Histogram-Based Approximation of Set-Valued Query-Answers.
Yannis E. Ioannidis, Viswanath Poosala:
Histogram-Based Approximation of Set-Valued Query-Answers.
VLDB 1999: 174-185@inproceedings{DBLP:conf/vldb/IoannidisP99,
author = {Yannis E. Ioannidis and
Viswanath Poosala},
editor = {Malcolm P. Atkinson and
Maria E. Orlowska and
Patrick Valduriez and
Stanley B. Zdonik and
Michael L. Brodie},
title = {Histogram-Based Approximation of Set-Valued Query-Answers},
booktitle = {VLDB'99, Proceedings of 25th International Conference on Very
Large Data Bases, September 7-10, 1999, Edinburgh, Scotland,
UK},
publisher = {Morgan Kaufmann},
year = {1999},
isbn = {1-55860-615-7},
pages = {174-185},
ee = {db/conf/vldb/IoannidisP99.html},
crossref = {DBLP:conf/vldb/99},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX
Abstract
Answering queries approximately has recently
been proposed as a way to reduce query response
times in on-line decision support systems, when
the precise answer is not necessary or early
feedback is helpful. Most of the work in this
area uses sampling-based techniques and handles
aggregate queries, ignoring queries that return
relations as answers. In this paper, we extend the
scope of approximate query answering to general queries.
We propose a novel and intuitive error measure for quantifying
the error in an approximate query answer, which can be a
multiset in general. We also study the use of histograms
in approximate query answering as an alternative to sampling.
In that direction, we develop a histogram algebra and
demonstrate how complex SQL queries on a database may
be translated into algebraic operations on the corresponding
histograms. Finally, we present the results of an initial
set of experiments where various types of histograms and
sampling are compared with respect to their effectiveness
in approximate query answering as captured by the introduced
error measure. The results indicate that the MaxDiff(V,A)
histograms provide quality approximations for both set-valued and
aggregate queries, while sampling is competitive mainly for
aggregate queries with no join operators.
Copyright © 1999 by the VLDB Endowment.
Permission to copy without fee all or part of this material is granted provided that the copies are not made or
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Endowment. To copy otherwise, or to republish, requires
a fee and/or special permission from the Endowment.
Online Paper
DVD Version: Load ACM SIGMOD Anthology DVD 1" and ...
BibTeX
Printed Edition
Malcolm P. Atkinson, Maria E. Orlowska, Patrick Valduriez, Stanley B. Zdonik, Michael L. Brodie (Eds.):
VLDB'99, Proceedings of 25th International Conference on Very Large Data Bases, September 7-10, 1999, Edinburgh, Scotland, UK.
Morgan Kaufmann 1999, ISBN 1-55860-615-7
Contents BibTeX
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SIGMOD Conference 1998: 331-342 BibTeX
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Fast Incremental Maintenance of Approximate Histograms.
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BibTeX
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BibTeX
Referenced by
- Kaushik Chakrabarti, Minos N. Garofalakis, Rajeev Rastogi, Kyuseok Shim:
Approximate Query Processing Using Wavelets.
VLDB 2000: 111-122
- Dimitrios Gunopulos, George Kollios, Vassilis J. Tsotras, Carlotta Domeniconi:
Approximating Multi-Dimensional Aggregate Range Queries over Real Attributes.
SIGMOD Conference 2000: 463-474
- Swarup Acharya, Phillip B. Gibbons, Viswanath Poosala:
Congressional Samples for Approximate Answering of Group-By Queries.
SIGMOD Conference 2000: 487-498
- Viswanath Poosala, Venkatesh Ganti, Yannis E. Ioannidis:
Approximate Query Answering using Histograms.
IEEE Data Eng. Bull. 22(4): 5-14(1999)
- Swarup Acharya, Phillip B. Gibbons, Viswanath Poosala:
Aqua: A Fast Decision Support Systems Using Approximate Query Answers.
VLDB 1999: 754-757
BibTeX
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