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A Robust, Optimization-Based Approach
for Approximate Answering of Aggregate
Queries
May 1, 2001

Download Document

BibTex
Authors

Surajit Chaudhuri

Gautam Das

Vivek Narasayya
Published In

SIGMOD
Publication Type
Inproceedings
Book Title
SIGMOD
Pages
45
Number
MSR-TR-2001-37
Publisher
Association for Computing Machinery, Inc.
Copyright © 2007 by the Association for Computing Machinery, Inc. Permission to make
digital or hard copies of part or all of this work for personal or classroom use is granted without
fee provided that copies are not made or distributed for profit or commercial advantage and that
copies bear this notice and the full citation on the first page. Copyrights for components of this
work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy
otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific
permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212)
869-0481, or permissions@acm.org. The definitive version of this paper can be found at
ACM’s Digital Library --http://www.acm.org/dl/.

Abstract

Related Info
Abstract
The ability to approximately answer aggregation queries accurately and efficiently is of great
benefit for decision support and data mining tools. In contrast to previous sampling-based
studies, we treat the problem as an optimization problem whose goal is to minimize the error in
answering queries in the given workload. A key novelty of our approach is that we can tailor the
choice of samples to be robust even for workloads that are “similar― but not necessarily
identical to the given workload. Finally, our techniques recognize the importance of taking into
account the variance in the data distribution in a principled manner. We show how our solution
can be implemented on a database system, and present results of extensive experiments on
Microsoft SQL Server 2000 that demonstrate the superior quality of our method compared to
previous work.
Related Info
Related Files
 SIG01-AQP.pdf
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 Data Management, Exploration and Mining (DMX)
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 Search and information retrieval
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 Microsoft Research Lab - Redmond
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