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The Aqua approximate query answering system

Authors:

Swarup Acharya,

Phillip B. Gibbons,

Viswanath Poosala, and

Sridhar RamaswamyAuthors Info & Claims

SIGMOD '99: Proceedings of the 1999 ACM SIGMOD international conference on Management of data

June 1999

Pages 574 - 576

https://doi.org/10.1145/304182.304581

Published: 01 June 1999 Publication History

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Abstract

Aqua is a system for providing fast, approximate answers to aggregate queries, which are very common in OLAP applications. It has been designed to run on top of any commercial relational DBMS. Aqua precomputes synopses (special statistical summaries) of the original data and stores them in the DBMS. It provides approximate answers along with quality guarantees by rewriting the queries to run on these synopses. Finally, Aqua keeps the synopses up-to-date as the database changes, using fast incremental maintenance techniques.

References

[1]

S. Acharya, P. B. Gibbons, and V. Poosala. Congressional samples for approximate answering of group-by queries. Technical report, Bell Laboratories, Murray Hill, New Jersey, March 1999.

Google Scholar

[2]

S. Acharya, P. B. Gibbons, V. Poosala, and S. Ramaswamy. Join synopses for approximate query answering. In Proc. A CM SIGMOD International Conf. on Management of Data, June 1999.

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Google Scholar

[3]

P.B. Gibbons, Y. Matias, and V. Poosala. Fast incremental maintenance of approximate histograms. In Proc. 23rd International Conf. on Very Large Data Bases, pages 466-475, August 1997.

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Google Scholar

[4]

V. Poosala, Y. E. Ioannidis, P. J. Haas, and E. J. Shekita. Improved histograms for selectivity estimation of range predicates. In Proc. A CM SIGMOD International Conf. on Management of Data, pages 294-305, June 1996.

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Cited By

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Laouir AImine AHong JPark J(2024)DiApprox: Differential Privacy-based Online Range Queries Approximation for Multidimensional DataProceedings of the 39th ACM/SIGAPP Symposium on Applied Computing10.1145/3605098.3636070(337-344)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3605098.3636070
Negi PWu ZKipf ATatbul NMarcus RMadden SKraska TAlizadeh M(2023)Robust Query Driven Cardinality Estimation under Changing WorkloadsProceedings of the VLDB Endowment10.14778/3583140.358316416:6(1520-1533)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.14778/3583140.3583164
Burtsev VWilhelm MDrewes AGurumurthy BBroneske DPionteck TSaake G(2023)FPGA-Integrated Bag of Little Bootstraps Accelerator for Approximate Database Query ProcessingApplied Reconfigurable Computing. Architectures, Tools, and Applications10.1007/978-3-031-42921-7_8(115-130)Online publication date: 27-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-42921-7_8
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Index Terms

The Aqua approximate query answering system
1. Information systems
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Database query processing and optimization (theory)

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The Aqua approximate query answering system

Aqua is a system for providing fast, approximate answers to aggregate queries, which are very common in OLAP applications. It has been designed to run on top of any commercial relational DBMS. Aqua precomputes synopses (special statistical summaries) of ...
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Join synopses for approximate query answering
SIGMOD '99: Proceedings of the 1999 ACM SIGMOD international conference on Management of data

In large data warehousing environments, it is often advantageous to provide fast, approximate answers to complex aggregate queries based on statistical summaries of the full data. In this paper, we demonstrate the difficulty of providing good approximate ...
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Join synopses for approximate query answering

In large data warehousing environments, it is often advantageous to provide fast, approximate answers to complex aggregate queries based on statistical summaries of the full data. In this paper, we demonstrate the difficulty of providing good approximate ...
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Information & Contributors

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Published In

SIGMOD '99: Proceedings of the 1999 ACM SIGMOD international conference on Management of data

June 1999

604 pages

ISBN:1581130848

DOI:10.1145/304182

Chairmen:
Susan B. Davidson
Univ. of Pennsylvania, Philidelphia
,
Christos Faloutsos
Carnegie Mellon Univ., Pittsburgh

ACM SIGMOD Record Volume 28, Issue 2
June 1999
599 pages
ISSN:0163-5808
DOI:10.1145/304181
Chairmen:
Susan Davidson
Univ. of Pennsylvania
,
Christos Faloutsos
Carnegie Mellon Univ.
Issue’s Table of Contents

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 1999

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SIGMOD/PODS99

Sponsor:

SIGMOD/PODS99: International Conference on Management of Data and Symposium on Principles of Database Systems

May 31 - June 3, 1999

Pennsylvania, Philadelphia, USA

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

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Laouir AImine AHong JPark J(2024)DiApprox: Differential Privacy-based Online Range Queries Approximation for Multidimensional DataProceedings of the 39th ACM/SIGAPP Symposium on Applied Computing10.1145/3605098.3636070(337-344)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3605098.3636070
Negi PWu ZKipf ATatbul NMarcus RMadden SKraska TAlizadeh M(2023)Robust Query Driven Cardinality Estimation under Changing WorkloadsProceedings of the VLDB Endowment10.14778/3583140.358316416:6(1520-1533)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.14778/3583140.3583164
Burtsev VWilhelm MDrewes AGurumurthy BBroneske DPionteck TSaake G(2023)FPGA-Integrated Bag of Little Bootstraps Accelerator for Approximate Database Query ProcessingApplied Reconfigurable Computing. Architectures, Tools, and Applications10.1007/978-3-031-42921-7_8(115-130)Online publication date: 27-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-42921-7_8
Shankar SParameswaran A(2022)Towards Observability for Production Machine Learning PipelinesProceedings of the VLDB Endowment10.14778/3565838.356585315:13(4015-4022)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.14778/3565838.3565853
Manousis ACheng ZBasat RLiu ZSekar V(2022)Enabling efficient and general subpopulation analytics in multidimensional data streamsProceedings of the VLDB Endowment10.14778/3551793.355186715:11(3249-3262)Online publication date: 1-Jul-2022
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Zhao KYu JHe ZLi RZhang HIves ZBonifati AEl Abbadi A(2022)Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian ProcessProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3526156(973-987)Online publication date: 10-Jun-2022
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Shang ZZgraggen EBuratti BEichmann PKarimeddiny NMeyer CRunnels WKraska T(2021)DavosProceedings of the VLDB Endowment10.14778/3476311.347637014:12(2893-2905)Online publication date: 1-Jul-2021
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Yan SDing BGuo WZhou JWei ZJiang XXu S(2021)FlashPProceedings of the VLDB Endowment10.14778/3446095.344609614:5(721-729)Online publication date: 1-Jan-2021
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Yan JGu ZRzeszotarski JKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Tessera: Discretizing Data Analysis Workflows on a Task LevelProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445728(1-15)Online publication date: 6-May-2021
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Battle LScheidegger C(2021)A Structured Review of Data Management Technology for Interactive Visualization and AnalysisIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.302889127:2(1128-1138)Online publication date: Feb-2021
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The Aqua approximate query answering system

Join synopses for approximate query answering

Join synopses for approximate query answering