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Engineering Minimal k-Perfect Hash Functions

Authors: Hermann, Stefan; Kirmayer, Sebastian; Lehmann, Hans-Peter; Sanders, Peter; Walzer, Stefan; Benoit, Anne; Kaplan, Haim; +2 Authors

Engineering Minimal k-Perfect Hash Functions

Abstract

Given a set $S$ of $n$ keys, a $k$-perfect hash function (kPHF) is a data structure that maps the keys to the first m integers, where each output integer can be hit by at most k input keys. When $m = ⌈n/k⌉$, the resulting function is called a minimal k-perfect hash function (MkPHF). Applications of kPHFs can be found in external memory data structures or to create efficient 1-perfect hash functions, which in turn have a wide range of applications from databases to bioinformatics. Several papers from the 1980s look at external memory data structures with small internal memory indexes. However, actual $k$-perfect hash functions are surprisingly rare, and the area has not seen a lot of research recently. At the same time, recent research in 1-perfect hashing shows that there is a lack of efficient kPHFs. In this paper, we revive the area of $k$-perfect hashing, presenting four new constructions. Our implementations simultaneously dominate older approaches in space consumption, construction time, and query time. We see this paper as a possible starting point of an active line of research, similar to the area of 1-perfect hashing.

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Keywords

ddc:004, Perfect Hashing, Information systems → Point lookups, DATA processing & computer science, Theory of computation → Bloom filters and hashing, Compressed Data Structures, Theory of computation → Data compression, info:eu-repo/classification/ddc/004

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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!
0
Average
Average
Average
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