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Abstract

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.

Keywords

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

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

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