article · 2008
Abstract Parametric Query Optimization
Yannis E. Ioannidis, Raymond T. Ngt, Kyuseok Shim, Timos K. Sellis
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Abstract
In most database systems, the values of many impor-tant run-time parameters of the system, the data, or the query are unknown at query optimization time. Parametric query optimization attempts to identify several execution plans, each one of which is optimal for a subset of all possible values of the run-time pa-rameters. We present a general formulation of this problem and study it primarily for the buffer size pa-rameter. We adopt randomized algorithms as the main approach to this style of optimization and enhance them with a sideways information passing feature that increases their effectiveness in the new task. Experi-mental results of these enhanced algorithms show that they optimize queries for large numbers of buffer sizes in the same time needed by their conventional versions l PartiaIIy supported by NSF under PYI Grant IRI-9157368 and by grams from DEC, HP, and AT&T.
Cite this paper
Ioannidis, Y. E., Ngt, R. T., Shim, K., & Sellis, T. K. (2008). Abstract Parametric Query Optimization.
Ioannidis, Yannis E., et al. “Abstract Parametric Query Optimization.” 2008.
Ioannidis, Yannis E., Raymond T. Ngt, Kyuseok Shim, and Timos K. Sellis. 2008. “Abstract Parametric Query Optimization.”
Ioannidis, Y.E. et al. (2008) ‘Abstract Parametric Query Optimization’.
@article{ioannidis2008abstract,
title = {{Abstract Parametric Query Optimization}},
author = {Ioannidis, Yannis E. and Ngt, Raymond T. and Shim, Kyuseok and Sellis, Timos K.},
year = {2008}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.