An attribute based model for database access cost analysis
ACM Transactions on Database Systems (TODS)
Estimating block accesses in database organizations: a closed noniterative formula
Communications of the ACM
On estimating block accesses in database organizations
Communications of the ACM
Estimating block accesses and number of records in file management
Communications of the ACM
A stochastic evaluation model for database organizations in data retrieval systems
Communications of the ACM
Evaluation and selection of file organization—a model and system
Communications of the ACM
A relational model of data for large shared data banks
Communications of the ACM
System Simulation
A note on estimating the cardinality of the projection of a database relation
ACM Transactions on Database Systems (TODS)
IEEE Transactions on Knowledge and Data Engineering
A Hybrid Estimator for Selectivity Estimation
IEEE Transactions on Knowledge and Data Engineering
LEO - DB2's LEarning Optimizer
Proceedings of the 27th International Conference on Very Large Data Bases
Robust query processing through progressive optimization
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
LEO: An autonomic query optimizer for DB2
IBM Systems Journal
A framework for query optimization in temporal databases
SSDBM'1990 Proceedings of the 5th international conference on Statistical and Scientific Database Management
Estimating the output cardinality of partial preaggregation with a measure of clusteredness
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Optimization of sub-query processing in distributed data integration systems
Journal of Network and Computer Applications
Progressive query optimization for federated queries
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
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We present an analytical formula for estimating the cardinality of the projection on certain attributes of a subset of a relation in a relational database. This formula takes into account a priori knowledge of the semantics of the real-world objects and relationships that the database is intended to represent. Experimental testing of the formula shows that it has an acceptably low percentage error, and that its worst-case error is smaller than the best-known formula. Furthermore, the formula presented here has the advantage that it does not require a scan of the relation.