Advertisement

Adaptive Semantic Visualization for Bibliographic Entries

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8034)

Abstract

Adaptive visualizations aim to reduce the complexity of visual representations and convey information using interactive visualizations. Although the research on adaptive visualizations grew in the last years, the existing approaches do not make use of the variety of adaptable visual variables. Further the existing approaches often premises experts, who has to model the initial visualization design. In addition, current approaches either incorporate user behavior or data types. A combination of both is not proposed to our knowledge. This paper introduces the instantiation of our previously proposed model that combines both: involving different influencing factors for and adapting various levels of visual peculiarities, on visual layout and visual presentation in a multiple visualization environment. Based on data type and users’ behavior, our system adapts a set of applicable visualization types. Moreover, retinal variables of each visualization type are adapted to meet individual or canonic requirements on both, data types and users’ behavior. Our system does not require an initial expert modeling.

Keywords

Digital Library User Model Visual Presentation Visual Variable Visual Adaptation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ahn, J.W., Brusilovsky, P.: Adaptive visualization of search results: bringing user models to visual analytics. Information Visualization, 167–179 (2009)Google Scholar
  2. 2.
    Ahn, J.W.: Adaptive Visualization for Focused Personalized Information Retrieval. PhD thesis, School of Information Sciences, University of Pittsburgh (2010)Google Scholar
  3. 3.
    Toker, D., Conati, C., Carenini, G., Haraty, M.: Towards adaptive information visualization: On the influence of user characteristics. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds.) UMAP 2012. LNCS, vol. 7379, pp. 274–285. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Steichen, B., Carenini, G., Conati, C.: User-adaptive information visualization: using eye gaze data to infer visualization tasks and user cognitive abilities. In: Proc. of IUI, IUI 2013, pp. 317–328. ACM, New York (2013)Google Scholar
  5. 5.
    Mackinlay, J.: Automating the design of graphical presentations of relational information. ACM Trans. Graph. 5, 110–141 (1986)CrossRefGoogle Scholar
  6. 6.
    Mackinlay, J., Hanrahan, P., Stolte, C.: Show Me: Automatic Presentation for Visual Analysis. IEEE Transactions on Visualization and Computer Graphics, 1137–1144 (2007)Google Scholar
  7. 7.
    Gotz, D., et al.: HARVEST: an intelligent visual analytic tool for the masses. In: Proceedings of the First International Workshop on Intelligent Visual Interfaces for Text Analysis, IVITA 2010, pp. 1–4. ACM, New York (2010)CrossRefGoogle Scholar
  8. 8.
    Zhou, M.X., Feiner, S.K.: Visual task characterization for automated visual discourse synthesis. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 1998, pp. 392–399. ACM Press/Addison-Wesley Publishing Co., New York (1998)Google Scholar
  9. 9.
    Yi, J.S., Ah Kang, Y., Stasko, J., Jacko, J.: Toward a deeper understanding of the role of interaction in information visualization. IEEE Transactions on Visualization and Computer Graphics 13, 1224–1231 (2007)CrossRefGoogle Scholar
  10. 10.
    Pike, W.A., Stasko, J., Chang, R., O’Connell, T.A.: The science of interaction. Information Visualization 8, 263–274 (2009)CrossRefGoogle Scholar
  11. 11.
    Ware, C.: Information Visualization Perception for Design. Morgan Kaufmann (Elsevier) (2013)Google Scholar
  12. 12.
    Rensink, R.A.: Change detection. Annual Review of Psychology, 245–277 (2002)Google Scholar
  13. 13.
    Nazemi, K., Stab, C., Kuijper, A.: A reference model for adaptive visualization systems. In: Jacko, J.A. (ed.) Human-Computer Interaction, Part I, HCII 2011. LNCS, vol. 6761, pp. 480–489. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  14. 14.
