Query-through-Drilldown: Data-Oriented Extensional Queries

Alan Dix1 and Damon Oram2

1 Computing Department, Infolab21, Lancaster University, Lancaster, UK
2 Corporate Information Systems, Information Systems Services, Lancaster University, Lancaster, UK

< Alan on the Web > < Damon on the Web >

Paper at AVI'2008, 28-30 May 2006, Napoli, ITALY.

Download full paper (PDF, 919K)


Traditional database query formulation is intensional: at the level of schemas, table and column names.  Previous work has shown that filters can be created using a query paradigm focused on interaction with data tables. This paper presents a technique, Query-through-Drilldown, to enable join formulation in a data-oriented paradigm.  Instead of formulating joins at the level of schemas, the user drills down through tables of data and the query is implicitly created based on the user's actions. Query-through-Drilldown has been applied to a large relational database, but similar techniques could be applied to semi-structured data or semantic web ontologies.

keywords: database query, data-oriented interaction,  SQL, tabular interface, extensional query,  data structure mining, query-by-browsing


  1. Batini, C.   Catarci, T.   Costabile, M.F.   Levialdi, S.  1991. Visual strategies for querying databases In Proc. of IEEE Workshop on Visual Languages (Kobe, Japan, 8-11 Oct 1991), 183-189.

  2. Carey, M., Haas, L., Maganty, V., and Williams. J. 1996. PESTO : an integrated query/browser for object databases. In Proc. of the Int. Conference on Very Large Databases (VLDB), (Mumbai, India, August 1996). 203-214

  3. Conklin, N., Prabhakar, S., and North, C. 2002. Multiple Foci Drill-Down through Tuple and Attribute Aggregation Polyarchies in Tabular Data. In Proc. of the IEEE Symposium on information Visualization (InfoVis'02) (October 28 - 29, 2002). IEEE Comp. Soc., 131–134

  4. Dix, A. 1992. Human issues in the use of pattern recognition techniques. In Neural Networks and Pattern Recognition in Human Computer Interaction Eds. R. Beale and J. Finlay. Ellis Horwood. 429-451.

  5. Dix, A. and Patrick, A. 1994. Query By Browsing. In Proc. of IDS'94: The 2nd International Workshop on User Interfaces to Databases, P. Sawyer, Ed. Springer Verlag. 236-248.

  6. Dix, A. 1998. Interactive Querying - locating and discovering information. Second Workshop on Information Retrieval and Human Computer Interaction, (Glasgow, 11th Sept. 1998). http://www.hcibook.com/alan/papers/IQ98/

  7. Greene, S. L., Gomez, L. M., and Devlin, S. J. (1986).  A Cognitive Analysis of Database Query Production, In Proc. of the Human Factors Society, 9-13.

  8. Jetter, H.-C., Gerken, J., Konig, W., Grun, C. and Reiterer, H. (2005): HyperGrid - Accessing Complex Information Spaces. In: Proc. of the HCI05 Conference on People and Computers XIX 2005. 349-364.

  9. Liang, G. 2007. Method and System for Visual Query Construction and Representation. United States Patent 20070260582. Publication Date: 11/08/2007. http://www.freepatentsonline.com/20070260582.html

  10. MySQL 5.1 Reference Manual, Section 5.2.3. The General Query Log. Accessed 19th December 2007. http://dev.mysql.com/doc/refman/5.1/en/query-log.html

  11. Pirolli, P., Schank, P., Hearst, M., and Diehl, C. 1996. Scatter/gather browsing communicates the topic structure of a very large text collection. In Proc. CHI '96. ACM, New York, NY, 213-220.

  12. Pollitt, A. S., Ellis, G. P., and Smith, M. P. 1994. HIBROWSE for bibliographic database. J. Inf. Sci. 20, 6 (Nov. 1994), 413-426.

  13. Polyviou, S., Evripidou, P. and Samaras, G. 2004. Query by Browsing: A Visual Query Language Based on the Relational Model and the Desktop User Interface Paradigm. The 3rd Hellenic Symposium on Data Management, (HDMS04), (Athens, Greece, 28-29 June 2004).

  14. Prud'hommeaux, E. and Seaborne, A. (eds.) 2007. SPARQL Query Language for RDF. W3C Recommendation, 12 November 2007, http://www.w3.org/TR/2007/PR-rdf-sparql-query-20071112/. Latest version available at http://www.w3.org/TR/rdf-sparql-query/.

  15. Quinlan, J. R. 1986. Induction of Decision Trees. Mach. Learn. 1, 1 (Mar. 1986), 81-106.

  16. Rao, R. and Card, S. K. 1994. The table lens: merging graphical and symbolic representations in an interactive focus + context visualization for tabular information. In Proc. CHI '94. ACM, New York, 318-322

  17. Robertson, G., Cameron, K., Czerwinski, M., and Robbins, D. 2002. Polyarchy visualization: visualizing multiple intersecting hierarchies. In Proc. CHI '02. ACM, New York, NY, 423-430.

  18. schraefel, m. Karam, M., and Zhao, S. 2003. mSpace: interaction design for user-determined, adaptable domain exploration in hypermedia. In Proc. AH2003 Workshop on Adaptive Hypermedia and Adaptive Web-Based Systems,, 217–235
     Witkowski, A., Bellamkonda, S., Bozkaya, T., Naimat, A.,

  19. Sheng, L., Subramanian, S., and Waingold, A. 2005. Query by Excel. In Proc. of the 31st international Conference on Very Large Data Bases (Trondheim, Norway, August 30 - September 02, 2005). Very Large Data Bases. VLDB Endowment, 1204-1215.

  20. Zloof, M. (1975). Query by example. Proc. AFIPS National Computer Conf. 44, AFIPS Press, New Jersey. 431-438.

[[full size]]

Figure 1. QbB (web interface) user selects records.

[[full size]]

Figure 2. QbB generates SQL and highlights query results.

(a) [[full size]]

(b) [[full size]]

Figure 3. Selecting a column to drilldown through

[[full size]]

Figure 4. Selected column expands.

[[full size]]

Figure 6.  Additional column for m–n relation.

[[full size]]

Figure 7.  Complex query: added columns and reordered.

[[full size]]

Figure 8.  Relationship graph for database.

[[full size]]

Figure 9.  Query tree.

[[full size]]

Figure 12. Prototype with four tables joined.

[[full size]]

Figure 13. Prototype after filtering and computed column.

[[full size]]

Figure 14. Long menus!


Alan Dix 26/3/2008