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Knowde: A Visual Search Interface

  • Maurice SchleußingerEmail author
  • Maria Henkel
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 850)

Abstract

Information Visualizations are well established to represent high density information in an intuitive and interactive way. There are no popular general retrieval systems, however, which utilize the power of information visualizations for search result representation. This paper describes Knowde, a search interface with purely visual result representation. It employs a powerful information retrieval system and works in a common web browser in real-time. This working prototype, with three different variations of network graphs will assist us in exploring current issues in visualization research, such as the challenge of system evaluation.

Keywords

Visualization Information retrieval Information system Visual search interface 

References

  1. 1.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley Longman Publishing Co, Boston (1999)Google Scholar
  2. 2.
    Belkin, N.J., Croft, W.B.: Retrieval techniques. In: Williams, M.E. (ed.) Annual Review of Information Science and Technology, vol. 22, pp. 109–145. Elsevier Science Inc., New York (1987)Google Scholar
  3. 3.
    Bostock, M., Ogievetsky, V., Heer, J.: D3 data-driven documents. IEEE Trans. Vis. Comput. Graph. 17, 2301–2309 (2011)CrossRefGoogle Scholar
  4. 4.
    Clarkson, E.C., Desai, K., Foley, J.D.: ResultMaps: visualization for search interfaces. IEEE Trans. Vis. Comput. Graph. 15, 1057–1064 (2009)CrossRefGoogle Scholar
  5. 5.
    Cugini, J., Laskowski, S., Sebrechts, M.: Design of 3D visualization of search results: evolution and evaluation. In: 12th Annual International Symposium: Electronic Imaging 2000: Visual Data Exploration and Analysis, vol. 3960, pp. 198–210 (2000)Google Scholar
  6. 6.
    Ellis, G., Dix, A.: An explorative analysis of user evaluation studies in information visualisation. In: Proceedings of the 2006 AVI Workshop on BEyond Time and Errors: Novel Evaluation Methods for Information Visualization - BELIV 2006, p. 1 (2006)Google Scholar
  7. 7.
    Gormley, C., Tong, Z.: Elasticsearch the Definite Guide. O’Reilly Media, Sebastopol (2010)Google Scholar
  8. 8.
    Grierson, H.J., Corney, J.R., Hatcher, G.D.: Using visual representations for the searching and browsing of large, complex, multimedia data sets. Int. J. Inf. Manag. 35, 244–252 (2015)CrossRefGoogle Scholar
  9. 9.
    Hearst, M.: The design of search user interfaces. In: Search User Interfaces (2009)Google Scholar
  10. 10.
    Heer, J., Boyd, D.: Vizster: visualizing online social networks. In: Proceedings - IEEE Symposium on Information Visualization, INFO VIS, pp. 33–40 (2005)Google Scholar
  11. 11.
    Heer, J., van Ham, F., Carpendale, S., Weaver, C., Isenberg, P.: Creation and collaboration: engaging new audiences for information visualization. In: Kerren, A., Stasko, J.T., Fekete, J.-D., North, C. (eds.) Information Visualization. LNCS, vol. 4950, pp. 92–133. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-70956-5_5CrossRefGoogle Scholar
  12. 12.
    Keim, D.A.: Information visualization and visual data mining. IEEE Trans. Vis. Comput. Graph. 8, 1–8 (2002)CrossRefGoogle Scholar
  13. 13.
    Klouche, K., Ruotsalo, T., Micallef, L., Andolina, S., Jacucci, G.: Visual re-ranking for multi-aspect information retrieval. In: Proceedings of CHIIR 2017. pp. 57–66 (2017)Google Scholar
  14. 14.
    Kraker, P., Kittel, C., Enkhbayar, A.: Open knowledge maps: creating a visual interface to the world’s scientific knowledge based on natural language processing. 027.7. J Libr. Cult. 4, 98–103 (2016)Google Scholar
  15. 15.
    Nguyen, T.N., Zhang, J.: A novel visualization model for web search results. IEEE Trans. Vis. Comput. Graph. 12, 981–988 (2006)CrossRefGoogle Scholar
  16. 16.
    Nielsen, J.: Usability Engineering. Morgan Kaufmann Publishers Inc., San Francisco (1993)CrossRefGoogle Scholar
  17. 17.
    Rohrer, R.M., Swing, E.: Web-based information visualization. IEEE Comput. Graph. Appl. 17, 52–59 (1997)CrossRefGoogle Scholar
  18. 18.
    Schumann, L., Stock, W.G.: The Information Service Evaluation (ISE) model. Webology 11(1) (2014). Article no. 115Google Scholar
  19. 19.
    Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: The Craft of Information Visualization, pp. 364–371 (2003)CrossRefGoogle Scholar
  20. 20.
    Shneiderman, B., Byrd, D., Croft, W.B.: Clarifying search: a user-interface framework for text searches. D-Lib Mag. 3(1), 18–20 (1997)Google Scholar
  21. 21.
    Smith, G., Czerwinski, M., Meyers, B., Robbins, D., Robertson, G., Tan, D.S.: FacetMap: a scalable search and browse visualization. IEEE Trans. Vis. Comput. Graph. 12, 797–804 (2006)CrossRefGoogle Scholar
  22. 22.
    Stock, W.G., Stock, M.: Handbook of Information Science. De Gruyter Saur, Berlin (2013)CrossRefGoogle Scholar
  23. 23.
    van Ham, F., Perer, A.: Search, show context, expand on demand: supporting large graph exploration with degree-of-interest. IEEE Trans. Vis. Comput. Graph. 15, 953–960 (2009)CrossRefGoogle Scholar
  24. 24.
    Ware, C.: Information Vizualization: Perception for Design. Elsevier, Amsterdam (2004)Google Scholar
  25. 25.
    Yi, J.S., ah Kang, Y., Stasko, J.T., Jacko, J.A.: Toward a deeper understanding of the role of interaction in information visualization. IEEE Trans. Vis. Comput. Graph. 13, 1224–1231 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Center of Information and Media TechnologyHeinrich Heine UniversityDüsseldorfGermany
  2. 2.Heinrich Heine UniversityDüsseldorfGermany

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