    Nazemi, K., Kohlhammer, J.: Visual variables in adaptive visualizations. In: 1st International Workshop on User-Adaptive Visualization, WUAV 2013. Extended Proceedings of UMAP 2013. CEUR Workshop Proceedings, vol. 997, pp. 1613–1673 (2013) ISSN 1613-0073Google Scholar
  15. 15.
    Treisman, A.M., Gelade, G.: A feature-integration theory of attention. Cognitive Psychology 12(1), 97–136 (1980)CrossRefGoogle Scholar
  16. 16.
    Golemati, M., Halatsis, C., Vassilakis, C., Katifori, A., Peloponnese, U.O.: A Context-Based Adaptive Visualization Environment. In: Proceedings of the Conference on Information Visualization, IV 2006, pp. 62–67. IEEE Computer Society, Washington, DC (2006)Google Scholar
  17. 17.
    Shi, L., Cao, N., Liu, S., Qian, W., Tan, L., Wang, G., Sun, J., Lin, C.Y.: Himap: Adaptive visualization of large-scale online social networks. In: Proceedings of the 2009 IEEE Pacific Visualization Symposium, PACIFICVIS 2009, pp. 41–48. IEEE Computer Society, Washington, DC (2009)CrossRefGoogle Scholar
  18. 18.
    da Silva, I., Santucci, G., del Sasso Freitas, C.: Ontology Visualization: One Size Does Not Fit All. In: Matkovic, K., Santucci, G. (eds.) Proceedings of EuroVA 2012: International Workshop on Visual Analytics, Eurographics Association, pp. 91–95, Vienna (2012)Google Scholar
  19. 19.
    Shen, Z., Ogawa, M., Teoh, S.T., Ma, K.L.: Biblioviz: a system for visualizing bibliography information. In: Proceedings of the 2006 Asia-Pacific Symposium on Information Visualisation, APVis 2006, vol. 60, pp. 93–102. Australian Computer Society, Inc., Darlinghurst (2006)Google Scholar
  20. 20.
    Matthews, T.: Citation Map Visualizing Citation Data in the Web of Science. Thomson Reuters (2010)Google Scholar
  21. 21.
    Tsatsaronis, G., Varlamis, I., Torge, S., Reimann, M., Nørvåg, K., Schroeder, M., Zschunke, M.: How to become a group leader? or modeling author types based on graph mining. In: Gradmann, S., Borri, F., Meghini, C., Schuldt, H. (eds.) TPDL 2011. LNCS, vol. 6966, pp. 15–26. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  22. 22.
    Chou, J.K., Yang, C.K.: PaperVis: Literature Review Made Easy. Computer Graphics Forum 30, 721–730 (2011)CrossRefGoogle Scholar
  23. 23.
    Bergstrom, P., Atkinson, D.: Augmenting the exploration of digital libraries with web-based visualizations. In: Fourth International Conference on Digital Information Management, ICDIM 2009, pp. 1–7 (2009)Google Scholar
  24. 24.
    Nazemi, K., Breyer, M., Burkhardt, D., Fellner, D.W.: Visualization Cockpit: Orchestration of Multiple Visualizations for Knowledge-Exploration. International Journal of Advanced Corporate Learning 3, 26–34 (2010)Google Scholar
  25. 25.
    Nazemi, K., Breyer, M., Hornung, C.: SeMap: A Concept for the Visualization of Semantics as Maps. In: Stephanidis, C. (ed.) UAHCI 2009, Part III. LNCS, vol. 5616, pp. 83–91. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  26. 26.
    Nazemi, K., Stab, C., Fellner, D.W.: Interaction Analysis for Adaptive User Interfaces. In: Huang, D.-S., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds.) ICIC 2010. LNCS, vol. 6215, pp. 362–371. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  27. 27.
    Seeberg, C.: Life Long Learning; Modulare Wissensbasen für elktronische Lernumgebungen (Modular Knowledge-bases for Electronical Learning Environments). Springer, Heidelberg (2003)Google Scholar
  28. 28.
    Ullrich, D., Diefenbach, S.: Intui. exploring the facets of intuitive interaction. In: Mensch & Computer 2010: 10. Fachübergreifende Konferenz für Interaktive und Kooperative Medien. Interaktive Kulturen, p. 251. Oldenbourg Wissenschaftsverlag (2010)Google Scholar
  29. 29.
    Ullrich, D., Diefenbach, S.: From magical experience to effortlessness: an exploration of the components of intuitive interaction. In: Proceedings of the 6th Nordic Conference on Human-Computer Interaction: Extending Boundaries, pp. 801–804. ACM (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Fraunhofer Insititute for Computer Graphics Research (IGD)DarmstadtGermany

Personalised recommendations