A Review on Visualization Recommendation Strategies

Pawandeep Kaur, Michael Owonibi

2017

Abstract

Choosing the best visualization of a given dataset becomes more and more complex as not only the amount of data, but also the number of visualization types and the number of potential uses of visualizations grow tremendously. This challenge has spurred on the research into visualization recommendation systems. The ultimate aim of such a system is the suggestion of visualizations which provide interesting insights into the data. It should ideally consider data characteristics, domain knowledge and individual preferences to produce aesthetically appealing and easy to understand charts. Based on the mentioned factors, we have reviewed in this paper the state-of-the-art in visualization recommendation systems starting from the earliest attempt made on this subject. We identify challenges to visualization and visualization recommendation to guide future research directions.

References

  1. Bertin, J., 1983. Semiology of graphics: diagrams, networks, maps. University of Wisconsin Press.
  2. Bouali, F., Guettala, A. and Venturini, G., 2015. VizAssist: an interactive user assistant for visual data mining. The Visual Computer.(pp.1-17).
  3. Chen, C., 2005. Top 10 unsolved information visualization problems. IEEE computer graphics and applications, 25(4).(pp.12-16).
  4. Gilson, O., Silva, N., Grant, P.W. and Chen, M., 2008, May. From web data to visualization via ontology mapping. In Computer Graphics Forum, 27(3).(pp. 959-966). Blackwell Publishing Ltd.
  5. Gotz, D. and Wen, Z., 2009, February. Behavior-driven visualization recommendation. In Proceedings of the 14th international conference on Intelligent user interfaces (pp. 315-324). ACM.
  6. Hanrahan, P., 2006, June. Vizql: a language for query, analysis and visualization. In Proceedings of the 2006 ACM SIGMOD international conference on Management of data (pp. 721-721). ACM.
  7. Kerpedjiev, S., Carenini, G., Roth, S.F. and Moore, J.D., 1997. AutoBrief: a multimedia presentation system for assisting data analysis. Computer Standards & Interfaces, 18(6).(pp.583-593).
  8. Key, A., Howe, B., Perry, D. and Aragon, C., 2012, May. VizDeck: self-organizing dashboards for visual analytics. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data.(pp. 681-684). ACM.
  9. Klumpar, D.M, Anderson, K., and Simoudis, A. (1994). Rave: Rapid visualization environment. The 1994 Goddard Conference on Space Applications of Artificial Intelligence.(pp 29-38).
  10. Mackinlay, J., 1986. Automating the design of graphical presentations of relational information. ACM Transactions On Graphics (Tog), 5(2).(pp.110-141).
  11. Mackinlay, J., Hanrahan, P. and Stolte, C., 2007. Show me: Automatic presentation for visual analysis. IEEE Transactions on Visualization and Computer Graphics, 13(6).(pp.1137-1144).
  12. Munzner, T., 2009. A nested model for visualization design and validation. IEEE transactions on visualization and computer graphics, 15(6).(pp.921-928).
  13. Mutlu, B., Veas, E. and Trattner, C., 2016. VizRec: Recommending Personalized Visualizations. ACM Transactions on Interactive Intelligent Systems (TiiS), 6(4).(pp.31).
  14. Roth, S.F. and Mattis, J., 1990. Data characterization for intelligent graphics presentation. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. (pp. 193-200). ACM.
  15. Satyanarayan, A., Moritz, D., Wongsuphasawat, K. and Heer, J., 2017. Vega-lite: A grammar of interactive graphics. IEEE Transactions on Visualization & Computer Graphics, (1).(pp.341-350).
  16. Steichen, B., Carenini, G. and Conati, C., 2013, User-adaptive information visualization: using eye gaze data to infer visualization tasks and user cognitive abilities. In Proceedings of the 2013 international conference on Intelligent user interfaces (pp. 317-328). ACM.
  17. Shneiderman, B., 1996, September. The eyes have it: A task by data type taxonomy for information visualizations. In Visual Languages, 1996. Proceedings., IEEE Symposium (pp. 336-343). IEEE.
  18. Shneiderman, B., 1999. Dynamic queries, starfield displays, and the path to Spotfire.
  19. Stolte, C., Tang, D. and Hanrahan, P., 2002. Polaris: A system for query, analysis, and visualization of multidimensional relational databases. IEEE Transactions on Visualization and Computer Graphics, 8(1).(pp.52-65).
  20. Vartak, M., Huang, S., Siddiqui, T., Madden, S. and Parameswaran, A., 2015 Towards Visualization Recommendation Systems.Workshop on Data Systems.
  21. Viegas, F.B., Wattenberg, M., Van Ham, F., Kriss, J. and McKeon, M., 2007. Manyeyes: a site for visualization at internet scale. IEEE transactions on visualization and computer graphics, 13(6),(pp.1121-1128).
  22. Voigt, M., Pietschmann, S. and Meißner, K., 2012, February. Towards a semantics-based, end-user-centered information visualization process. In Proc. of the 3rd international workshop on semantic models for adaptive interactive systems (SEMAIS 2012).
  23. Wehrend, S. and Lewis, C., 1990,. A problem-oriented classification of visualization techniques. In Proceedings of the 1st Conference on Visualization'90. (pp. 139-143). IEEE Computer Society Press.
  24. Wongsuphasawat, K., Moritz, D., Anand, A., Mackinlay, J., Howe, B. and Heer, J., 2016. Voyager: Exploratory analysis via faceted browsing of visualization recommendations. IEEE transactions on visualization and computer graphics, 22(1), (pp.649-658).
  25. Zhou, M.X. and Feiner, S.K., 1998, January. Visual task characterization for automated visual discourse synthesis. In Proceedings of the SIGCHI conference on Human factors in computing systems. (pp. 392-399). ACM Press/Addison-Wesley Publishing Co.
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Paper Citation


in Harvard Style

Kaur P. and Owonibi M. (2017). A Review on Visualization Recommendation Strategies . 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 266-273. DOI: 10.5220/0006175002660273


in Bibtex Style

@conference{ivapp17,
author={Pawandeep Kaur and Michael Owonibi},
title={A Review on Visualization Recommendation Strategies},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017)},
year={2017},
pages={266-273},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006175002660273},
isbn={978-989-758-228-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017)
TI - A Review on Visualization Recommendation Strategies
SN - 978-989-758-228-8
AU - Kaur P.
AU - Owonibi M.
PY - 2017
SP - 266
EP - 273
DO - 10.5220/0006175002660273