The Community for Technology Leaders
RSS Icon
Issue No.12 - Dec. (2012 vol.18)
pp: 2729-2738
M. Sedlmair , Univ. of British Columbia, Vancouver, BC, Canada
A. Frank , Bertrand AG, Munich, Germany
T. Munzner , Univ. of British Columbia, Vancouver, BC, Canada
A. Butz , Univ. of Munich (LMU), Munich, Germany
We present a network visualization design study focused on supporting automotive engineers who need to specify and optimize traffic patterns for in-car communication networks. The task and data abstractions that we derived support actively making changes to an overlay network, where logical communication specifications must be mapped to an underlying physical network. These abstractions are very different from the dominant use case in visual network analysis, namely identifying clusters and central nodes, that stems from the domain of social network analysis. Our visualization tool RelEx was created and iteratively refined through a full user-centered design process that included a full problem characterization phase before tool design began, paper prototyping, iterative refinement in close collaboration with expert users for formative evaluation, deployment in the field with real analysts using their own data, usability testing with non-expert users, and summative evaluation at the end of the deployment. In the summative post-deployment study, which entailed domain experts using the tool over several weeks in their daily practice, we documented many examples where the use of RelEx simplified or sped up their work compared to previous practices.
user centred design, automotive electronics, data visualisation, electronic engineering computing, on-board communications, overlay networks, change management, overlay network specification, network visualization design, automotive engineer, traffic pattern, in-car communication network, data abstraction, logical communication specification, physical network, visual network analysis, social network analysis, visualization tool RelEx, user-centered design process, usability testing, summative evaluation, Data visualization, Change detection algorithms, Network topology, Automotive engineering, Collaboration, Traffic control, design study, Network visualization, change management, traffic routing, traffic optimization, automotive
M. Sedlmair, A. Frank, T. Munzner, A. Butz, "RelEx: Visualization for Actively Changing Overlay Network Specifications", IEEE Transactions on Visualization & Computer Graphics, vol.18, no. 12, pp. 2729-2738, Dec. 2012, doi:10.1109/TVCG.2012.255
[1] D. L. Alderson and J. C. Doyle., Catching the “network science” bug: In-sight and opportunity for the operations researcher Operations Research, 56(5): 1047-1065, 2008.
[2] A. Barsky, T. Munzner, J. Gardy,, and R. Kincaid., Cerebral: visualizing multiple experimental conditions on a graph with biological context. IEEE Trans. Visualization and Computer Graphics (Proc. InfoVis 2008), 14(6): 1253-60, 2008.
[3] S. Borgatti, Centrality and network flow Social Networks, 27(1): 55-71, 2005.
[4] R. Bosch, GmbH. Autoelektrik/Autoelektronik, 5. Auflage. Vieweg Verlag, Braunschweig, Germany, 2007.
[5] R. Bosch, GmbH. Kraftfahrzeugtechnisches Taschenbuch. 26. Auflage. Vieweg Verlag, Braunschweig, Germany, 2007.
[6] B. Brown, S. Reeves, and S. Sherwood., Into the wild: Challenges and opportunities for field trial methods. In Proc. ACM Conf. Human Factors in Computing Systems (CHI), pages 1657-1666. ACM, 2011.
[7] M. Broy., Challenges in automotive software engineering. In Proc. ACM Intl. Conf. Software Engineering (ICSE), pages 33-42, 2006.
[8] S. Card, J. Mackinlay, and B. Shneiderman., Readings in information visualization: using vision to think. Morgan Kaufmann, 1999.
[9] A. Dix and G. Ellis., Starting simple - adding value to static visualisation through simple interaction. In Proc. Advanced Visual Interfaces (AVI), pages 124-134, 1998.
[10] S. P. Dow, A. Glassco, J. Kass., M. Schwarz, D. L. Schwartz,, and S. R. Klemmer., Parallel prototyping leads to better design results, more divergence, and increased self-efficacy ACM Trans. Computer-Human Interaction (To CHI), 17(4): 1-24, 2010.
