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Effective query size estimation using neural networks
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DOI：10.1023/A:1014333932021.png)
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摘要
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This paper describes a novel approach to estimate the size of database query results using neural networks. Using the proposed approach, three layer neural networks are constructed and trained to learn the cumulative distribution functions of attribute values in relations. With a trained network, the estimation of the query result size could be obtained instantly by simply computing the network output from the given query predicates. The basic computational model using a cumulative distribution function to compute the query result size is described. The network construction and training is discussed. Comprehensive experiments were conducted to study the effectiveness of the proposed approach. The results indicate that the approach produces estimates with accuracies that are comparable with or higher than those reported in the literature.
Keyword:
query processing
relational algebra operations
cost based optimization
query size estimation
neural networks
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