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Part of book or chapter of book
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https://doi.org/10.1007/114271...
Part of book or chapter of book . 2005 . Peer-reviewed
License: Springer TDM
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DBLP
Conference object . 2017
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A Practical Minimal Perfect Hashing Method

Authors: Fabiano C. Botelho; Yoshiharu Kohayakawa; Nivio Ziviani;

A Practical Minimal Perfect Hashing Method

Abstract

We propose a novel algorithm based on random graphs to construct minimal perfect hash functions h. For a set of n keys, our algorithm outputs h in expected time O(n). The evaluation of h(x) requires two memory accesses for any key x and the description of h takes up 1.15n words. This improves the space requirement to 55% of a previous minimal perfect hashing scheme due to Czech, Havas and Majewski. A simple heuristic further reduces the space requirement to 0.93n words, at the expense of a slightly worse constant in the time complexity. Large scale experimental results are presented.

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    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    15
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
15
Average
Top 10%
Top 10%