[11] Eclipse Foundation. Eclipse. http:/, last visited:03/ 12.
[12] F. Fischer, F. Mansmann, D. Keirn,, and S. Pietzko., Large-scale network monitoring for visual analysis of attacks. In Proc. Intl. Workshop Visualization for Computer Security (VizSec), pages 111-118, 2008.
[13] M. Ghoniem, J. Fekete, and P. Castagliola, On the readability of graphs using node-link and matrix-based representations: a controlled experiment and statistical analysis Information Visualization, 4(2): 114, 2005.
[14] K. Grimm., Software technology in an automotive company: major challenges. In Proc. Intl. Conf. Software Engineering (ICSE), pages 498-503, Los Alamitos, CA, USA, 2003. IEEE Computer Society.
[15] G. Hayes, The relationship of action research to human-computer interaction ACM Trans. Computer-Human Interaction (To CHI), 18(3): 15, 2011.
[16] C. Healey, K. Booth, and J. Enns., Harnessing Preattentive Processes for Multivariate Data Visualization. In Proc. Graphics Interface (GI), pages 107-117, 1993.
[17] J. Heer and d. boyd., Vizster: Visualizing online social networks. In Proc. IEEE Symp. Information Visualization (Info Vis), pages 32-39, 2005.
[18] H. Heinecke., Automotive System Design – Challenges and Potential. In Proc. IEEE Conf. Design, Automation and Test in Europe (DATE), pages 656-657, 2005.
[19] N. Henry, A. Bezerianos, and J. Fekete, Improving the Readability of Clustered Social Networks using Node Duplication IEEE Trans. Visualization and Computer Graphics (Proc. InfoVis2008), 14(6): 1317-1324, 2008.
[20] N. Henry and J. Fekete, MatrixExplorer: a dual-representation system to explore social networks IEEE Trans. Visualization and Computer Graphics (Proc. InfoVis 2006), 12(5): 677-684, 2006.
[21] N. Henry, J. Fekete, and M. McGuffin, NodeTrix: a hybrid visualization of social networks IEEE Trans. Visualization and Computer Graphics (Proc. InfoVis 2007), 13(6): 1302-1309, 2007.
[22] K. Holtzblatt and S. Jones., Contextual inquiry: A participatory technique for system design. In D. Schuler,A. Namioka, (eds.): Participatory Design: Principles and Practices, pages 177-210. Lawrence Erlbaum Associates, 1993.
[23] M. J., Intons-Peterson. Imagery paradigms: How vulnerable are they to experimenters’ expectations? Journ. Experimental Psychology - Human Perception and Performance, 9: 394-412, 1983.
[24] H. Kang, C. Plaisant, B. Lee,, and B. Bederson., NetLens: iterative exploration of content-actor network data. Information Visualization, 6(1): 1831, 2007.
[25] B. Lee, C. Plaisant, C. Parr., J. Fekete, and N. Henry., Task taxonomy for graph visualization. In Proc. AVI Workshop on BEyond time and errors: novel evaLuation methods for Information Visualization (BELIV). ACM, 2006. Article 14.
[26] G. Leen, D. Heffernan, and A. Dunne, Digital networks in the automotive vehicle Computing & Control Engineering Journ., 10(6): 257-266, 1999.
[27] T. Munzner., Process and pitfalls in writing information visualization research papers. In Information Visualization: Human-Centered Issues and Perspectives, pages 134-153. Springer LNCS 4950, 2008.
[28] T. Munzner, A nested model for visualization design and validation IEEE Trans. Visualization and Computer Graphics (Proc. Info Vis 2009), 15(6): 921-928, 2009.
[29] T. Munzner,F. Guimbretiére,, and G. Robertson., Constellation: a visualization tool for linguistic queries from MindNet. In Proc. IEEE Symp. Information Visualization (Info Vis), pages 132-135, 1999.
[30] G. Namata, B. Staats, L. Getoor,, and B. Shneiderman., A dual-view approach to interactive network visualization. In Proc. ACM Conf. Information and Knowledge Management (CIKM), pages 939-942, 2007.
