Index Selection in a Self-Adaptive Relational Data Base Management System.
MASSACHUSETTS INST OF TECH CAMBRIDGE LAB FOR COMPUTER SCIENCE
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The development of large integrated data bases that support a variety of applications in an enterprise promises to be one of the most important data processing activities of the next decade. The effective utilization of such a data bases depends on the ability of data base management systems to cope with the evolution of data base applications. In this thesis, we attempt to develop a methodology for monitoring the developing pattern of access to a data base and for choosing near-optimal physical data base organizations based on the evidenced mode of use. More specifically, we consider the problem of adaptively selecting the set of secondary indices to be maintained in an integrated relational data base. Stress is placed on the acquisition of an accurate usage model and on the precise estimation of data base characteristics, through the use of access monitoring and the application of forecasting and smoothing techniques.
- Administration and Management