Ranked retrieval plays an important role in explorative querying, where the user is interested in the top k results of complex ad-hoc queries. In such a scenario, response times are very important, but at the same time, tuning techniques, such as materialized views, are hard to use. Therefore it would be highly desirable to exploit the top-k property of the query to speed up the computation, reducing intermediate results and thus execution time. We present a novel approach to optimize ad-hoc top-k queries, propagating the top-k nature down the execution plan. Our experimental results support our claim that integrating top-k processing into algebraic optimization greatly reduces the query execution times and provides strong evidence that the resulting execution plans are robust against statistical misestimations.


Query Processing Execution Plan Query Optimizer Ranking Attribute Query Execution Time 
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.
    Li, C., Chang, K.C.C., Ilyas, I.F., Song, S.: Ranksql: Query algebra and optimization for relational top-k queries. In: SIGMOD (2005)Google Scholar
  2. 2.
    Fagin, R., Lotem, A., Naor, M.: Optimal aggregation algorithms for middleware. In: PODS (2001)Google Scholar
  3. 3.
    Theobald, M., Weikum, G., Schenkel, R.: Top-k query evaluation with probabilistic guarantees. In: VLDB (2004)Google Scholar
  4. 4.
    Chaudhuri, S., Gravano, L., Marian, A.: Optimizing top-k selection queries over multimedia repositories. TKDE 16(8), 992–1009 (2004)Google Scholar
  5. 5.
    Ilyas, I.F., Shah, R., Aref, W.G., Vitter, J.S., Elmagarmid, A.K.: Rank-aware query optimization. In: SIGMOD, pp. 203–214 (2004)Google Scholar
  6. 6.
    Ilyas, I.F., Aref, W.G., Elmagarmid, A.K.: Supporting top-k join queries in relational databases. In: VLDB, pp. 754–765 (2003)Google Scholar
  7. 7.
    Carey, M.J., Kossmann, D.: Reducing the braking distance of an sql query engine. In: VLDB (1998)Google Scholar
  8. 8.
    Markl, V., Megiddo, N., Kutsch, M., Tran, T.M., Haas, P.J., Srivastava, U.: Consistently estimating the selectivity of conjuncts of predicates. In: VLDB (2005)Google Scholar
  9. 9.
    Stillger, M., Lohman, G.M., Markl, V., Kandil, M.: Leo - db2’s learning optimizer. In: VLDB, pp. 19–28 (2001)Google Scholar
  10. 10.
    Avnur, R., Hellerstein, J.M.: Eddies: Continuously adaptive query processing. In: SIGMOD (2000)Google Scholar
  11. 11.
    Ilyas, I.F., Markl, V., Haas, P.J., Brown, P., Aboulnaga, A.: Cords: Automatic discovery of correlations and soft functional dependencies. In: SIGMOD (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Thomas Neumann
    • 1
  1. 1.Max-Planck-Institut Informatik, SaarbrückenGermany

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