Selectivity estimation using homogeneity measurement

Author

Chen, Meng Chang ; McNamee, Lawrence ; Matlo, Norman

Author_Institution

Dept. of Comput. Sci., California Univ., Los Angeles, CA, USA

fYear

1990

fDate

5-9 Feb 1990

Firstpage

304

Lastpage

310

Abstract

A new approach is presented for organizing a large collection of multidimensional data with an unknown distribution by partitioning the data such that the data are relatively homogeneously distributed in each block. A multidimensional tree is generated according to this partition. After the tree is generated, summary data estimation such as selectively estimation can be performed via a tree search. This approach is applicable to both ordered and categorial attributes. The merits of this method are verified theoretically and by simulation

Keywords

database management systems; database theory; information retrieval systems; trees (mathematics); categorial attributes; homogeneity measurement; homogeneously distributed; multidimensional data; multidimensional tree; selectively estimation; summary data estimation; tree search; unknown distribution; Additives; Computer science; Data engineering; Databases; Decision making; Gaussian distribution; Organizing; Parametric statistics; Query processing; Statistical distributions;

fLanguage

English

Publisher

ieee

Conference_Titel

Data Engineering, 1990. Proceedings. Sixth International Conference on

Conference_Location

Los Angeles, CA

Print_ISBN

0-8186-2025-0

Type

conf

DOI

10.1109/ICDE.1990.113482

Filename

113482