Please use this identifier to cite or link to this item: https://doi.org/10.1023/A:1014333932021
Title: Effective query size estimation using neural networks
Authors: Lu, H. 
Setiono, R. 
Keywords: Cost based optimization
Neural networks
Query processing
Query size estimation
Relational algebra operations
Issue Date: 2002
Source: Lu, H., Setiono, R. (2002). Effective query size estimation using neural networks. Applied Intelligence 16 (3) : 173-183. ScholarBank@NUS Repository. https://doi.org/10.1023/A:1014333932021
Abstract: 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.
Source Title: Applied Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/42908
ISSN: 0924669X
DOI: 10.1023/A:1014333932021
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