This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Model-Driven Design for the Visual Analysis of Heterogeneous Data
June 2012 (vol. 18 no. 6)
pp. 998-1010
A. Lex, Inst. for Comput. Graphics & Vision, Graz Univ. of Technol., Graz, Austria
H. Schulz, Dept. of Comput. Graphics, Univ. of Rostock, Rostock, Germany
M. Streit, Inst. for Comput. Graphics & Vision, Graz Univ. of Technol., Graz, Austria
D. Schmalstieg, Inst. for Comput. Graphics & Vision, Graz Univ. of Technol., Graz, Austria
H. Schumann, Dept. of Comput. Graphics, Univ. of Rostock, Rostock, Germany
As heterogeneous data from different sources are being increasingly linked, it becomes difficult for users to understand how the data are connected, to identify what means are suitable to analyze a given data set, or to find out how to proceed for a given analysis task. We target this challenge with a new model-driven design process that effectively codesigns aspects of data, view, analytics, and tasks. We achieve this by using the workflow of the analysis task as a trajectory through data, interactive views, and analytical processes. The benefits for the analysis session go well beyond the pure selection of appropriate data sets and range from providing orientation or even guidance along a preferred analysis path to a potential overall speedup, allowing data to be fetched ahead of time. We illustrate the design process for a biomedical use case that aims at determining a treatment plan for cancer patients from the visual analysis of a large, heterogeneous clinical data pool. As an example for how to apply the comprehensive design approach, we present Stack'n'flip, a sample implementation which tightly integrates visualizations of the actual data with a map of available data sets, views, and tasks, thus capturing and communicating the analytical workflow through the required data sets.

[1] J.J. Thomas and K.A. Cook, Illuminating the Path: The Research and Development Agenda for Visual Analytics. IEEE Press, 2005.
[2] V.L. O'Day and R. Jeffries, “Orienteering in an Information Landscape: How Information Seekers Get from Here to There,” Proc. SIGCHI Conf. Human Factors in Computing Systems (INTERACT '93 and CHI '93), pp. 438-445, 1993.
[3] T. Jankun-Kelly and K. Ma, “Visualization Exploration and Encapsulation via a Spreadsheet-Like Interface,” IEEE Trans. Visualization and Computer Graphics, vol. 7, no. 3, pp. 275-287, July-Sept. 2001.
[4] D. Gotz and Z. Wen, “Behavior-Driven Visualization Recommendation,” Proc. Conf. Intelligent User Interfaces (IUI '09), pp. 315-324, 2009.
[5] W. Willett, J. Heer, and M. Agrawala, “Scented Widgets: Improving Navigation Cues with Embedded Visualizations,” IEEE Trans. Visualization and Computer Graphics, vol. 13, no. 6, pp. 1129-1136, Nov./Dec. 2007.
[6] D. Koop, C.E. Scheidegger, S.P. Callahan, H.T. Vo, J. Freire, and C.T. Silva, “VisComplete: Automating Suggestions for Visualization Pipelines,” IEEE Trans. Visualization and Computer Graphics, vol. 14, no. 6, pp. 1691-1698, Nov./Dec. 2008.
[7] A. Perer and B. Shneiderman, “Systematic yet Flexible Discovery: Guiding Domain Experts through Exploratory Data Analysis,” Proc. ACM Conf. Intelligent User Interfaces (IUI '08), pp. 109-118, 2008.
[8] M. Streit, H. Schulz, D. Schmalstieg, and H. Schumann, “Towards Multi-User Multi-Level Interaction,” Proc. Workshop Collaborative Visualization on Interactive Surfaces (Part of VisWeek '09), pp. 5-8, 2009.
[9] K.A. Marrs, S.A. Steib, C.A. Abrams, and M.G. Kahn, “Unifying Heterogeneous Distributed Clinical Data in a Relational Database,” Proc. Symp. Computer Applications in Medical Care, pp. 644-648, 1993.
[10] M.D. Lieberman, S. Taheri, H. Guo, F. Mir-Rashed, I. Yahav, A. Aris, and B. Shneiderman, “Visual Exploration across Biomedical Databases,” IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 8, no. 2, pp. 536-550, Mar./Apr. 2011.
[11] P.P.S. Chen, “The Entity-Relationship Model toward a Unified View of Data,” ACM Trans. Database Systems, vol. 1, no. 1, pp. 9-36, 1976.
[12] T. Munzner, “A Nested Process Model for Visualization Design and Validation,” IEEE Trans. Visualization and Computer Graphics, vol. 15, no. 6, pp. 921-928, Nov./Dec. 2009.