[31] C. B. Nielsen,S. D. Jackman, I. Birol, and S. J. M. Jones., ABySS-Explorer: visualizing genome sequence assemblies IEEE Trans. Visualization and Computer Graphics (Proc. InfoVis 2009), 15(6): 881-8, 2009.
[32] D. A, Norman The Design of Everyday Things. Doubleday, 1988.
[33] C. North and B. Shneiderman., Snap-together visualization: a user interface for coordinating visualizations via relational schemata. In Proc. ACM Conf. Advanced Visual Interfaces (AVI), pages 128-135, 2000.
[34] A. Perer, I. Guy, E. Uziel., I. Ronen, and M. Jacovi., Visual social network analytics for relationship discovery in the enterprise. In Proc. IEEE Conf. Visual Analytics Science and Technology (VAST), pages 71-79, 2011.
[35] A. Perer and B. Shneiderman, Integrating Statistics and Visualization: Case Studies of Gaining Clarity during Exploratory Data Analysis Proc. ACM Conf. Human Factors in Computing Systems (CHI), pages 265-274, 2008.
[36] A. Pretorius and J. Van Wijk., Visual inspection of multivariate graphs. Computer Graphics Forum (Proc. Eurovis 2008), 27(3): 967-974, 2008.
[37] A. Pretschner, M. Broy, I. Kruger,, and T. Stauner., Software engineering for automotive systems: A roadmap. In Proc. ICSE Workshop on Future of Software Engineering (FOSE‘07), pages 55-71. IEEE Computer Society, 2007.
[38] M. Sedlmair., Visual Analysis of In-Car Communication Networks. PhD thesis, Faculty of Mathematics, Computer Science and Statistics, University of Munich, 2010.
[39] M. Sedlmair, P. Isenberg, D. Baur,, and A. Butz., Information Visualization Evaluation in Large Companies: Challenges, Experiences and Recommendations. Information Visualization, 10(3): 248-266, 2011.
[40] M. Sedlmair, P. Isenberg, D. Baur., M. Mauerer, C. Pigorsch,, and A. Butz., Cardiogram: Visual analytics for automotive engineers. In Proc. ACM Conf. Human Factors in Computing Systems (CHI), 2011.
[41] M. Sedlmair, M. Meyer, and T. Munzner, Design Study Methodology: Reflections from the Trenches and the Stacks IEEE Trans. Visualization and Computer Graphics (Proc. InfoVis 2012), 2012. In press.
[42] B. Shneiderman and C. Plaisant., Strategies for evaluating information visualization tools: multi-dimensional in-depth long-term case studies. In Proc. AVI Workshop on BEyond Time and Errors: Novel Evaluation Methods for Information Visualization (BELIV), 2006.
[43] A. Treisman, Preattentive processing in vision Computer Vision, Graphics, and Image Processing, 31 (2): 156-177, 1985.
[44] F. van, Ham. Using multilevel call matrices in large software projects. In Proc. IEEE Symp. Information Visualization (Info Vis), pages 227-232, 2003.
[45] F. van Ham, H. Schulz, and J. Dimicco., Honeycomb: Visual Analysis of Large Scale Social Networks. In Proc. Intl. Conf. Human-Computer Interaction (INTERACT), pages 420-442. Springer-Verlag, 2009.
[46] T. von Landesberger, A. Kuijper, T. Schreck., J. Kohlhammer, J. J. van Wijk, J.-D. Fekete, and D. W. Fellner., Visual Analysis of Large Graphs Proc. Eurographics, 2010.
[47] C. Ware., Information Visualization: Perception for Design. Morgan Kaufmann, 2004.
[48] C. Weaver., Building highly-coordinated visualizations in Improvise. In Proc. IEEE Symp. Information Visualization (Info Vis), pages 159-166, 2004.
[49] W. Willinger, D. Alderson, and J. C. Doyle., Mathematics and the Internet: A source of enormous confusion and great potential Notices of the American Mathemematical Society, 56(5): 586-599, 2009.
23 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool