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Bloom Filters

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Encyclopedia of Database Systems

Synonyms

Hash filter

Definition

A Bloom filter is a simple, space-efficient randomized data structure based on hashing that represents a set in a way that allows membership queries to determine whether an element is a member of the set. False positives are possible, but not false negatives. In many applications, the space savings afforded by Bloom filters outweigh the drawbacks of a small probability for a false positive. Various extensions of Bloom filters can be used to handle alternative settings, such as when elements can be inserted and deleted from the set, and more complex queries, such as when each element has an associated function value that should be returned.

Historical Background

Burton Bloom introduced what is now called a Bloom filter in his 1970 paper [2], where he described the technique as an extension of hash-coding methods for applications where error-free methods require too much space and were not strictly necessary. The specific application he considered...

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Recommended Reading

  1. Babb E. Implementing a relational database by means of specialized hardware. ACM Trans. Database Syst., 4(1):1–29, 1979.

    Article  Google Scholar 

  2. Bloom B. Space/time tradeoffs in hash coding with allowable errors. Commun. ACM, 13(7):422–426, 1970.

    Article  MATH  Google Scholar 

  3. Bonomi F., Mitzenmacher M., Panigrahy R., Singh S., and Varghese G. Beyond Bloom filters: from approximate membership checks to approximate state machines. Comput. Commun. Rev., 36(4):315–326, 2006.

    Article  Google Scholar 

  4. Bratbergsengen K. Hashing methods and relational algebra operations. In Proc. 10th Int. Conf. on Very Large Data Bases, 1984, pp. 323–333.

    Google Scholar 

  5. Broder A. and Mitzenmacher M. Network applications of Bloom filters: a survey. Internet Math., (4):485–509, 2005.

    MathSciNet  Google Scholar 

  6. Chazelle B., Kilian J., Rubinfeld R., and Tal A. The Bloomier filter: an efficient data structure for static support lookup tables. In Proc. 15th Annual ACM-SIAM Symp. on Discrete Algorithms, 2004, pp. 30–39.

    Google Scholar 

  7. Cohen S. and Matias Y. Spectral Bloom filters. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 2003, pp. 241–252.

    Google Scholar 

  8. Cormode G. and Muthukrishnan S. An improved data stream summary: the count-min sketch and its applications. J. Algorithms, 55(1):58–75, 2003.

    Article  MathSciNet  Google Scholar 

  9. Fan L., Cao P., Almeida J., and Broder A.Z. Summary cache: a scalable wide-area Web cache sharing protocol. IEEE/ACM Trans. Network., 8(3):281–293, 2000.

    Article  Google Scholar 

  10. Gremilion L.L. Designing a Bloom filter for differential file access. Commun. ACM, 25:600–604, 1982.

    Article  Google Scholar 

  11. Mackett L.F. and Lohman G.M. R* optimizer validation and performance evaluation for distributed queries. In Proc. 27th Int. Conf. on Very Large Data Bases, 1986, pp. 149–159.

    Google Scholar 

  12. McIlroy M.D. Development of a spelling list. IEEE Trans. Commun., 30(1):91–99, January 1982.

    Article  Google Scholar 

  13. Mullin J.K. and Margoliash D.J. A tale of three spelling checkers. Software Pract. Exp., 20(6):625–630, June 1990.

    Article  Google Scholar 

  14. Mullin J.K. Estimating the size of a relational join. Inf. Syst., 18(3):189–196, 1993.

    Article  Google Scholar 

  15. Mitzenmacher M. Compressed Bloom filters. IEEE/ACM Trans. Network., 10(5):604–612, October 2002.

    Article  Google Scholar 

  16. Spafford E.H. Opus: preventing weak password choices. Comp. Sec., 11:273–278, 1992.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Harvard University, Boston, MA, USA

    Michael Mitzenmacher

Authors
  1. Michael Mitzenmacher
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Editor information

Editors and Affiliations

  1. College of Computing, Georgia Institute of Technology, 266 Ferst Drive, 30332-0765, Atlanta, GA, USA

    LING LIU (Professor) (Professor)

  2. Database Research Group David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada

    M. TAMER ÖZSU (Professor and Director, University Research Chair) (Professor and Director, University Research Chair)

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© 2009 Springer Science+Business Media, LLC

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Mitzenmacher, M. (2009). Bloom Filters. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_751

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