Selectivity estimation for spatial joins

@article{An2001SelectivityEF,
  title={Selectivity estimation for spatial joins},
  author={Ning An and Zhen-Yu Yang and Anand Sivasubramaniam},
  journal={Proceedings 17th International Conference on Data Engineering},
  year={2001},
  pages={368-375}
}
Spatial joins are important and time consuming operations in spatial database management systems. It is crucial to be able to accurately estimate the performance of these operations so that one can derive efficient query execution plans, and even develop/refine data structures to improve their performance. While estimation techniques for analyzing the performance of other operations, such as range queries, on spatial data has come under scrutiny, the problem of estimating selectivity for… CONTINUE READING

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Key Quantitative Results

  • It is shown that in most cases, picking samples randomly, with a sample size of 5-10% of the dataset, gives less than 10% errors at a overhead that is around 10% of the join time when the R-trees for the two datasets are not available.
  • In general, we find that if the R-trees are not available for the datasets, we can get the estimation error within 10% with sample sizes of 10% (i.e. 10/10), with time overheads that are also within 10% for random sampling (RSWR).

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References

Publications referenced by this paper.
SHOWING 1-10 OF 11 REFERENCES