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Adapting User Interfaces by Analyzing Data Characteristics for Determining Adequate Visualizations

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 6776)

Abstract

Today the information visualization takes in an important position, because it is required in nearly every context where large databases have to be visualized. For this challenge new approaches are needed to allow the user an adequate access to these data. Static visualizations are only able to show the data without any support to the users, which is the reason for the accomplished researches to adaptive user-interfaces, in particular for adaptive visualizations. By these approaches the visualizations were adapted to the users’ behavior, so that graphical primitives were change to support a user e.g. by highlighting user-specific entities, which seems relevant for a user. This approach is commonly used, but it is limited on changes for just a single visualization. Modern heterogeneous data providing different kinds of aspects, which modern visualizations try to regard, but therefore a user often needs more than a single visualization for making an information retrieval. In this paper we describe a concept for adapting the user-interface by selecting visualizations in dependence to automatically generated data characteristics. So visualizations will be chosen, which are fitting well to the generated characteristics. Finally the user gets an aquatically arranged set of visualizations as initial point of his interaction through the data.

Keywords

Adaptive Visualizations Human-Centered Interfaces Human-Computer-Interfaces Semantics Visualization 

References

  1. 1.
    Card, S.K., Mackinlay, J.D., Shneiderman, B.: Readings in Information Visualization: Using Vision to Think. In: Readings in Information Visualization: Using Vision to Think, Morgan-Kaufmann, San Francisco (1999)Google Scholar
  2. 2.
    Chi, E.H.: A Taxonomy of Visualization Techniques using the Data State Reference Model. In: Proceedings of the IEEE Symposium on Information Visualization (2000)Google Scholar
  3. 3.
    Shneiderman, B.: The Eyes Have It: A Task by Data Type Taxonomy for Information Visualization. In: Proceedings Visual Languages (1996)Google Scholar
  4. 4.
    Golemati, M., Halatsis, C., Vasilakis, C., Katifori, A., Lepouras, G.: A Context-Based Adaptive Visualization Environment. In: Proceedings of the Information Visualization. IEEE Computer Society, Los Alamitos (2006)Google Scholar
  5. 5.
    Ahlberg, C., Wistrand, E.: IVEE: An Information Visualization & Exploration Environment. In: Proceedings of IEEE Viz (1999)Google Scholar
  6. 6.
    Katifori, A., Halatsis, C., Lepouras, G., Vassilakis, C., Giannopoulou, E.: Ontology Visualization Methods - A Survey. ACM Computing Surveys 39, Article 10 (2007)Google Scholar
  7. 7.
    von Landesberger, T., Kuijper, A., Schreck, T., Kohlhammer, J., Wijk, J.J., van Fekete, J.D., Fellner, D.W.: Visual Analysis of Large Graphs. In: Conference of EUROGRAPHICS (2010)Google Scholar
  8. 8.
    Barabasi, A.L., Bonabeau, E.: Scale-Free Networks. Journal of Scientific America (2003)Google Scholar
  9. 9.
    Yevtushenko, S.: Computing and Visualizing Concept Lattices. Thesis, TU Darmstadt, Darmstadt (2004)Google Scholar
  10. 10.
    Nazemi, K., Burkhardt, D., Breyer, M., Stab, C., Fellner, D.W.: Semantic Visualization Cockpit: Adaptable Composition of Semantics-Visualization Techniques for Knowledge-Exploration. In: International Conference Interactive Computer Aided Learning 2010 (ICL 2010), pp. 163–173. University Press, Kassel (2010)Google Scholar
  11. 11.
    Stab, C., Nazemi, K., Fellner, D.W.: SemaTime - Timeline Visualization of Time-Dependent Relations and Semantics. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Chung, R., Hammound, R., Hussain, M., Kar-Han, T., Crawfis, R., Thalmann, D., Kao, D., Avila, L. (eds.) ISVC 2010. LNCS, vol. 6455, pp. 514–523. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Stab, C., Breyer, M., Nazemi, K., Burkhardt, D., Hofmann, C., Fellner, D.W.: SemaSun: Visualization of Semantic Knowledge based on an improved Sunburst Visualization Metaphor. In: Proceedings of ED-Media 2010: World Conference on Educational Multimedia, Hypermedia & Telecommunications, Toronto (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Fraunhofer Institute for Computer Graphics ResearchDarmstadtGermany

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