[13] C. Stary, “TADEUS: Seamless Development of Task-Based and User-Oriented Interfaces,” IEEE Trans. Systems, Man, and Cybernetics, Part A, vol. 30, no. 5, pp. 509-525, Sept. 2000.
[14] A. Puerta, “A Model-Based Interface Development Environment,” IEEE Software, vol. 14, no. 4, pp. 40-47, July/Aug. 1997.
[15] N. Gehlenborg, S.I. O'Donoghue, N.S. Baliga, A. Goesmann, M.A. Hibbs, H. Kitano, O. Kohlbacher, H. Neuweger, R. Schneider, D. Tenenbaum, and A. Gavin, “Visualization of Omics Data for Systems Biology,” Nature Methods, vol. 7, no. 3, pp. 56-68, 2010.
[16] M. Streit, A. Lex, M. Kalkusch, K. Zatloukal, and D. Schmalstieg, “Caleydo: Connecting Pathways and Gene Expression,” Bioinformatics, vol. 25, no. 20, pp. 2760-2761, 2009.
[17] A. Lex, M. Streit, E. Kruijff, and D. Schmalstieg, “Caleydo: Design and Evaluation of a Visual Analysis Framework for Gene Expression Data in Its Biological Context,” Proc. IEEE Symp. Pacific Visualization (PacificVis '10). pp. 57-64, 2010.
[18] Y.B. Shrinivasan and J.J. van Wijk, “Supporting the Analytical Reasoning Process in Information Visualization,” Proc. SIGCHI Conf. Human Factors in Computing Systems (CHI '08), pp. 1237-1246, 2008.
[19] M. Kreuseler, T. Nocke, and H. Schumann, “A History Mechanism for Visual Data Mining,” Proc. IEEE Symp. Information Visualization (InfoVis '04), pp. 49-56, 2004.
[20] J. Heer, J. Mackinlay, C. Stolte, and M. Agrawala, “Graphical Histories for Visualization: Supporting Analysis, Communication, and Evaluation,” IEEE Trans. Visualization and Computer Graphics, vol. 14, no. 6, pp. 1189-1196, Nov./Dec. 2008.
[21] C. Collins and S. Carpendale, “VisLink: Revealing Relationships amongst Visualizations,” IEEE Trans. Visualization and Computer Graphics, vol. 13, no. 6, pp. 1192-1199, Nov./Dec. 2007.
[22] K. Ma, “Image Graphs—A Novel Approach to Visual Data Exploration,” Proc. IEEE Conf. Visualization (Vis '99), pp. 81-88, 1999.
[23] T.J. Jankun-Kelly, K. Ma, and M. Gertz, “A Model and Framework for Visualization Exploration,” IEEE Trans. Visualization and Computer Graphics, vol. 13, no. 2, pp. 357-369, Mar./Apr. 2007.
[24] D.A. Keim, F. Mansmann, J. Schneidewind, and H. Ziegler, “Challenges in Visual Data Analysis,” Proc. Conf. Information Visualisation (IV '06), pp. 9-14, 2006.
[25] B. Shneiderman, “The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations,” Proc. IEEE Symp. Visual Languages (VL '96), pp. 336-343, 1996.
[26] L. Bavoil, S. Callahan, C. Scheidegger, H. Vo, P. Crossno, C. Silva, and J. Freire, “VisTrails: Enabling Interactive Multiple-View Visualizations,” Proc. IEEE Conf. Visualization (VIS '05), pp. 135-142, 2005.
[27] C. North and B. Shneiderman, “Snap-Together Visualization: A User Interface for Coordinating Visualizations via Relational Schemata,” Proc. ACM Conf. Advanced Visual Interfaces (AVI '00), pp. 128-135, 2000.
[28] M. Waldner, W. Puff, A. Lex, M. Streit, and D. Schmalstieg, “Visual Links across Applications,” Proc. Conf. Graphics Interface (GI '10), pp. 129-136, 2010.

Index Terms:
patient treatment,cancer,data visualisation,medical computing,data visualization,model-driven design process,analysis task workflow,data trajectory,interactive views,analytical process,biomedical use case,cancer patient treatment plan,heterogeneous clinical data pool visual analysis,Stacknflip,Data models,Analytical models,Visualization,Data visualization,Computational modeling,Concrete,Biological system modeling,multiple data sets.,Visual analytics,analysis guidance,model-driven design
Citation:
A. Lex, H. Schulz, M. Streit, D. Schmalstieg, H. Schumann, "Model-Driven Design for the Visual Analysis of Heterogeneous Data," IEEE Transactions on Visualization and Computer Graphics, vol. 18, no. 6, pp. 998-1010, June 2012, doi:10.1109/TVCG.2011.108
Usage of this product signifies your acceptance of the Terms of Use.