research-article

Understanding bloom filter intersection for lazy address-set disambiguation

Authors:

Mark C. Jeffrey,

J. Gregory SteffanAuthors Info & Claims

SPAA '11: Proceedings of the twenty-third annual ACM symposium on Parallelism in algorithms and architectures

Pages 345 - 354

https://doi.org/10.1145/1989493.1989551

Published: 04 June 2011 Publication History

Abstract

A Bloom filter is a probabilistic bit-array-based set representation that has recently been applied to address-set disambiguation in systems that ease the burden of parallel programming. However, many of these systems intersect the Bloom filter bit-arrays to approximate address-set intersection and decide set disjointness. This is in contrast with the conventional and well-studied approach of making individual membership queries into the Bloom filter. In this paper we present much-needed probabilistic models for the unconventional application of testing set disjointness using Bloom filters. Consequently, we demonstrate that intersecting Bloom filters requires substantially larger bit-arrays to provide the same probability of false set-overlap as querying into the bit-array. For when intersection is unavoidable, we prove that partitioned Bloom filters require less space than unpartitioned. Finally, we show that for Bloom filters with a single hash function, surprisingly, intersection and querying share the same probability of false set-overlap.

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Cited By

Almeida P(2023)A Case for Partitioned Bloom FiltersIEEE Transactions on Computers10.1109/TC.2022.321899572:6(1681-1691)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1109/TC.2022.3218995
Klingler FCohen RSommer CDressler F(2019)Bloom HoppingIEEE Transactions on Mobile Computing10.1109/TMC.2018.284012318:3(534-545)Online publication date: 17-Jul-2019
https://dl.acm.org/doi/10.1109/TMC.2018.2840123
Sengupta NBagchi ABedathur SRamanath M(2018)Sampling and Reconstruction Using Bloom FiltersIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.278580330:7(1324-1337)Online publication date: 1-Jul-2018
https://doi.org/10.1109/TKDE.2017.2785803
Show More Cited By

Index Terms

Understanding bloom filter intersection for lazy address-set disambiguation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
2. Theory of computation
  1. Randomness, geometry and discrete structures

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cover image ACM Conferences

SPAA '11: Proceedings of the twenty-third annual ACM symposium on Parallelism in algorithms and architectures

June 2011

404 pages

ISBN:9781450307437

DOI:10.1145/1989493

Co-chairs:
Friedhelm Meyer auf der Heide
University of Paderborn, Germany
,
Rajmohan Rajaraman
Northeastern University, USA

Copyright © 2011 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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Published: 04 June 2011

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June 4 - 6, 2011

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Cited By

Almeida P(2023)A Case for Partitioned Bloom FiltersIEEE Transactions on Computers10.1109/TC.2022.321899572:6(1681-1691)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1109/TC.2022.3218995
Klingler FCohen RSommer CDressler F(2019)Bloom HoppingIEEE Transactions on Mobile Computing10.1109/TMC.2018.284012318:3(534-545)Online publication date: 17-Jul-2019
https://dl.acm.org/doi/10.1109/TMC.2018.2840123
Sengupta NBagchi ABedathur SRamanath M(2018)Sampling and Reconstruction Using Bloom FiltersIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.278580330:7(1324-1337)Online publication date: 1-Jul-2018
https://doi.org/10.1109/TKDE.2017.2785803
Fu YBiersack E(2016)False-Positive Probability and Compression Optimization for Tree-Structured Bloom FiltersACM Transactions on Modeling and Performance Evaluation of Computing Systems10.1145/29403241:4(1-39)Online publication date: 21-Sep-2016
https://dl.acm.org/doi/10.1145/2940324
Cheng HMa H(2013)Membership classification using Integer Bloom Filter2013 IEEE/ACIS 12th International Conference on Computer and Information Science (ICIS)10.1109/ICIS.2013.6607871(385-390)Online publication date: Jun-2013
https://doi.org/10.1109/ICIS.2013.6607871
Foley SNavarro-Arribas G(2013)A Bloom Filter Based Model for Decentralized AuthorizationInternational Journal of Intelligent Systems10.1002/int.2159328:6(565-582)Online publication date: 26-Mar-2013
https://doi.org/10.1002/int.21593

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