Reducing Visual Complexity in Software Maps using Importance-based Aggregation of Nodes

Daniel Limberger, Willy Scheibel, Sebastian Hahn, Jürgen Döllner


Depicting massive software system data using treemaps can result in visual clutter and increased cognitive load. This paper introduces an adaptive level-of-detail (LoD) technique that uses scoring for interactive aggregation on a per-node basis. The scoring approximates importance by degree-of-interest measures as well as screen and user-interaction scores. The technique adheres to established aggregation guidelines and was evaluated by means of two user studies. The first investigates task completion time in visual search. The second evaluates the readability of the presented nesting level contouring for aggregates. With the adaptive LoD technique software maps allow for multi-resolution depictions of software system information while facilitating annotation and efficient identification of important nodes.


  1. Balzer, M. and Deussen, O. (2007). Level-of-detail visualization of clustered graph layouts. In Proc. IEEE APVIS, pages 133-140.
  2. Bederson, B. B., Shneiderman, B., and Wattenberg, M. (2002). Ordered and quantum treemaps: Making effective use of 2D space to display hierarchies. ACM Trans. Graph., 21(4):833-854.
  3. Bertin, J. (1967). Sémiologie graphique. Mouton.
  4. Bladh, T., Carr, D. A., and Scholl, J. (2004). Extending tree-maps to three dimensions: A comparative study. In Proc. APCHI, pages 50-59.
  5. Blanch, R. and Lecolinet, E. (2007). Browsing zoomable treemaps: Structure-aware multi-scale navigation techniques. IEEE Trans. Vis. Comput. Graph., 13(6):1248-1253.
  6. Bohnet, J. and Döllner, J. (2011). Monitoring code quality and development activity by software maps. In Proc. ACM MTD, pages 9-16.
  7. Breunig, M. M., Kriegel, H.-P., Ng, R. T., and Sander, J. (2000). LOF: Identifying density-based local outliers. In Proc. ACM SIGMOD, pages 93-104.
  8. Bruls, M., Huizing, K., and van Wijk, J. (1999). Squarified treemaps. In Proc. Eurographics/IEEE TCVG Symposium on Visualization, pages 33-42.
  9. Chuah, M. C. (1998). Dynamic aggregation with circular visual designs. In Proc. IEEE InfoVis, pages 35-43.
  10. Cui, Q., Ward, M., Rundensteiner, E., and Yang, J. (2006). Measuring data abstraction quality in multiresolution visualizations. IEEE Trans. Vis. Comput. Graph., 12(5):709-716.
  11. Ellis, G. and Dix, A. (2007). A taxonomy of clutter reduction for information visualisation. IEEE Trans. Vis. Comput. Graph., 13(6):1216-1223.
  12. Elmqvist, N. and Fekete, J.-D. (2010). Hierarchical aggregation for information visualization: Overview, techniques, and design guidelines. IEEE Trans. Vis. Comput. Graph., 16(3):439-454.
  13. Fekete, J.-D. and Plaisant, C. (2002). Interactive information visualization of a million items. In Proc. IEEE IV, pages 117-124.
  14. Furnas, G. W. (1986). Generalized fisheye views. InProc. ACM CHI, pages 16-23.
  15. Hagh-Shenas, H., Interrante, V., Healey, C., and Kim, S. (2006). Weaving versus blending: A quantitative assessment of the information carrying capacities of two alternative methods for conveying multivariate data with color. In Proc. ACM APGV, pages 164-164.
  16. Hao, M., Dayal, U., Keim, D., and Schreck, T. (2007). Multi-resolution techniques for visual exploration of large time-series data. In Proc. EG/VGTC EA, pages 27-34.
  17. Harper, S., Michailidou, E., and Stevens, R. (2009). Toward a definition of visual complexity as an implicit measure of cognitive load. ACM Trans. Appl. Percept., 6(2):10:1-10:18.
