Algorithms for index-assisted selectivity estimation

Author

Aoki, Paul M.

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA, USA

fYear

1999

fDate

23-26 Mar 1999

Firstpage

258

Abstract

The standard mechanisms for query selectivity estimation used in relational database systems rely on properties that are specific to the attribute types. The query optimizer in an extensible database system is, in general, unable to exploit these mechanisms for user-defined types, forcing the database extender to invent new estimation mechanisms. In this paper, we discuss extensions to the generalized search tree (GiST) that simplify the creation of user-defined selectivity estimation methods. An experimental comparison of such methods with multidimensional estimators from the literature has demonstrated very competitive results

Keywords

abstract data types; database indexing; query processing; relational databases; tree searching; GiST; attribute type-specific properties; database extension; extensible database system; generalized search tree; index-assisted query selectivity estimation algorithms; multidimensional estimators; query optimizer; relational database systems; user-defined selectivity estimation methods; user-defined types; Computer vision; Costs; Database systems; Fractals; Multidimensional systems; NASA; Partitioning algorithms; Query processing; Sampling methods; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Data Engineering, 1999. Proceedings., 15th International Conference on

Conference_Location

Sydney, NSW

ISSN

1063-6382

Print_ISBN

0-7695-0071-4

Type

conf

DOI

10.1109/ICDE.1999.754938

Filename

754938