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Choosing Best Hashing Strategies and Hash Functions

Authors: Deepak Garg; Mahima Singh;

Choosing Best Hashing Strategies and Hash Functions

Abstract

The paper gives the guideline to choose a best suitable hashing method hash function for a particular problem. After studying the various problem we find some criteria has been found to predict the best hash method and hash function for that problem. We present six suitable various classes of hash functions in which most of the problems can find their solution. Paper discusses about hashing and its various components which are involved in hashing and states the need of using hashing for faster data retrieval. Hashing methods were used in many different applications of computer science discipline. These applications are spread from spell checker, database management applications, symbol tables generated by loaders, assembler, and compilers. There are various forms of hashing that are used in different problems of hashing like Dynamic hashing, Cryptographic hashing, Geometric hashing, Robust hashing, Bloom hash, String hashing. At the end we conclude which type of hash function is suitable for which kind of problem.

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    Top 10%
    influence
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citations
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!
34
Top 10%
Top 10%
Average
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