On estimating access costs in relational databases
Information Processing Letters
Choice of the optimal number of blocks for data access by an index
Information Systems
Physical database design for relational databases
ACM Transactions on Database Systems (TODS)
Statistical profile estimation in database systems
ACM Computing Surveys (CSUR)
Index scans using a finite LRU buffer: a validated I/O model
ACM Transactions on Database Systems (TODS)
Database access path selection: a two step approach
Information Systems
Implications of certain assumptions in database performance evauation
ACM Transactions on Database Systems (TODS)
The difficulty of optimum index selection
ACM Transactions on Database Systems (TODS)
Minimum cost selection of secondary indexes for formatted files
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
Approximating block accesses in database organizations
Communications of the ACM
Analysis and performance of inverted data base structures
Communications of the ACM
Estimating Bucket Accesses: A Practical Approach
Proceedings of the Second International Conference on Data Engineering
Estimating Block Accessses when Attributes are Correlated
VLDB '86 Proceedings of the 12th International Conference on Very Large Data Bases
A Comprehensive Analytical Performance Model for Disk Devices under Random Workloads
IEEE Transactions on Knowledge and Data Engineering
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Access path deployment is a critical issue in physical database design. Access paths typically include a clustered index as the primary access path and a set of secondary indexes as auxiliary access paths. To deploy the right access paths requires an effective algorithm and accurate estimation of the parameters used by the algorithm. One parameter central to any index-selection algorithm is the block selectivity of a query. Existing methods for estimating block selectivities are limited by restrictive assumptions. Furthermore, most existing methods produce estimates useful for aiding the selection of secondary indexes only. Little research has been done in the area of estimating block selectivities for supporting the selection of the clustered index. The paper presents a set of methods that do not depend on any specific assumption, produce accurate estimates, and can be used to aid in selecting the clustered index as well as secondary indexes.