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Issue No.12 - Dec. (2011 vol.17)
pp: 1872-1881
Jürgen Waser , VRVis Vienna
Hrvoje Ribičić , VRVis Vienna
Raphael Fuchs , ETH Zürich
Christian Hirsch , VRVis Vienna
Benjamin Schindler , ETH Zürich
Günther Blöschl , TU Vienna
Eduard Gröller , TU Vienna
ABSTRACT
Flood disasters are the most common natural risk and tremendous efforts are spent to improve their simulation and management. However, simulation-based investigation of actions that can be taken in case of flood emergencies is rarely done. This is in part due to the lack of a comprehensive framework which integrates and facilitates these efforts. In this paper, we tackle several problems which are related to steering a flood simulation. One issue is related to uncertainty. We need to account for uncertain knowledge about the environment, such as levee-breach locations. Furthermore, the steering process has to reveal how these uncertainties in the boundary conditions affect the confidence in the simulation outcome. Another important problem is that the simulation setup is often hidden in a black-box. We expose system internals and show that simulation steering can be comprehensible at the same time. This is important because the domain expert needs to be able to modify the simulation setup in order to include local knowledge and experience. In the proposed solution, users steer parameter studies through the World Lines interface to account for input uncertainties. The transport of steering information to the underlying data-flow components is handled by a novel meta-flow. The meta-flow is an extension to a standard data-flow network, comprising additional nodes and ropes to abstract parameter control. The meta-flow has a visual representation to inform the user about which control operations happen. Finally, we present the idea to use the data-flow diagram itself for visualizing steering information and simulation results. We discuss a case-study in collaboration with a domain expert who proposes different actions to protect a virtual city from imminent flooding. The key to choosing the best response strategy is the ability to compare different regions of the parameter space while retaining an understanding of what is happening inside the data-flow system.
INDEX TERMS
Emergency/Disaster Management, Visual Knowledge Discovery, Visualization System and Toolkit Design, Data-Flow, Meta-Flow, Parameter Study, Uncertainty, Visualization of Control.
CITATION
Jürgen Waser, Hrvoje Ribičić, Raphael Fuchs, Christian Hirsch, Benjamin Schindler, Günther Blöschl, Eduard Gröller, "Nodes on Ropes: A Comprehensive Data and Control Flow for Steering Ensemble Simulations", IEEE Transactions on Visualization & Computer Graphics, vol.17, no. 12, pp. 1872-1881, Dec. 2011, doi:10.1109/TVCG.2011.225
REFERENCES
[1] Mevislab: A development environment for medical image processing and visualization. http:/www.mevislab.de/(last visited on March, 17th 2011).
[2] Visdom - An integrated visualization system. http:/visdom.at(last visited on May, 11th 2011).
[3] World Lines Video. http://visdom.at/media/slidesworldlines.mp4 (last visited on March, 25th 2011).
[4] Advanced Visual Systems Inc. AVS - Advanced Visual System. http:/www.avs.com/ (last visited on March, 21st 2011).
[5] A. Amirkhanov, C. Heinzl, M. Reiter, and M. E. Gröller, Visual optimal-ity and stability analysis of 3dct scan positions. IEEE Transactions on Visualization and Computer Graphics, 16 (6): 1477 –1487, 2010.
[6] ANSYS, Inc. Explicit Dynamics, Chapter 10: Optimization Studies. http://www.cadfamily.com/download/CAE/ANSYS-Explicit Explicit_Dynamics_Optimization_ Studies.pdf (last visited on March, 2nd 2011), 2009.
[7] H. Apel, A. Thieken, B. Merz, and G. Blöschl, Flood risk assessment and associated uncertainty. Natural Hazards and Earth System Science, 4: 295–308, 2004.
[8] H. Apel, A. Thieken, B. Merz, and G. Blöschl, A probabilistic modeling system for assessing flood risks. Natural Hazards, 38: 79–100, 2006.
[9] L. Bartram, A. Ho, J. Dill, and F. Henigman, The continuous zoom: A constrained fisheye technique for viewing and navigating large information spaces. pages 207–215.ACM Press, 1995.
