Equi-depth multidimensional histograms
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
Multilayer feedforward networks are universal approximators
Neural Networks
Practical selectivity estimation through adaptive sampling
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Statistical estimators for aggregate relational algebra queries
ACM Transactions on Database Systems (TODS)
On the propagation of errors in the size of join results
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Sequential sampling procedures for query size estimation
SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Adaptive selectivity estimation using query feedback
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Statistical estimators for relational algebra expressions
Proceedings of the seventh ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Algorithm 500: Minimization of Unconstrained Multivariate Functions [E4]
ACM Transactions on Mathematical Software (TOMS)
Estimating block transfers and join sizes
SIGMOD '83 Proceedings of the 1983 ACM SIGMOD international conference on Management of data
Database evaluation using multiple regression techniques
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Accurate estimation of the number of tuples satisfying a condition
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
VLDB '88 Proceedings of the 14th International Conference on Very Large Data Bases
Query Size Estimation Using Machine Learning
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
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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.