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Bloomier Filters: A Second Look

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5193))

Abstract

A Bloom filter is a space efficient structure for storing static sets, where the space efficiency is gained at the expense of a small probability of false-positives. A Bloomier filter generalizes a Bloom filter to compactly store a function with a static support. In this article we give a simple construction of a Bloomier filter. The construction is linear in space and requires constant time to evaluate. The creation of our Bloomier filter takes linear time which is faster than the existing construction. We show how one can improve the space utilization further at the cost of increasing the time for creating the data structure.

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References

  1. Bloom, B.: Space/time tradeoffs in hash coding with allowable errors. Comm. of the ACM 13, 422–426 (1970)

    Article  MATH  Google Scholar 

  2. Bollobás, B.: The evolution of random graphs. Trans. Amer. Math. Soc. 286, 257–274 (1984)

    Article  MATH  MathSciNet  Google Scholar 

  3. Broder, A., Mitzenmacher, M.: Network applications of Bloom filters: a survey, Allerton (2002)

    Google Scholar 

  4. Byers, J., Considine, J., Mitzenmacher, M.: Informed content delivery over adaptive overlay networks. In: Proc. ACM SIGCOMM 2002. Comp. Communication Review, vol. 34(4), pp. 47–60 (2002)

    Google Scholar 

  5. Calkin, N.J.: Dependent sets of constant weight binary vectors. Combinatorics, Probability and Computing 6(3), 263–271 (1997)

    Article  MATH  MathSciNet  Google Scholar 

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

    Google Scholar 

  7. Charles, D., Chellapilla, K.: Bloomier Filters: A second look (extended version) (2008) arXiv:0807.0928

    Google Scholar 

  8. Cohen, S., Matias, Y.: Spectral Bloom filters. In: ACM SIGMOD (2003)

    Google Scholar 

  9. Czech, Z., Havas, G., Majewski, B.S.: An optimal algorithm for generating minimal perfect hash functions. Information Processing Letters 43(5), 257–264 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  10. Czech, Z., Havas, G., Majewski, B.S., Wormald, N.C.: Graphs, hypergraphs and hashing. In: van Leeuwen, J. (ed.) WG 1993. LNCS, vol. 790, pp. 153–165. Springer, Heidelberg (1994)

    Google Scholar 

  11. Dietzfelbinger, M., Pagh, R.: Succinct Data Structures for Retrieval and Approximate Membership. In: ICALP (to appear, 2008)

    Google Scholar 

  12. Erdős, P., Rényi, A.: On the evolution of random graphs. Publ. Math. Inst. Hungar. Acad. Sci. 5, 17–61 (1960)

    Google Scholar 

  13. Fan, L., Cao, P., Almeida, J., Broder, A.: Summary cache: a scalable wide-area web cache sharing protocol. IEEE/ACM Transactions on Networking 8, 281–293 (2000)

    Article  Google Scholar 

  14. Fang, M., Shivakumar, N., Garcia-Molina, H., Motwani, R., Ullman, J.: Computing iceberg queries efficiently. In: Proc. 24th Int. Conf. on VLDB, pp. 299–310 (1998)

    Google Scholar 

  15. Geller, D., Kra, I., Popescu, S., Simanca, S.: On circulant matrices (manuscript), http://www.math.sunysb.edu/~sorin

  16. Gremillion, L.L.: Designing a Bloom filter for differential file access. Comm. of the ACM 25, 600–604 (1982)

    Article  Google Scholar 

  17. Mitzenmacher, M.: Compressed Bloom filters. IEEE Transactions on Networking 10 (2002)

    Google Scholar 

  18. Rhea, S.C., Kubiatowicz, J.: Proabilistic location and routing. In: Proceedings of INFOCOMM (2002)

    Google Scholar 

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Dan Halperin Kurt Mehlhorn

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© 2008 Springer-Verlag Berlin Heidelberg

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Charles, D., Chellapilla, K. (2008). Bloomier Filters: A Second Look. In: Halperin, D., Mehlhorn, K. (eds) Algorithms - ESA 2008. ESA 2008. Lecture Notes in Computer Science, vol 5193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87744-8_22

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  • DOI: https://doi.org/10.1007/978-3-540-87744-8_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87743-1

  • Online ISBN: 978-3-540-87744-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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