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Data Exploration for Bisociative Knowledge Discovery: A Brief Overview of Tools and Evaluation Methods

  • Tatiana Gossen
  • Marcus Nitsche
  • Stefan Haun
  • Andreas Nürnberger
Open Access
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7250)

Abstract

In this chapter we explain the definition of the term (data) exploration. We refine this definition in the context of browsing, navigating and searching. We provide a definition of bisociative exploration and derive requirements on user interfaces, which are designed to support bisociative knowledge discovery. We discuss how to support subtasks of bisociative data exploration with appropriate user interface elements. We also present a set of exploratory tools, which are currently available or in development. Finally, we discuss the problem of usability evaluation in the context of exploratory search. Two main issues - complexity and comparability - are explained and possible solutions proposed.

Keywords

exploration exploratory search tools usability evaluation 
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References

  1. 1.
    ISO 9241-11: Ergonomic requirements for office work with visual display terminals (VDTs). Part 11 - guidelines for specifying and measuring usability. Geneva: International Standards Organisation. Also available from the British Standards Institute, London (1998)Google Scholar
  2. 2.
    Collins English Dictionary - Complete and Unabridged. HarperCollins Publishers (2003)Google Scholar
  3. 3.
    The American Heritage Dictionary of the English Language. Houghton Mifflin (2009)Google Scholar
  4. 4.
    Azzopardi, L., Järvelin, K., Kamps, J., Smucker, M.: Proc. of SIGIR 2010 Workshop on the Simulation of Interaction: Automated Evaluation of Interactive IR (SimInt 2010). ACM Press (2010)Google Scholar
  5. 5.
    Bates, M.J.: The design of browsing and berrypicking techniques for the online search interface. Online Review 13(5), 407–424 (1989)CrossRefGoogle Scholar
  6. 6.
    Berthold, M.R. (ed.): Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250. Springer, Heidelberg (2012)Google Scholar
  7. 7.
    Bevan, N.: Measuring usability as quality of use. Software Quality Journal 4(2), 115–130 (1995)CrossRefGoogle Scholar
  8. 8.
    Card, S., Mackinlay, J., Shneiderman, B.: Using visualization to think. Readings in Information Visualization (1999)Google Scholar
  9. 9.
    Dengel, A., Agne, S., Klein, B., Ebert, A., Deller, M.: Human-centered interaction with documents. In: Proceedings of the 1st ACM International Workshop on Human-centered Multimedia, HCM 2006, pp. 35–44. ACM, New York (2006)Google Scholar
  10. 10.
    Dubitzky, W., Kötter, T., Berthold, M.R.: Towards Creative Information Exploration Based on Koestler’s Concept of Bisociation. In: Bisociation, Part I. Springer (2011)Google Scholar
  11. 11.
    Eick, S.G., Wills, G.J.: Navigating large networks with hierarchies. Readings in Information Visualization Using Vision to Think, 207–214 (1999)Google Scholar
  12. 12.
    Görg, C., Stasko, J.: Jigsaw: investigative analysis on text document collections through visualization. In: DESI II: Second International Workshop on Supporting Search and Sensemaking for Electronically Stored Information in Discovery Proceedings. University College London, UK (2008)Google Scholar
  13. 13.
    Gossen, T., Haun, S., Nuernberger, A.: How to Evaluate Exploratory User Interfaces? In: Proceedings of the SIGIR 2011 Workshop on ”Entertain Me”: Supporting Complex Search Tasks, pp. 23–24. ACM Press (2011)Google Scholar
  14. 14.
    Han, J., Kamber, M.: Data mining: concepts and techniques. Morgan Kaufmann (2006)Google Scholar
  15. 15.
    Haun, S., Gossen, T., Nürnberger, A., Kötter, T., Thiel, K., Berthold, M.R.: On the Integration of Graph Exploration and Data Analysis: The Creative Exploration Toolkit. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 301–312. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  16. 16.
    Haun, S., Nürnberger, A., Kötter, T., Thiel, K., Berthold, M.R.: CET: A Tool for Creative Exploration of Graphs. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS, vol. 6323, pp. 587–590. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    Hearst, M.: Search user interfaces. Cambridge University Press (2009)Google Scholar
