Information Exploration in Search Computing

  • Alessandro Bozzon
  • Marco Brambilla
  • Stefano Ceri
  • Piero Fraternali
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6585)


Search computing queries typically address search tasks that go beyond a single interaction. In this paper, we show a query paradigm that supports multi-step, exploratory search over multiple Web data sources. Our paradigm requires users to be aware of searching over “interconnected objects” with given semantics, but each exploration step is simplified as much as possible, by presenting to users at each step simple interfaces, offering some choices that can be supported by the system; choices include moving “forward”, by adding new objects to the search, or “backward”, by excluding some objects from the search; and the selection and de-selection of displayed results in order to dynamically manipulate the result set. For supporting exploration, we designed a new architectural element, called query orchestrator, which connects the user interface module with the execution engine; the orchestrator maintains the history of the query session and caches query results for reuse at subsequent interactions.


Query Expansion Query Execution Search Computing Object Instance Information Exploration 
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.


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  1. 1.
    Adar, E.: Guess: a language and interface for graph exploration. In: Proceedings of the SIGCHI conference on Human Factors in computing systems (CHI 2006), pp. 791–800. ACM, New York (2006)CrossRefGoogle Scholar
  2. 2.
    Aula, A., Russell, D.M.: Complex and Exploratory Web Search. In: Information Seeking Support Systems Workshop (ISSS 2008), Chapel Hill, NC, USA, June 26-27 (2008)Google Scholar
  3. 3.
    Bates, M.J.: The design of browsing and berry-picking techniques for online search interface. Online Review 13, 407–424 (1989)CrossRefGoogle Scholar
  4. 4.
    Bozzon, A., Brambilla, M., Ceri, S., Fraternali, P.: Liquid query: multi-domain exploratory search on the web. In: WWW 2010: Proceedings of the 19th International Conference on World Wide Web, pp. 161–170. ACM, New York (2010)Google Scholar
  5. 5.
    Bozzon, A., Brambilla, M., Ceri, S., Fraternali, P., Manolescu, I.: Liquid Queries and Liquid Results in Search Computing. In: Ceri, S., Brambilla, M. (eds.) Search Computing. LNCS, vol. 5950, pp. 244–267. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Bozzon, A., Brambilla, M., Catarci, T., Ceri, S., Fraternali, P., Matera, M.: Visualization of Multi-Domain Ranked Data. In: Ceri, S., Brambilla, M. (eds.) Search Computing II. LNCS, vol. 6585, pp. 53–69. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Braga, D., Corcoglioniti, F., Grossniklaus, M., Vadacca, S.: Efficient Computation of Search Computing Queries. In: Ceri, S., Brambilla, M. (eds.) Search Computing II. LNCS, vol. 6585, pp. 141–155. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Brambilla, M., Tettamanti, L.: Search Computing Tools and Processes. In: Ceri, S., Brambilla, M. (eds.) Search Computing II. LNCS, vol. 6585, pp. 169–181. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Brambilla, M., Campi, A., Ceri, S., Eynard, D., Ronchi, S.: Semantic Resource Framework. In: Ceri, S., Brambilla, M. (eds.) Search Computing II. LNCS, vol. 6585, pp. 73–84. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Broder, A.: A taxonomy of web search. SIGIR Forum 36(2), 3–10 (2002)CrossRefzbMATHGoogle Scholar
  11. 11.
    ezDL, Easy Access to Digital Libraries,
  12. 12.
    Fuhr, N.: A probability ranking principle for interactive information retrieval. Inf. Retr. 11(3), 251–265 (2008)CrossRefGoogle Scholar
  13. 13.
    Huttenhower, C., Mehmood, S.O., Troyanskaya, O.G.: Graphle: Interactive exploration of large, dense graphs. BMC Bioinformatics 10(417) (2009), doi:10.1186/1471-2105-10-417Google Scholar
  14. 14.
    Kuhlthau, C.C.: Kuhlthau’s information search process. In: Fisher, K., Erdelez, S., Lynne, E.F., McKechnie (eds.) Theories of information behavior, pp. 230–234. Information Today, Medford (2005)Google Scholar
  15. 15.
    Kumar, R., Tomkins, A.: A Characterization of Online Search Behaviour. Data Engineering Bullettin 32(2) (June 2009)Google Scholar
  16. 16.
    Marchionini, G.: Exploratory search: from finding to understanding. Commun. ACM 49(4), 41–46 (2006)CrossRefGoogle Scholar
  17. 17.
    Munzner, T.: Exploring large graphs in 3D hyperbolic space. IEEE Computer Graphics and Applications 18(4), 18–23 (1998)CrossRefGoogle Scholar
  18. 18.
    Pirolli, P., Stuart, K.C.: Information Foraging. Psychological Review 106(4), 643–675 (1999)CrossRefGoogle Scholar
  19. 19.
    Robins, D.: Interactive Information Retrieval: Context and Basic Notions. Informing Science Journal 3(2), 57–62 (2000)Google Scholar
  20. 20.
    Rose, D.: The information-seeking funnel. In: Marchionini, G., White, R. (eds.) National Science Foundation workshop on Information-Seeking Support Systems (ISSS), Chapel Hill, NC, June 26-27 (2008)Google Scholar
  21. 21.
    Rose, D.E., Levinson, D.: Understanding user goals in Web search. In: WWW 2004, Proceedings of the 13th International Conference on World Wide Web, New York, NY, USA, pp. 13–19 (2004)Google Scholar
  22. 22.
    Tzitzikas, Y., Hainaut, J.-L.: How to tame a very large ER diagram (Using link analysis and force-directed drawing algorithms). In: Delcambre, L.M.L., Kop, C., Mayr, H.C., Mylopoulos, J., Pastor, Ó. (eds.) ER 2005. LNCS, vol. 3716, pp. 144–159. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  23. 23.
    Wattenberg, M.: Visual exploration of multivariate graphs. In: Proceedings of the SIGCHI conference on Human Factors in computing systems (CHI 2006), pp. 811–819. ACM, New York (2006)CrossRefGoogle Scholar
  24. 24.
    White, R.W., Muresan, G., Marchionini, G.: ACM SIGIR Workshop on Evaluating Exploratory Search Systems, Seattle (2006)Google Scholar
  25. 25.
    White, R.W., Drucker, S.M.: Investigating behavioural variability in web search. In: 16th WWW Conf., Banff, Canada, pp. 21–30 (2007)Google Scholar
  26. 26.
    White, R.W., Roth, R.A.: Exploratory Search. In: Marchionini, G. (ed.) Beyond the Query–Response Paradigm. Synthesis Lectures on Information Concepts, Retrieval, and Services Series, vol. 3. Morgan & Claypool, San Francisco (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Alessandro Bozzon
    • 1
  • Marco Brambilla
    • 1
  • Stefano Ceri
    • 1
  • Piero Fraternali
    • 1
  1. 1.Dipartimento di Elettronica ed InformazionePolitecnico di MilanoMilanoItaly

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