research-article

Seesaw Counting Filter: An Efficient Guardian for Vulnerable Negative Keys During Dynamic Filtering

Authors:

Guihai ChenAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 2759 - 2767

https://doi.org/10.1145/3485447.3511996

Published: 25 April 2022 Publication History

Abstract

Bloom filter is an efficient data structure for filtering negative keys (keys not in a given set) with substantially small space. However, in real-world applications, there widely exist vulnerable negative keys, which will bring high costs if not being properly filtered, especially when positive keys are added/deleted dynamically. To address the problem, we propose SeeSaw Counting Filter (SSCF), which is innovated with encapsulating the vulnerable negative keys into a unified counter array named seesaw counter array, and dynamically modulating (or varying) the applied hash functions to guard the encapsulated keys from being misidentified. Moreover, we propose ada-SSCF to handle the scenarios where the vulnerable negative keys cannot be obtained in advance. We extensively evaluate our SSCF, which shows that SSCF outperforms the cutting-edge filters by 3 × on averages regarding accuracy while ensuring a low operation latency. All source codes are in [2].

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cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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National Natural Science Foundation of China
Key Research and Development Project of Jiangsu Province
Natural Science Foundation of Jiangsu Province for Distinguished Young Scholars

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

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Chen GHe ZLi MLuo S(2024)Oasis: An Optimal Disjoint Segmented Learned Range FilterProceedings of the VLDB Endowment10.14778/3659437.365944717:8(1911-1924)Online publication date: 31-May-2024
https://dl.acm.org/doi/10.14778/3659437.3659447
Wen RMcCoy HTench DTagliavini GBender MConway AFarach-Colton MJohnson RPandey P(2024)Adaptive Quotient FiltersProceedings of the ACM on Management of Data10.1145/36771282:4(1-28)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3677128
Liu JWang FMo DLuo S(2024)Structural Designs Meet Optimality: Exploring Optimized LSM-tree Structures in a Colossal Configuration SpaceProceedings of the ACM on Management of Data10.1145/36549782:3(1-26)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654978
Wang HGuo TYang JZhang H(2024)GRF: A Global Range Filter for LSM-Trees with Shape EncodingProceedings of the ACM on Management of Data10.1145/36549442:3(1-27)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654944
Pandey PFarach-Colton MDayan NZhang HBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)Beyond Bloom: A Tutorial on Future Feature-Rich FiltersCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3654681(636-644)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3654681
Wang HDai HChen SLi MGu RChai HZheng JChen ZLi SDeng XChen G(2024)Bamboo Filters: Make Resizing Smooth and AdaptiveIEEE/ACM Transactions on Networking10.1109/TNET.2024.340399732:5(3776-3791)Online publication date: Oct-2024
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Liu JDai HXia RLi MBasat RLi RGu RZheng JChen G(2024)A Generic Framework for Finding Special Quadratic Elements in Data StreamsIEEE/ACM Transactions on Networking10.1109/TNET.2024.339202932:4(3269-3284)Online publication date: Aug-2024
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Li MLuo WDai HChai HGu RWang XChen G(2024)The Reinforcement Cuckoo FilterIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621370(1940-1949)Online publication date: 20-May-2024
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