Thumbnail
Access Restriction
Subscribed

Author Ross, Robert ♦ Subrahmanian, V. S. ♦ Grant, John
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2005
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Aggregates ♦ Probabilistic relational databases
Abstract Though extensions to the relational data model have been proposed in order to handle probabilistic information, there has been very little work to date on handling aggregate operators in such databases. In this article, we present a very general notion of an aggregate operator and show how classical aggregation operators (such as COUNT, SUM, etc.) as well as statistical operators (such as percentiles, variance, etc.) are special cases of this general definition. We devise a formal linear programming based semantics for computing aggregates over probabilistic DBMSs, develop algorithms that satisfy this semantics, analyze their complexity, and introduce several families of approximation algorithms that run in polynomial time. We implemented all of these algorithms and tested them on a large set of data to help determine when each one is preferable.
ISSN 00045411
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2005-01-01
Publisher Place New York
e-ISSN 1557735X
Journal Journal of the ACM (JACM)
Volume Number 52
Issue Number 1
Page Count 48
Starting Page 54
Ending Page 101


Open content in new tab

   Open content in new tab
Source: ACM Digital Library