Efficient Top-k Query Evaluation on Probabilistic Data

  title={Efficient Top-k Query Evaluation on Probabilistic Data},
  author={Christopher R{\'e} and Nilesh N. Dalvi and Dan Suciu},
  journal={2007 IEEE 23rd International Conference on Data Engineering},
Modern enterprise applications are forced to deal with unreliable, inconsistent and imprecise information. Probabilistic databases can model such data naturally, but SQL query evaluation on probabilistic databases is difficult: previous approaches have either restricted the SQL queries, or computed approximate probabilities, or did not scale, and it was shown recently that precise query evaluation is theoretically hard. In this paper we describe a novel approach, which computes and ranks… CONTINUE READING
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Publications referenced by this paper.
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Efficient topk query evaluation on probabilistic data ( extended version )

  • I. F. Ilyas, K. C.-C. Chang.
  • 2006

Evaluation of having queries on probabilistic databases

  • C. Re, N.Dalvi, D.Suciu
  • 2006
1 Excerpt

New developments in ranking and selection : an empirical comparison of three main approaches

  • S. Chick Branke, C. Schmidt
  • Proceedings of the Winter Simulation Conference
  • 2005

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