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Space-efficient online computation of quantile summaries
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Source International Conference on Management of Data archive
Proceedings of the 2001 ACM SIGMOD international conference on Management of data table of contents
Santa Barbara, California, United States
Pages: 58 - 66  
Year of Publication: 2001
ISBN:1-58113-332-4
Also published in ...
Authors
Michael Greenwald  Computer & Information Science Department, University of Pennsylvania, 200 South 33rd Street, Philadelphia, PA
Sanjeev Khanna  Computer & Information Science Department, University of Pennsylvania, 200 South 33rd Street, Philadelphia, PA
Sponsor
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
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ABSTRACT

An ∈-approximate quantile summary of a sequence of N elements is a data structure that can answer quantile queries about the sequence to within a precision of ∈N.

We present a new online algorithm for computing∈-approximate quantile summaries of very large data sequences. The algorithm has a worst-case space requirement of &Ogr;(1÷∈ log(∈N)). This improves upon the previous best result of &Ogr;(1÷∈ log2(∈N)). Moreover, in contrast to earlier deterministic algorithms, our algorithm does not require a priori knowledge of the length of the input sequence.

Finally, the actual space bounds obtained on experimental data are significantly better than the worst case guarantees of our algorithm as well as the observed space requirements of earlier algorithms.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Viswanath Poosala, Venkatesh Ganti, and Yannis E. Ioannidis. Approximate query answering using histograms. Bulletin of the IEEE Technical Committee on Data Engineering, 22(4):6-15, December 1999.
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CITED BY  87

Collaborative Colleagues:
Michael Greenwald: colleagues
Sanjeev Khanna: colleagues