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Statistical estimators for aggregate relational algebra queries

Editor: Gio Wiederhold Authors:

Wen-Chi Hou,

Gultekin OzsoyogluAuthors Info & Claims

ACM Transactions on Database Systems (TODS), Volume 16, Issue 4

Pages 600 - 654

https://doi.org/10.1145/115302.115300

Published: 01 December 1991 Publication History

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Rong KLu YBailis PKandula SLevis P(2020)Approximate partition selection for big-data workloads using summary statisticsProceedings of the VLDB Endowment10.14778/3407790.340784813:12(2606-2619)Online publication date: 14-Sep-2020
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Hacigumus HChi YWu WZhu STatemura JNaughton J(2013)Predicting query execution timeProceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013)10.1109/ICDE.2013.6544899(1081-1092)Online publication date: 8-Apr-2013
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Statistical estimators for aggregate relational algebra queries

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Reviews

Reviewer: Pericles Loucopoulos

The authors describe the derivation of statistical estimators for COUNT(E) queries on a relational database and present algorithms for evaluating these estimators. A brief description of the relational model and the background statistical theory is provided, and this background is supplemented by a comprehensive set of references to related work. The application of statistical theory to the production of COUNT(E) estimators is explained in a thorough but readable form, and the algorithms provide a concise summary of the results. The paper proposes several enhancements to the estimators, and concentrates on cluster-sampling–based estimators in its appraisal of their performance on arbitrary relational databases. The experimental results are described in some detail, but provide useful insights into the type of estimator most appropriate to a variety of situations. The paper is long, but the length is justified by the detailed coverage of the subject area. It provides a valuable contribution to the field of statistical estimators.

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cover image ACM Transactions on Database Systems

ACM Transactions on Database Systems Volume 16, Issue 4

Dec. 1991

176 pages

ISSN:0362-5915

EISSN:1557-4644

DOI:10.1145/115302

Editor:
Gio Wiederhold
Stanford Univ., Stanford, CA

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 1991

Published in TODS Volume 16, Issue 4

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Rong KLu YBailis PKandula SLevis P(2020)Approximate partition selection for big-data workloads using summary statisticsProceedings of the VLDB Endowment10.14778/3407790.340784813:12(2606-2619)Online publication date: 14-Sep-2020
https://dl.acm.org/doi/10.14778/3407790.3407848
Ibrahim IAlbarrak ALi X(2017)Constrained recommendations for query visualizationsKnowledge and Information Systems10.1007/s10115-016-1001-551:2(499-529)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1007/s10115-016-1001-5
Hacigumus HChi YWu WZhu STatemura JNaughton J(2013)Predicting query execution timeProceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013)10.1109/ICDE.2013.6544899(1081-1092)Online publication date: 8-Apr-2013
https://dl.acm.org/doi/10.1109/ICDE.2013.6544899
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Bunge JFitzpatrick M(2012)Estimating the Number of Species: A ReviewJournal of the American Statistical Association10.1080/01621459.1993.1059433088:421(364-373)Online publication date: 20-Dec-2012
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Dobra AJermaine CRusu FXu F(2009)Turbo-charging estimate convergence in DBOProceedings of the VLDB Endowment10.14778/1687627.16876752:1(419-430)Online publication date: 1-Aug-2009
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Joshi SJermaine C(2009)Sampling-based estimators for subset-based queriesThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-008-0095-018:1(181-202)Online publication date: 1-Jan-2009
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Weiss Ferreira Chaves LBuchmann EBöhm KLi YLiu BSarawagi S(2008)TagmarkProceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/1401890.1402009(999-1007)Online publication date: 24-Aug-2008
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Banaei-Kashani FShahabi C(2008)Fixed-Precision Approximate Continuous Aggregate Queries in Peer-to-Peer DatabasesProceedings of the 2008 IEEE 24th International Conference on Data Engineering10.1109/ICDE.2008.4497578(1427-1429)Online publication date: 7-Apr-2008
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Processing time-constrained aggregate queries in CASE-DB

Sampling-based estimators for subset-based queries

An Extended Relational Algebra on Abstract Objects for Summarizing Answers to Queries

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