Efficient Top-k Query Evaluation on Probabilistic Data

@article{R2007EfficientTQ,
  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},
  year={2007},
  pages={886-895}
}
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
Highly Influential
This paper has highly influenced 20 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 401 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 271 extracted citations

402 Citations

050'08'11'14'17
Citations per Year
Semantic Scholar estimates that this publication has 402 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 25 references

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

Similar Papers

Loading similar papers…