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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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Hashing practice: analysis of hashing and universal hashing

Authors: M. V. Ramakrishna;

Hashing practice: analysis of hashing and universal hashing

Abstract

Much of the literature on hashing deals with overflow handling (collision resolution) techniques and its analysis. What does all the analytical results mean in practice and how can they be achieved with practical files? This paper considers the problem of achieving analytical performance of hashing techniques in practice with reference to successful search lengths, unsuccessful search lengths and the expected worst case performance (expected length of the longest probe sequence). There has been no previous attempt to explicitly link the analytical results to performance of real life files. Also, the previously reported experimental results deal mostly with successful search lengths. We show why the well known division method performs “well” under a specific model of selecting the test file. We formulate and justify an hypothesis that by choosing functions from a particular class of hashing functions, the analytical performance can be obtained in practice on real life files. Experimental results presented strongly support our hypothesis. Several interesting problems arising are mentioned in conclusion.

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    influence
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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!
18
Top 10%
Top 10%
Average
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