31(3pt2): 1005-1014, (2012).

DOI: 10.1111/j.1467-8659.2012.03093.x

Google Scholar

[4] U. Brandes and S. R. Corman. Visual unrolling of network evolution and the analysis of dynamic discourse. Information Visualization, 2(1): 40-50, (2003).

DOI: 10.1057/palgrave.ivs.9500037

Google Scholar

[5] U. Brandes, S. Cornelsen, and D. Wagner. How to Draw the Minimum Cuts of a Planar Graph, pages 89-119. Springer Berlin Heidelberg, (2001).

DOI: 10.1007/3-540-44541-2_10

Google Scholar

[6] N. Cesario, A. Pang, and L. Singh. Visualizing node attribute uncertainty in graphs. Proc. SPIE, 7868: 78680H-78680H-13, (2011).

DOI: 10.1117/12.872677

Google Scholar

[7] J. Edmonds and R. M. Karp. Theoretical improvements in algorithmic efficiency for network flow problems. J. ACM, 19(2): 248-264, (1972).

DOI: 10.1145/321694.321699

Google Scholar

[8] P. Elias, A. Feinstein, and C. Shannon. A note on the maximum flow through a network. Information Theory, IEEE Transactions on, 2(4): 117-119, (1956).

DOI: 10.1109/tit.1956.1056816

Google Scholar

[9] L. R. Ford and D. R. Fulkerson. Maximal Flow through a Network. Canadian Journal of Mathematics, 8: 399-404, (1956).

DOI: 10.4153/cjm-1956-045-5

Google Scholar

[10] S. Fortune. Voronoi diagrams and delaunay triangulations. In J. E. Goodman and J. O'Rourke, editors, Handbook of Discrete and Computational Geometry, pages 377-388. CRC Press, Inc., (1997).

DOI: 10.1201/9781420035315.ch23

Google Scholar

[11] S. Hadlak, H. Schumann, and H. -J. Schulz. A Survey of Multi-faceted Graph Visualization. In R. Borgo, F. Ganovelli, and I. Viola, editors, Eurographics Conference on Visualization (EuroVis) - STARs. The Eurographics Association, (2015).

Google Scholar

[12] S. Halim. https: /visualgo. net/maxflow. online, (2017).

Google Scholar

[13] M. Itoh, D. Yokoyama, M. Toyoda, Y. Tomita, S. Kawamura, and M. Kitsuregawa. Visual exploration of changes in passenger flows and tweets on mega-city metro network. IEEE Transactions on Big Data, 2(1): 85-99, March (2016).

DOI: 10.1109/tbdata.2016.2546301

Google Scholar

[14] J. Jaffe. Bottleneck flow control. IEEE Transactions on Communications, 29(7): 954-962, (1981).

DOI: 10.1109/tcom.1981.1095081

Google Scholar

[15] M. Kikolski. Identification of production bottlenecks with the use of plant simulation software. Ekonomia i Zarzadzanie, 8(4): 103-112, (2017).

DOI: 10.1515/emj-2016-0038

Google Scholar

[16] S. Klamt, J. Saez-Rodriguez, and E. D. Gilles. Structural and functional analysis of cellular networks with cellnetanalyzer. BMC Systems Biology, 1: open access, (2007).

DOI: 10.1186/1752-0509-1-2

Google Scholar

[17] S. Klamt and A. von Kamp. An application programming interface for cellnetanalyzer. BioSystems, 105: 162-168, (2011).

DOI: 10.1016/j.biosystems.2011.02.002

Google Scholar

[18] C. G. Lee and S. C. Park. Survey on the virtual commissioning of manufacturing systems. Journal of Computational Design and Engineering, 1(3): 213 - 222, (2014).

Google Scholar

[19] C. Vehlow, F. Beck, and D. Weiskopf. Visualizing group structures in graphs: A survey. Computer Graphics Forum, pages n/a-n/a, (2016).

DOI: 10.1111/cgf.12872

Google Scholar

[20] H. Yu, P. M. Kim, E. Sprecher, V. Trifonov, and M. Gerstein. The importance of bottlenecks in protein networks: Correlation with gene essentiality and expression dynamics. PLoS Computational Biology, 3(4), (2007).

DOI: 10.1371/journal.pcbi.0030059

Google Scholar