ACM Home Page
Please provide us with feedback. Feedback
Practical selectivity estimation through adaptive sampling
Full text PdfPdf (1.20 MB)
Source International Conference on Management of Data archive
Proceedings of the 1990 ACM SIGMOD international conference on Management of data table of contents
Atlantic City, New Jersey, United States
Pages: 1 - 11  
Year of Publication: 1990
ISBN:0-89791-365-5
Also published in ...
Authors
Richard J. Lipton  Department of Computer Science, Princeton University
Jeffrey F. Naughton  Department of Computer Sciences, University of Wisconsin
Donovan A. Schneider  Department of Computer Sciences, University of Wisconsin
Sponsor
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): n/a,   Downloads (12 Months): n/a,   Citation Count: 85
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/93597.93611
What is a DOI?

ABSTRACT

Recently we have proposed an adaptive, random sampling algorithm for general query size estimation. In earlier work we analyzed the asymptotic efficiency and accuracy of the algorithm, in this paper we investigate its practicality as applied to selects and joins. First, we extend our previous analysis to provide significantly improved bounds on the amount of sampling necessary for a given level of accuracy. Next, we provide “sanity bounds” to deal with queries for which the underlying data is extremely skewed or the query result is very small. Finally, we report on the performance of the estimation algorithm as implemented in a host language on a commercial relational system. The results are encouraging, even with this loose coupling between the estimation algorithm and the DBMS.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
BDT83
Chr83a
 
Chr83b
S Chrxstodoulakxs Estxmatmg record selectlvltles Informatzon Systems, 8(2) 69- 79, 1983
 
Dem80
R Demolombe Estimation of the number of tuples satxsfymg a query expressed in predicate calculus language In Proc Szzth VLDB, pages 55-63, 1980
Fed84
 
Fel68
W Feller An Introduction to Probability Theory and Its Applzcatsons, volume 1 John Wiley and Sons, inc, New York, New York, 1968
HOT88
HOT89
HTY82
KK85
 
Koo80
P~ Kool The optzm~zat~on of quemes zn relatzonal database systems PhD thesis, Case Western Umverslty, Cleveland, Ohio, 1980
 
LN89
LN90
 
LNS90
1~ Lipton and J Naughton and D Schneider Practical Selectlwty Estimation through Adaptive Samphng Umverslty of Wisconsin-Madison Computer Sciences Department Techmcal Report, March 1990
 
Lyn88
MCS88
MD88
 
MDL83
A Montgomery, D D'Souza, and S Lee The cost of relatmnal algebraic operatmns on skewed data Estimates and experiments Information Processing Letters, pages 235-241, 1983
 
MK85
B Muthuswamy and G Kerschberg A DDSM for relatmnal query optlmlzatmn Techmcal report, Umverslty of South Carohna, Columbia, 1985 As cited in {MCS88}
 
OR86
 
OR89
PSC84
SAC+79

CITED BY  85

Collaborative Colleagues:
Richard J. Lipton: colleagues
Jeffrey F. Naughton: colleagues
Donovan A. Schneider: colleagues