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An optimal Bloom filter replacement

Published: 23 January 2005 Publication History

Abstract

This paper considers space-efficient data structures for storing an approximation S' to a set S such that SS' and any element not in S belongs to S' with probability at most ∈. The Bloom filter data structure, solving this problem, has found widespread use. Our main result is a new RAM data structure that improves Bloom filters in several ways:• The time for looking up an element in S' is O(1), independent of ∈.• The space usage is within a lower order term of the lower bound.• The data structure uses explicit hash function families.• The data structure supports insertions and deletions on S in amortized expected constant time.The main technical ingredient is a succinct representation of dynamic multisets. We also consider three recent generalizations of Bloom filters.

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  • (2024)Adaptive Quotient FiltersProceedings of the ACM on Management of Data10.1145/36771282:4(1-28)Online publication date: 30-Sep-2024
  • (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
  • (2023)Locality sensitive hashing in fourier frequency domain for soft set containment searchProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668581(56352-56383)Online publication date: 10-Dec-2023
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cover image ACM Conferences
SODA '05: Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
January 2005
1205 pages
ISBN:0898715857

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Society for Industrial and Applied Mathematics

United States

Publication History

Published: 23 January 2005

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Overall Acceptance Rate 411 of 1,322 submissions, 31%

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View all
  • (2024)Adaptive Quotient FiltersProceedings of the ACM on Management of Data10.1145/36771282:4(1-28)Online publication date: 30-Sep-2024
  • (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
  • (2023)Locality sensitive hashing in fourier frequency domain for soft set containment searchProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668581(56352-56383)Online publication date: 10-Dec-2023
  • (2023)ChainedFilter: Combining Membership Filters by Chain RuleProceedings of the ACM on Management of Data10.1145/36267211:4(1-27)Online publication date: 12-Dec-2023
  • (2023)High-Performance Filters for GPUsProceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming10.1145/3572848.3577507(160-173)Online publication date: 25-Feb-2023
  • (2022)A Theoretical Perspective on Hyperdimensional ComputingJournal of Artificial Intelligence Research10.1613/jair.1.1266472(215-249)Online publication date: 4-Jan-2022
  • (2022)New query optimization techniques in the Spark engine of Azure synapseProceedings of the VLDB Endowment10.14778/3503585.350360115:4(936-948)Online publication date: 14-Apr-2022
  • (2022)On the optimal time/space tradeoff for hash tablesProceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing10.1145/3519935.3519969(1284-1297)Online publication date: 9-Jun-2022
  • (2021)Chucky: A Succinct Cuckoo Filter for LSM-TreeProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3457273(365-378)Online publication date: 9-Jun-2021
  • (2021)Vector Quotient FiltersProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3452841(1386-1399)Online publication date: 9-Jun-2021
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