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Journal of Computer and System Sciences
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Journal of Computer and System Sciences
Article . 1979
License: Elsevier Non-Commercial
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Article . 1979 . Peer-reviewed
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Universal classes of hash functions

Authors: J. Lawrence Carter; Mark N. Wegman;

Universal classes of hash functions

Abstract

AbstractThis paper gives an input independent average linear time algorithm for storage and retrieval on keys. The algorithm makes a random choice of hash function from a suitable class of hash functions. Given any sequence of inputs the expected time (averaging over all functions in the class) to store and retrieve elements is linear in the length of the sequence. The number of references to the data base required by the algorithm for any input is extremely close to the theoretical minimum for any possible hash function with randomly distributed inputs. We present three suitable classes of hash functions which also can be evaluated rapidly. The ability to analyze the cost of storage and retrieval without worrying about the distribution of the input allows as corollaries improvements on the bounds of several algorithms.

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Keywords

Data structures, Computer Networks and Communications, Applied Mathematics, universal classes of hash functions, Theoretical Computer Science, storage and retrieval on keys, Discrete mathematics in relation to computer science, Information storage and retrieval of data, Computational Theory and Mathematics, data base, cost

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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!
2K
Top 0.1%
Top 0.01%
Top 10%
hybrid