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Article

Query sampling in DB2 Universal Database

Authors:

Calisto ZuzarteAuthors Info & Claims

SIGMOD '04: Proceedings of the 2004 ACM SIGMOD international conference on Management of data

Pages 839 - 843

https://doi.org/10.1145/1007568.1007664

Published: 13 June 2004 Publication History

Abstract

Executing ad hoc queries against large databases can be prohibitively expensive. Exploratory analysis of data may not require exact answers to queries, however: results based on sampling the data are often satisfactory. Supporting sampling as a primitive SQL operator turns out to be difficult because sampling does not commute with many SQL operators.In this paper, we describe an implementation in IBM® DB2® Universal Database (UDB) of a sampling operator that commutes with some SQL operators. As a result, the query with the sampling operator always returns a random sample of the answers and in many cases runs faster than it would have without such an operator.

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Cited By

Rumbaugh DXie D(2023)Practical Dynamic Extension for Sampling IndexesProceedings of the ACM on Management of Data10.1145/36267441:4(1-26)Online publication date: 12-Dec-2023
https://dl.acm.org/doi/10.1145/3626744
Su XWang H(2021)MISS: finding optimal sample sizes for approximate analyticsDistributed and Parallel Databases10.1007/s10619-021-07376-5Online publication date: 21-Oct-2021
https://doi.org/10.1007/s10619-021-07376-5
Godfrey PGryz JLasek P(2016)Interactive Visualization of Large Data SetsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2016.255732428:8(2142-2157)Online publication date: 1-Aug-2016
https://doi.org/10.1109/TKDE.2016.2557324
Show More Cited By

Query sampling in DB2 Universal Database
1. Information systems
  1. Data management systems
    1. Database management system engines
    2. Query languages
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory

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Information

Published In

cover image ACM Conferences

SIGMOD '04: Proceedings of the 2004 ACM SIGMOD international conference on Management of data

June 2004

988 pages

ISBN:1581138598

DOI:10.1145/1007568

Conference Chairs:
Arnd Christian König
Microsoft Research
,
Stefan Dessloch
University of Kaiserslautern, Germany
,
General Chair:
Patrick Valduriez
INRIA, France
,
Program Chair:
Gerhard Weikum
University of the Saarland

Copyright © 2004 ACM.

Permission to make digital or hard copies of all or part 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, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOD: ACM Special Interest Group on Management of Data

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 June 2004

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SIGMOD/PODS04

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SIGMOD

SIGMOD/PODS04: International Conference on Management of Data and Symposium on Principles Database and Systems

June 13 - 18, 2004

Paris, France

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

Rumbaugh DXie D(2023)Practical Dynamic Extension for Sampling IndexesProceedings of the ACM on Management of Data10.1145/36267441:4(1-26)Online publication date: 12-Dec-2023
https://dl.acm.org/doi/10.1145/3626744
Su XWang H(2021)MISS: finding optimal sample sizes for approximate analyticsDistributed and Parallel Databases10.1007/s10619-021-07376-5Online publication date: 21-Oct-2021
https://doi.org/10.1007/s10619-021-07376-5
Godfrey PGryz JLasek P(2016)Interactive Visualization of Large Data SetsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2016.255732428:8(2142-2157)Online publication date: 1-Aug-2016
https://doi.org/10.1109/TKDE.2016.2557324
Nirkhiwale SDobra AJermaine C(2013)A sampling algebra for aggregate estimationProceedings of the VLDB Endowment10.14778/2556549.25565636:14(1798-1809)Online publication date: 1-Sep-2013
https://dl.acm.org/doi/10.14778/2556549.2556563
Köhler HZhou XSadiq SShu YTaylor KElmagarmid AAgrawal D(2010)Sampling dirty data for matching attributesProceedings of the 2010 ACM SIGMOD International Conference on Management of data10.1145/1807167.1807177(63-74)Online publication date: 6-Jun-2010
https://dl.acm.org/doi/10.1145/1807167.1807177
Buccafurri F(2009)Histograms for OLAP and Data-Stream QueriesEncyclopedia of Data Warehousing and Mining, Second Edition10.4018/978-1-60566-010-3.ch151(976-981)Online publication date: 2009
https://doi.org/10.4018/978-1-60566-010-3.ch151
Zuzarte CYu XYu CScheuermann PChaudhuri S(2006)Fast approximate computation of statistics on viewsProceedings of the 2006 ACM SIGMOD international conference on Management of data10.1145/1142473.1142564(724-724)Online publication date: 27-Jun-2006
https://dl.acm.org/doi/10.1145/1142473.1142564
Gemulla RBerthold HLehner W(2005)Hierarchisches gruppenbasiertes SamplingInformatik - Forschung und Entwicklung10.1007/s00450-004-0175-320:1-2(45-56)Online publication date: 15-Mar-2005
https://doi.org/10.1007/s00450-004-0175-3

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