  18. Johnson, B. and Shneiderman, B. (1991). Treemaps: A space-filling approach to the visualization of hierarchical information structures. In Proc. IEEE VIS, pages 284-291.
  19. Johnson, B. S. (1993). Treemaps: Visualizing hierarchical and categorical data. PhD thesis, University of Maryland. HCIL-94-04, UMI-94-25057.
  20. Liu, S., Cao, N., and Lv, H. (2008). Interactive visual analysis of the nsf funding information. In Proc. IEEE PacificVis, pages 183-190.
  21. Lü, H. and Fogarty, J. (2008). Cascaded treemaps: Examining the visibility and stability of structure in treemaps. In Proceedings of Graphics Interface 2008, GI 7808, pages 259-266, Toronto, Ont., Canada, Canada. Canadian Information Processing Society.
  22. McCabe, T. J. (1976). A complexity measure. In Proc. IEEE ICSE, pages 407-.
  23. Misue, K., Eades, P., Lai, W., and Sugiyama, K. (1995). Layout adjustment and the mental map. Journal of Visual Languages & Computing, 6(2):183-210.
  24. Mitchell, W., Shook, D., and Shah, S. L. (2004). A picture worth a thousand control loops: An innovative way of visualizing controller performance data. In Invited Plenary Presentation, Control Systems.
  25. Munzner, T., Guimbretière, F., Tasiran, S., Zhang, L., and Zhou, Y. (2003). TreeJuxtaposer: Scalable tree comparison using focus+context with guaranteed visibility. ACM Trans. Graph., 22(3):453-462.
  26. Rosenbaum, R. and Hamann, B. (2009). Progressive presentation of large hierarchies using treemaps. In Proc. ISVC, pages 71-80.
  27. Rosenholtz, R., Li, Y., Mansfield, J., and Jin, Z. (2005). Feature congestion: A measure of display clutter. In Proc. ACM CHI, pages 761-770.
  28. Schulz, H.-J., Hadlak, S., and Schumann, H. (2011). The design space of implicit hierarchy visualization: A survey. IEEE Trans. Vis. Comput. Graph., 17(4):393- 411.
  29. Shneiderman, B. (1992). Tree visualization with treemaps: A 2D space-filling approach. ACM Trans. Graph., 11(1):92-99.
  30. Shneiderman, B. (1996). The eyes have it: a task by data type taxonomy for information visualizations. In Proc. IEEE Symposium on Visual Languages, pages 336-343.
  31. Tak, S. and Cockburn, A. (2013). Enhanced spatial stability with hilbert and moore treemaps. IEEE Trans. Vis. Comput. Graph., 19(1):141-148.
  32. Trapp, M., Glander, T., Buchholz, H., and D öllner, J. (2008). 3D generalization lenses for interactive focus + context visualization of virtual city models. In Proc. IEEE IV, pages 356-361.
  33. Vliegen, R., van Wijk, J. J., and van der Linden, E.- J. (2006). Visualizing business data with generalized treemaps. IEEE Trans. Vis. Comput. Graph., 12(5):789-796.
  34. Wattenberg, M. (1999). Visualizing the stock market. In Proc. ACM CHI EA, pages 188-189.
  35. Wettel, R. and Lanza, M. (2008). CodeCity: 3d visualization of large-scale software. In Proc. ACM ICSE Companion, pages 921-922.

Paper Citation

in Harvard Style

Limberger D., Scheibel W., Hahn S. and Döllner J. (2017). Reducing Visual Complexity in Software Maps using Importance-based Aggregation of Nodes . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017) ISBN 978-989-758-228-8, pages 176-185. DOI: 10.5220/0006267501760185

in Bibtex Style

author={Daniel Limberger and Willy Scheibel and Sebastian Hahn and Jürgen Döllner},
title={Reducing Visual Complexity in Software Maps using Importance-based Aggregation of Nodes},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017)},

in EndNote Style

JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017)
TI - Reducing Visual Complexity in Software Maps using Importance-based Aggregation of Nodes
SN - 978-989-758-228-8
AU - Limberger D.
AU - Scheibel W.
AU - Hahn S.
AU - Döllner J.
PY - 2017
SP - 176
EP - 185
DO - 10.5220/0006267501760185