Database performance evaluation in an indexed file environment
ACM Transactions on Database Systems (TODS)
A Measurement Procedure for Queueing Network Models of Computer Systems
ACM Computing Surveys (CSUR)
Queueing Networks: A Critique of the State of the Art and Directions for the Future
ACM Computing Surveys (CSUR)
Comments on price/performance patterns of U. S. computer systems
Communications of the ACM
Price/performance patterns of U. S. computer systems
Communications of the ACM
Database performance evaluation in an indexed file environment
ACM Transactions on Database Systems (TODS)
Monitoring database performance—a control issue
ACM SIGSAC Review
Statistical profile estimation in database systems
ACM Computing Surveys (CSUR)
Practical selectivity estimation through adaptive sampling
SIGMOD '90 Proceedings of the 1990 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
Effective Query Size Estimation Using Neural Networks
Applied Intelligence
Optimizing Boolean Expressions in Object-Bases
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Approximate Query Answering In Numerical Databases
SSDBM '96 Proceedings of the Eighth International Conference on Scientific and Statistical Database Management
Information-Knowledge-Systems Management
HASE: a hybrid approach to selectivity estimation for conjunctive predicates
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
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A model of the inverted file of an automated bibliographic system is constructed using the Zipf distribution of word frequency. By ascertaining the parameters of the Zipfian model of the inverted file system, one can estimate the minimum data storage requirements of the database. In addition, given a few additional system parameters, access time for a specified query can be estimated. The estimation procedures are accomplished using logarithmic transformations and multiple regression techniques. This paper introduces the Zipfian models, their regression formulation, and their results and interpretation for application to database evaluation.