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Online aggregation

Authors:

Joseph M. Hellerstein,

Helen J. WangAuthors Info & Claims

SIGMOD '97: Proceedings of the 1997 ACM SIGMOD international conference on Management of data

Pages 171 - 182

https://doi.org/10.1145/253260.253291

Published: 01 June 1997 Publication History

Abstract

Aggregation in traditional database systems is performed in batch mode: a query is submitted, the system processes a large volume of data over a long period of time, and, eventually, the final answer is returned. This archaic approach is frustrating to users and has been abandoned in most other areas of computing. In this paper we propose a new online aggregation interface that permits users to both observe the progress of their aggregation queries and control execution on the fly. After outlining usability and performance requirements for a system supporting online aggregation, we present a suite of techniques that extend a database system to meet these requirements. These include methods for returning the output in random order, for providing control over the relative rate at which different aggregates are computed, and for computing running confidence intervals. Finally, we report on an initial implementation of online aggregation in POSTGRES.

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Cited By

Fallahian MDorodchi MKreth K(2024)GAN-Based Tabular Data Generator for Constructing Synopsis in Approximate Query Processing: Challenges and SolutionsMachine Learning and Knowledge Extraction10.3390/make60100106:1(171-198)Online publication date: 16-Jan-2024
https://doi.org/10.3390/make6010010
Chang CLo EYe C(2024)Biathlon: Harnessing Model Resilience for Accelerating ML Inference PipelinesProceedings of the VLDB Endowment10.14778/3675034.367505217:10(2631-2640)Online publication date: 1-Jun-2024
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Index Terms

Online aggregation

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Information & Contributors

Information

Published In

cover image ACM Conferences

SIGMOD '97: Proceedings of the 1997 ACM SIGMOD international conference on Management of data

June 1997

594 pages

ISBN:0897919114

DOI:10.1145/253260

Editors:
Joan M. Peckman
Univ. of Rhode Island, Kingston
,
Sudha Ram
Univ. of Arizona, Tucson
,
Michael Franklin

ACM SIGMOD Record Volume 26, Issue 2
June 1997
583 pages
ISSN:0163-5808
DOI:10.1145/253262
Chairman:
Sudha Ram
Univ. of Arizona, Tucson
,
Editor:
Joan M. Peckham
Univ. of Rhode Island, Kingston
Issue’s Table of Contents

Copyright © 1997 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: 01 June 1997

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SIGMOD/PODS97

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SIGMOD

SIGMOD/PODS97: Joint ACM SIGMOD International Conference on Management of Data and ACM Symposium on Principles of Database Systems

May 11 - 15, 1997

Arizona, Tucson, USA

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SIGMOD '97 Paper Acceptance Rate 42 of 202 submissions, 21%;

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

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Cited By

Fallahian MDorodchi MKreth K(2024)GAN-Based Tabular Data Generator for Constructing Synopsis in Approximate Query Processing: Challenges and SolutionsMachine Learning and Knowledge Extraction10.3390/make60100106:1(171-198)Online publication date: 16-Jan-2024
https://doi.org/10.3390/make6010010
Chang CLo EYe C(2024)Biathlon: Harnessing Model Resilience for Accelerating ML Inference PipelinesProceedings of the VLDB Endowment10.14778/3675034.367505217:10(2631-2640)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.14778/3675034.3675052
Hu PMotik B(2024)Accurate Sampling-Based Cardinality Estimation for Complex Graph QueriesACM Transactions on Database Systems10.1145/368920949:3(1-46)Online publication date: 17-Sep-2024
https://dl.acm.org/doi/10.1145/3689209
Jawarneh IBellavista PCorradi AFoschini LMontanari R(2024)SpatialSSJP: QoS-Aware Adaptive Approximate Stream-Static Spatial Join ProcessorIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.333066935:1(73-88)Online publication date: Jan-2024
https://doi.org/10.1109/TPDS.2023.3330669
Wu SShi MZhang DZhao JYuan GChen G(2024)When Quantum Computing Meets Database: A Hybrid Sampling Framework for Approximate Query ProcessingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.348027836:12(9532-9546)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3480278
Zhang HJing YHe ZZhang KWang X(2024)Learning-Based Sample Tuning for Approximate Query Processing in Interactive Data ExplorationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.334145136:11(6532-6546)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2023.3341451
Chang ZLi FShen Y(2024)Generalized Measure-Biased Sampling and Priority SamplingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.334067336:11(6251-6265)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2023.3340673
Konomi SGao LMushi DRen B(2024)DCLA: Towards Distributed Cooperative Learning Analytics for Developing CommunitiesHCI International 2024 – Late Breaking Papers10.1007/978-3-031-76815-6_8(94-106)Online publication date: 11-Dec-2024
https://doi.org/10.1007/978-3-031-76815-6_8
Ye SXu XWang YFu T(2023)Efficient Complex Aggregate Queries with Accuracy Guarantee Based on Execution Cost Model over Knowledge GraphsMathematics10.3390/math1118390811:18(3908)Online publication date: 14-Sep-2023
https://doi.org/10.3390/math11183908
Siddiqui TNarasayya VDumitru MChaudhuri S(2023)Cache-Efficient Top-k Aggregation over High Cardinality Large DatasetsProceedings of the VLDB Endowment10.14778/3636218.363622217:4(644-656)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.14778/3636218.3636222
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