[10] W. Berger, H. Piringer, P. Filzmoser, and E. Gröller, Uncertainty-aware exploration of continuous parameter spaces using multivariate prediction. Computer Graphics Forum, 30: 911–920, 2011.
[11] Bestiario company. Impure - A web-based visual programming tool written in ActionScript to visualize data from the internet. http: /www.impure.com/ (last visited on February, 8th 2011).
[12] I. Bitter, R. Van Uitert, I. Wolf, L. Ibanez, and J.-M. Kuhnigk, Comparison of four freely available frameworks for image processing and visualization that use itk. IEEE Transactions on Visualization and Computer Graphics, 13 (3): 483–493, 2007.
[13] G. Blöschl, Flood warning - on the value of local information. Intl. J. River Basin Management, 6 (1): 41–50, 2008.
[14] S. Bruckner and T. Möller, Result-driven exploration of simulation parameter spaces for visual effects design. IEEE Transactions on Visualization and Computer Graphics, 16 (6): 1467–1475, 2010.
[15] G. Cameron, Modular visualization environments: past, present, and future. ACM SIGGRAPH Computer Graphics 1995, 29 (2): 3–4, 1995.
[16] Centre for Research on the Epidemiology of Disasters (CRED). 2010 disasters in numbers. http://cred.be/sites/default/filesPressConference2010.pdf (last visited on March, 3rd 2011).
[17] M. Chen, D. Ebert, H. Hagen, R. S. Laramee, R. van Liere, K.-L. Ma, W. Ribarsky, G. Scheuermann, and D. Silver, Data, information, and knowledge in visualization. IEEE Computer Graphics and Applications, 29: 12–19, January 2009.
[18] H. Childs, E. Brugger, K. Bonnell, J. Meredith, M. Miller, B. Whitlock, and N. Max, A contract based system for large data visualization. IEEE Transactions on Visualization and Computer Graphics, pages 190–198, 2005.
[19] H. Cloke and F. Pappenberger, Ensemble flood forecasting: a review. Journal of Hydrology, 375 (3-4): 613–626, 2009.
[20] C. Elliott, V. Vijayakumar, W. Zink, and R. Hansen, National instruments labview: A programming environment for laboratory automation and measurement. Journal of the Association for Laboratory Automation, 12 (1): 17–24, 2007.
[21] W. Felger and F. Schröder, The visualization input pipeline - enabling semantic interaction in scientific visualization. Computer Graphics Forum, 11: 139–151, 1992.
[22] R. B. Haber and D. A. McNabb, Visualization idioms: A conceptual model for scientific visualization systems. IEEE Visualization in Scientific Computing, pages 74–93, 1990.
[23] W. M. Johnston, J. R. P. Hanna, and R. J. Millar, Advances in dataflow programming languages. ACM Computing Surveys, 36 (1): 1–34, 2004.
[24] Kitware. Paraview - an open source, multi-platform data analysis and visualization application. http:/www.paraview.org/ (last visited on March, 17th 2011).
[25] J. Komma, C. Reszler, G. Blöschl, and T. Haiden, Ensemble prediction of floods - catchment non-linearity and forecast probabilities. Natural Hazards and Earth System Sciences, 7: 431–444, 2007.
[26] M. Marttila-Kontio and R. Honkanen, Not-so-free data flow in a visual data flow programming language. Computer Science and Information Technology, International Conference on, pages 613–619, 2009.
[27] K. Matković, H. Hauser, R. Sainitzer, and M. E. Gröller, Process visualization with levels of detail. In Proceedings IEEE Symposium on Information Visualization 2002 (InfoVis 2002), pages 67–70, 2002.
[28] J. Meyer-Spradow, T. Ropinski, J. Mensmann, and K. H. Hinrichs, Voreen: A rapid-prototyping environment for ray-casting-based volume visualizations. IEEE Computer Graphics and Applications, 29 (6): 6–13, 2009.
[29] National Instruments (NI). LabVIEW: A graphical programming environment for engineers to develop measurement, test, and control systems. http://www.ni.comlabview (last visited on March, 21st 2011).
