Chapter 13: Liquid Queries and Liquid Results in Search Computing

  • Alessandro Bozzon
  • Marco Brambilla
  • Stefano Ceri
  • Piero Fraternali
  • Ioana Manolescu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5950)


Liquid queries are a flexible tool for information seeking, based on the progressive exploration of the search space; they produce “fluid” results which dynamically adapt to the shape of the query, as a liquid adapts to its container. The liquid query paradigm relies on the SeCo service mart and multi-domain query execution concepts: an expert user selects a priori the service marts relevant to the information seeking task at hand and the connections necessary to join them, and publishes such a definition in the SeCo back-end. The Liquid Query client-side interface consumes the application definition created by the expert and dynamically builds a query interface for the end-user. Such interface allows one to supply keywords to query the pre-configured service marts and offers controls for exploring the combinations computed by the SeCo execution engine. The interaction commands are based on a tabular representation of results and comprise: reordering, clustering, addition or deletion of attributes, addition of extra service marts to the query for specific items in the result set or for the entire result set, request of more results from all services or from selected ones, expansion of details on selected items, and more. The Liquid Query is equipped with multiple data visualization options suited to render multi-domain results and can be instrumented with indicators showing the quality of the result set.


user interfaces exploratory search search computing 


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  1. 1.
    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
  2. 2.
    Baeza-Yates, R.: Applications of Web Query Mining. In: Losada, D.E., Fernández-Luna, J.M. (eds.) ECIR 2005. LNCS, vol. 3408, pp. 7–22. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Barbosa, L., Freire, J.: Siphoning hidden-web data through keyword-based interfaces. In: SBBD 2004 (XIX Simpósio Brasileiro de Bancos de Dados, 18-20 de Outubro, Brasília, Distrito Federal, Brasil, pp. 309–321 (2004)Google Scholar
  4. 4.
    Bozzon, A., Brambilla, M., Fraternali, F.: Conceptual Modelling of Multimedia Search Applications Using Rich Process Models. In: ICWE 2009, pp. 315–329 (2009)Google Scholar
  5. 5.
    Brambilla, M., Cabot, J., Grossniklaus, M.: Modelling safe interface interactions in web applications. In: Laender, A.H.F. (ed.) ER 2009. LNCS, ch. 29, vol. 5829, pp. 387–400. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Broder, A.: A taxonomy of web search. SIGIR Forum 36(2), 3–10 (2002)CrossRefzbMATHGoogle Scholar
  7. 7.
    Cafarella, M.J., Halevy, A., Zhang, Y., Wang, D.Z., Wu, E.: WebTables: Exploring the Power of Tables on the Web. In: Proceedings of the VLDB Endowment, August 2008, vol. 1(1), pp. 538–549 (2008)Google Scholar
  8. 8.
    Clusty (2009),
  9. 9.
    Dash, D., Rao, J., Megiddo, N., Ailamaki, A., Lohman, G.: Dynamic faceted search for discovery-driven analysis. In: Proceeding of the 17th ACM Conference on information and Knowledge Management, CIKM 2008, Napa Valley, California, USA, October 26-30, pp. 3–12. ACM, New York (2008)Google Scholar
  10. 10.
    DBPL Faceted Search (2009),
  11. 11.
    Google Fusion Tables (2009),
  12. 12.
    Google Squared (2009),
  13. 13.
  14. 14.
    Freebase Parallax (2009),
  15. 15.
    HAKIA (2009),
  16. 16.
    Hunch (2009),
  17. 17.
    Inselberg, A.: The Plane with Parallel Coordinates. Visual Computer 1(4), 69–91 (1985)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Jansen, B.J., Booth, D.L., Spink, A.: Determining the user intent of web search engine queries. In: WWW 2007, pp. 1149–1150 (2007)Google Scholar
  19. 19.
    Jansen, B.J., Pooch, U.W.: A review of Web searching studies and a framework for future research. JASIST 52(3), 235–246 (2001)CrossRefGoogle Scholar
  20. 20.
    Kules, B., Capra, R., Banta, M., Sierra, S.: What do exploratory searchers look at in a faceted search interface? In: JCDL 2009, pp. 313–322 (2009)Google Scholar
  21. 21.
    Kumar, R., Tomkins, A.: A Characterization of Online Search Behaviour. Data Engineering Bullettin 32(2) (June 2009)Google Scholar
  22. 22.
    Lee, U., Liu, Z., Cho, J.: Automatic identification of user goals in Web search. In: WWW 2005, pp. 391–400 (2005)Google Scholar
  23. 23.
    Marchionini, G.: Exploratory search: from finding to understanding. Commun. ACM 49(4), 41–46 (2006)CrossRefGoogle Scholar
  24. 24.
    Microsoft Bing (2009),
  25. 25.
    Minack, E., Demartini, G., Nejdl, W.: Current Approaches to Search Result Diversification, L3S Techical Report,
  26. 26.
    Pirolli, P., Stuart, K.C.: Information Foraging. Psychological Review 106(4), 643–675 (1999)CrossRefGoogle Scholar
  27. 27.
    Rajaraman, A.: Kosmix: High Performance Topic Exploration using the Deep Web. In: Proceedings of the VLDB Endowment, August 2008, vol. 2(1), pp. 1524–1529 (2009)Google Scholar
  28. 28.
    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
  29. 29.
    Sacco, S.M., Tzitzikas, Y.: Dynamic Taxonomies and Faceted Search, Theory, Practice, and Experience. The Information Retrieval Series, vol. 25, p. 340. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  30. 30.
    Shafer, J.C., Agrawal, R., Lauw, H.W.: Symphony: Enabling Search-Driven Applications. In: USETIM (Using Search Engine Technology for Information Management) Workshop, VLDB Lyon (2009)Google Scholar
  31. 31.
    White, R.W., Muresan, G., Gary, M.: ACM SIGIR Workshop on Evaluating Exploratory Search Systems, Seattle (2006)Google Scholar
  32. 32.
    White, R.W., Drucker, S.M.: Investigating behavioural variability in web search. In: 16th WWW Conf., Banff, Canada, pp. 21–30 (2007)Google Scholar
  33. 33.
    White, R.W., Roth, R.A.: Exploratory Search. Beyond the Query–Response Paradigm. In: Marchionini, G. (ed.) Synthesis Lectures on Information Concepts, Retrieval, and Services Series, vol. 3. Morgan & Claypool, San Francisco (2009)Google Scholar
  34. 34.
    Wolfram Alpha (2009),
  35. 35.
    Yahoo! SearchMonkey (2009),
  36. 36.
    Yahoo! Pipes (2009),
  37. 37.
    YQL: Yahoo! Query Language (2009),

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alessandro Bozzon
    • 1
  • Marco Brambilla
    • 1
  • Stefano Ceri
    • 1
  • Piero Fraternali
    • 1
  • Ioana Manolescu
    • 2
  1. 1.Dipartimento di Elettronica e InformazionePolitecnico di MilanoMilanoItaly
  2. 2.INRIA, Saclay-Ile-de-France and LRIUniversité de Paris Sud-11France

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