On estimating access costs in relational databases
Information Processing Letters
Estimating the cost of updates in a relational database
ACM Transactions on Database Systems (TODS)
Choice of the optimal number of blocks for data access by an index
Information Systems
A general framework for computing block accesses
Information Systems
A unifying approach to evaluating block accesses in database organizations
Information Processing Letters
Optimization Strategies for Relational Queries
IEEE Transactions on Software Engineering
Implications of certain assumptions in database performance evauation
ACM Transactions on Database Systems (TODS)
Estimating block accesses in database organizations: a closed noniterative formula
Communications of the ACM
Approximating block accesses in database organizations
Communications of the ACM
A stochastic evaluation model for database organizations in data retrieval systems
Communications of the ACM
Analysis and performance of inverted data base structures
Communications of the ACM
Access path selection in a relational database management system
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
IEEE Transactions on Knowledge and Data Engineering
Block Access Estimation for Clustered Data Using a Finite LRU Buffer
IEEE Transactions on Software Engineering
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A method is proposed for dealing with nonuniform data distributions in database organizations in order to estimate the expected number of blocks containing the tuples requested by a query. When tuples with equal attribute value are not uniformly distributed over the blocks of secondary memory that store the relation, a clustering effect is observed. This can be detected by means of a single parameter, the clustering factor, which can be stored in the system catalog. The method can be applied to uniform data distributions as well, since it is shown that a uniform distribution can be viewed as a particular instance of a class of clustered distributions. In this case the proposed method allows considerable reduction of the number of computational steps needed to compute the estimated result.