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Minimal perfect hashing for large sets of data

  • Data And Software Engineering
  • Conference paper
  • First Online:
Advances in Computing and Information — ICCI '90 (ICCI 1990)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 468))

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Abstract

We present and analyze a method that permits the efficient application of minimal perfect hashing functions to much larger sets of data than previously possible. The method employs a probabilistic algorithm to obtain a separation of legal key values into disjoint subsets. The cardinality of each subset is small enough so that it can be efficiently minimally perfectly hashed. In the retrieval phase, the subset to which an input key belongs is determined and the hashing function for that subset is applied. The time complexity of locating the data associated with a given input key value is O(LogLogN). The space complexity of the algorithm is O(N LogLogN). Construction time for the necessary data structures is O(N 2 LogLogN).

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References

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Cincinnati, Cincinnati, Ohio

    Vincent G. Winters

Authors
  1. Vincent G. Winters
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Editor information

S. G. Akl F. Fiala W. W. Koczkodaj

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© 1991 Springer-Verlag Berlin Heidelberg

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Winters, V.G. (1991). Minimal perfect hashing for large sets of data. In: Akl, S.G., Fiala, F., Koczkodaj, W.W. (eds) Advances in Computing and Information — ICCI '90. ICCI 1990. Lecture Notes in Computer Science, vol 468. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-53504-7_85

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  • DOI: https://doi.org/10.1007/3-540-53504-7_85

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-53504-1

  • Online ISBN: 978-3-540-46677-2

  • eBook Packages: Springer Book Archive

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