research-article

Persistent Data Sketching

Authors:

Ji-Rong WenAuthors Info & Claims

SIGMOD '15: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

Pages 795 - 810

https://doi.org/10.1145/2723372.2749443

Published: 27 May 2015 Publication History

Abstract

A persistent data structure, also known as a multiversion data structure in the database literature, is a data structure that preserves all its previous versions as it is updated over time. Every update (inserting, deleting, or changing a data record) to the data structure creates a new version, while all the versions are kept in the data structure so that any previous version can still be queried.

Persistent data structures aim at recording all versions accurately, which results in a space requirement that is at least linear to the number of updates. In many of today's big data applications, in particular for high-speed streaming data, the volume and velocity of the data are so high that we cannot afford to store everything. Therefore, streaming algorithms have received a lot of attention in the research community, which use only sublinear space by sacrificing slightly on accuracy.

All streaming algorithms work by maintaining a small data structure in memory, which is usually called a em sketch, summary, or synopsis. The sketch is updated upon the arrival of every element in the stream, thus is ephemeral, meaning that it can only answer queries about the current status of the stream. In this paper, we aim at designing persistent sketches, thereby giving streaming algorithms the ability to answer queries about the stream at any prior time.

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Index Terms

Persistent Data Sketching
1. Theory of computation
  1. Design and analysis of algorithms

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Published In

cover image ACM Conferences

SIGMOD '15: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

May 2015

2110 pages

ISBN:9781450327589

DOI:10.1145/2723372

General Chair:
Timos Sellis
RMIT University, Australia
,
Program Chairs:
Susan B. Davidson
University of Pennsylvania, USA
,
Zack Ives
University of Pennsylvania, USA

Copyright © 2015 ACM.

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SIGMOD: ACM Special Interest Group on Management of Data

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New York, NY, United States

Publication History

Published: 27 May 2015

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Research-article

Funding Sources

HKRGC
Microsoft grant
Research Funds of Renmin University of China
973 Program of China
Fundamental Research Funds for the Cen- tral Universities
National Key Ba- sic Research Program (973 Program) of China

Conference

SIGMOD/PODS'15

Sponsor:

SIGMOD

SIGMOD/PODS'15: International Conference on Management of Data

May 31 - June 4, 2015

Victoria, Melbourne, Australia

Acceptance Rates

SIGMOD '15 Paper Acceptance Rate 106 of 415 submissions, 26%;

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

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Cited By

Zheng HHuang CHan XZheng JWang XTian CDou WChen GSekar VYu MSeneviratne AVeitch D(2024)μMon: Empowering Microsecond-level Network Monitoring with WaveletsProceedings of the ACM SIGCOMM 2024 Conference10.1145/3651890.3672236(274-290)Online publication date: 4-Aug-2024
https://dl.acm.org/doi/10.1145/3651890.3672236
Wu YJiang SXu YDong SYang KChen PYang T(2024)Unbiased Real-Time Traffic SketchingIEEE Transactions on Network Science and Engineering10.1109/TNSE.2023.328400411:3(2371-2383)Online publication date: May-2024
https://doi.org/10.1109/TNSE.2023.3284004
Liu JBasat RDe Wardt LDai HChen G(2024)DISCO: A Dynamically Configurable Sketch Framework in Skewed Data Streams2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00365(4801-4814)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00365
Liu ZDong FLiu CDeng XYang TZhao YLi JCui BZhang G(2024)WavingSketch: an unbiased and generic sketch for finding top-k items in data streamsThe VLDB Journal10.1007/s00778-024-00869-633:5(1697-1722)Online publication date: 29-Jul-2024
https://doi.org/10.1007/s00778-024-00869-6
Zhao YHan WZhong ZZhang YYang TCui B(2023)Double-Anonymous Sketch: Achieving Top-K-fairness for Finding Global Top-K Frequent ItemsProceedings of the ACM on Management of Data10.1145/35889331:1(1-26)Online publication date: 30-May-2023
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Liu ZZhang YZhu YZhang RYang TXie KWang SLi TCui B(2023)TreeSensing: Linearly Compressing Sketches with FlexibilityProceedings of the ACM on Management of Data10.1145/35889101:1(1-28)Online publication date: 30-May-2023
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Wu YJiang SDong SZhong ZChen JHu YYang TUhlig SCui BSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)MicroscopeSketch: Accurate Sliding Estimation Using Adaptive ZoomingProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599432(2660-2671)Online publication date: 6-Aug-2023
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Miao RZhang YQu GYang KYang TCui BSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Hyper-USS: Answering Subset Query Over Multi-Attribute Data StreamProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599383(1698-1709)Online publication date: 6-Aug-2023
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Miao RZhong ZGuo JLi ZYang TCui B(2023)BurstSketch: Finding Bursts in Data StreamsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.322368635:11(11126-11140)Online publication date: 1-Nov-2023
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