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Adaptive selectivity estimation using query feedback
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Source International Conference on Management of Data archive
Proceedings of the 1994 ACM SIGMOD international conference on Management of data table of contents
Minneapolis, Minnesota, United States
Pages: 161 - 172  
Year of Publication: 1994
ISBN:0-89791-639-5
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Authors
Chungmin Melvin Chen  Department of Computer Science, University of Maryland, College Park, MD
Nick Roussopoulos  Department of Computer Science, University of Maryland, College Park, MD
Sponsors
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 10,   Downloads (12 Months): 33,   Citation Count: 45
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ABSTRACT

In this paper, we propose a novel approach for estimating the record selectivities of database queries. The real attribute value distribution is adaptively approximated by a curve-fitting function using a query feedback mechanism. This approach has the advantage of requiring no extra database access overhead for gathering statistics and of being able to continuously adapt the value distribution through queries and updates. Experimental results show that the estimation accuracy of this approach is comparable to traditional methods based on statistics gathering.


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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CITED BY  45

Collaborative Colleagues:
Chungmin Melvin Chen: colleagues
Nick Roussopoulos: colleagues