[30] Numerical Algorithms Group (NAG). IRIS Explorer: A visual programming environment to develop, prototype and build visualization applications. http://www.nag.co.ukWelcome_IEC.asp (last visited on March, 21st 2011).
[31] I. P. on Climate Change (IPCC). Ipcc fourth assessment report: Climate change 2007 (ar4). http://www.ipcc.ch/publications_ and_data publications_and_data_reports.shtml (last visited on March, 17th 2011), 2007.
[32] S. G. Parker and C. R. Johnson, SCIRun: A scientific programming environment for computational steering. In Proceedings of the 1995 ACM/IEEE conference on Supercomputing, page 52, 1995.
[33] A. M. Pérez, J. M. Gómez, and R. C. Pérez, Semantic interaction in enterprise data-flow visualization environments: An exploratory study. In ICT Innovations 2009, pages 217–226. Springer Berlin Heidelberg, 2010.
[34] E. Santos, L. Lins, J. Ahrens, J. Freire, and C. Silva, VisMashup: Streamlining the creation of custom visualization applications. IEEE Transactions on Visualization and Computer Graphics, 15 (6): 1539–1546, 2009.
[35] C. Scheidegger, H. Vo, D. Koop, J. Freire, and C. Silva, Querying and creating visualizations by analogy. IEEE Transactions on Visualization and Computer Graphics, 13 (6): 1560 –1567, 2007.
[36] B. Schindler, J. Waser, R. Fuchs, and R. Peikert, Multiverse data-flow control. Technical Report 720, ETH Zürich Comp. Science, 2010.
[37] Scientific Computing and Imaging Institute (SCI). SCIRun: A scientific computing problem solving environment. http:/www.scirun.org (last visited on March, st 2011).
[38] C. T. Silva, J. Freire, and S. P. Callahan, Provenance for visualizations: Reproducibility and beyond. Computing in Science and Engineering, 9 (5): 82–89, 2007.
[39] M. G. Stewart and R. E. Melchers, Probabilistic risk assessment of engineering systems. Chapman and Hall, London, 1997.
[40] A. Telea and J. J. van Wijk, Vission: An object oriented dataflow system for simulation and visualization. In Proceedings IEEE Symposium on Visualization (VisSym 1999), pages 95–104, 1999.
[41] A. Telea and J. J. van Wijk, Smartlink: An agent for supporting dataflow application construction. In Proceedings IEEE Symposium on Visualization (VisSym 2000), pages 189–198, 2000.
[42] A. H. Thieken, M. Müller, H. Kreibich, and B. Merz, Flood damage and influencing factors: New insights from the August 2002 flood in Germany. Water Resources Research, 41: 16pp, 2005.
[43] C. Upson, T. Faulhaber Jr, D. Kamins, D. H. Laidlaw, D. Schlegel, J. Vroom, R. Gurwitz, and A. van Dam, The application visualization system: A computational environment for scientific visualization. IEEE Computer Graphics and Applications, 9: 30–42, July 1989.
[44] J. Waser, R. Fuchs, H. Ribicčić, B. Schindler, G. Blöschl, and M. E. Gröller, World Lines. IEEE Transactions on Visualization and Computer Graphics, 16 (6): 1458–1467, 2010.
[45] C. Weaver, Building highly-coordinated visualizations in Improvise. In Proceedings IEEE Symposium on Information Visualization 2004 (Info-Vis 2004), pages 159–166, 2004.
[46] C. Weaver, Visualizing coordination in situ. In Proceedings IEEE Symposium on Information Visualization 2005 (InfoVis 2005), pages 165–172, 2005.
[47] H. Wright, K. Brodlie, and M. Brown, The dataflow visualization pipeline as a problem solving environment. In Proceedings of the Eurographics Workshop on Virtual Environments and Scientific Visualization 1996, pages 267–276, 1996.
[48] H. Wright and J. Walton, Hyperscribe: A data management facility for the dataflow visualization pipeline. In IRIS Explorer Technical Report IETR/4, NAG Ltd, 1996.
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