  18. 18.
    Heer, J.: Exploring Enron: Visualizing ANLP results (2004), http://hci.stanford.edu/jheer/projects/enron/v1/
  19. 19.
    Herczeg, M.: Interaktionsdesign. Oldenbourg Wissenschaftsverlag (2006)Google Scholar
  20. 20.
    Kang, Y., Goerg, C., Stasko, J.: How can visual analytics assist investigative analysis? design implications from an evaluation. IEEE Transactions on Visualization and Computer Graphics (2010)Google Scholar
  21. 21.
    Kreuseler, M., Nocke, T., Schumann, H.: A history mechanism for visual data mining. In: Proceedings of the IEEE Symposium on Information Visualization (Infovis 2004). IEEE Computer Society (2004)Google Scholar
  22. 22.
    Leinhardt, G., Leinhardt, S.: Exploratory data analysis: New tools for the analysis of empirical data. Review of Research in Education 8, 85–157 (1980)zbMATHGoogle Scholar
  23. 23.
    Lohmann, S., Heim, P., Tetzlaff, L., Ertl, T., Ziegler, J.: Exploring Relationships between Annotated Images with the ChainGraph Visualization. In: Chua, T.-S., Kompatsiaris, Y., Mérialdo, B., Haas, W., Thallinger, G., Bailer, W. (eds.) SAMT 2009. LNCS, vol. 5887, pp. 16–27. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  24. 24.
    Marchionini, G.: Exploratory search: From finding to understanding. Communications of the ACM - Supporting Exploratory Search 49(4) (April 2006)CrossRefGoogle Scholar
  25. 25.
    Miller, G.A.: The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Science (63), 81–97 (1956)CrossRefGoogle Scholar
  26. 26.
    Naisbitt, J.: Megatrends. Ten New Directions Transforming Our Lives. Warner Books (1982)Google Scholar
  27. 27.
    Nielsen, J.: Usability engineering. Morgan Kaufmann (1993)Google Scholar
  28. 28.
    Norman, D.: Visual representations. Things that Make us Smart: Defending Human Attributes in the Age of the Machine (1994)Google Scholar
  29. 29.
    Plaisant, C.: The challenge of information visualization evaluation. In: Proceedings of the Working Conference on Advanced Visual Interfaces, pp. 109–116. ACM (2004)Google Scholar
  30. 30.
    Redish, J.: Expanding usability testing to evaluate complex systems. Journal of Usability Studies 2(3), 102–111 (2007)Google Scholar
  31. 31.
    Shneiderman, B., Plaisant, C.: Strategies for evaluating information visualization tools: multi-dimensional in-depth long-term case studies. In: Proceedings of the 2006 AVI Workshop on BEyond Time and Errors: Novel Evaluation Methods for Information Visualization, pp. 1–7. ACM (2006)Google Scholar
  32. 32.
    Spence, R.: Information Visualization. Addison Wesley/ACM Press, Harlow (2001)Google Scholar
  33. 33.
    Stober, S., Nürnberger, A.: Automatic evaluation of user adaptive interfaces for information organization and exploration. In: SIGIR Works. on SimInt 2010, pp. 33–34 (July 2010)Google Scholar
  34. 34.
    Thomas, J.J., Cook, K.A.: Illuminating the path –The research and development agenda for Visual Analytics. IEEE Computer Society Press (2005)Google Scholar
  35. 35.
    Tominski, C.: Event-Based Visualization for User-Centered Visual Analysis. PhD thesis, Institute for Computer Science, Department of Computer Science and Electrical Engineering, University of Rostock (2006)Google Scholar
  36. 36.
    Tominski, C., Abello, J., Schumann, H.: Cgv—an interactive graph visualization system. Computers & Graphics 33(6), 660–678 (2009)CrossRefGoogle Scholar
  37. 37.
    Tukey, J.: Exploratory data analysis. Addison-Wesley, Massachusetts (1977)Google Scholar
  38. 38.
    Tukey, J.W.: Exploratory Data Analysis. Addison-Wesley (1977)Google Scholar
  39. 39.
    Wei, F., Liu, S., Song, Y., Pan, S., Zhou, M.X., Qian, W., Shi, L., Tan, L., Zhang, Q.: Tiara: A visual exploratory text analytic system. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2010, pp. 153–162. ACM, New York (2010)Google Scholar

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Authors and Affiliations

  • Tatiana Gossen
    • 1
  • Marcus Nitsche
    • 1
  • Stefan Haun
    • 1
  • Andreas Nürnberger
    • 1
  1. 1.Data and Knowledge Engineering Group, Faculty of Computer ScienceOtto-von-Guericke-UniversityGermany

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