Hashing functions

Knott, Gary D.

doi:10.1093/comjnl/18.3.265

Cited by 62 publications

(37 citation statements)

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“…Although the related surveys by Knott [1975] and Comer [1979] are somewhat dated, they represent good coverage of the different approaches. In practice, the most common one-dimensional structures include linear hashing [Litwin 1980;Larson 1980], extendible hashing [Fagin et al 1979], and the B-tree [Bayer and McCreight 1972].…”

Section: One-dimensional Access Methodsmentioning

confidence: 99%

Multidimensional access methods

1998

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Search operations in databases require special support at the physical level. This is true for conventional databases as well as spatial databases, where typical search operations include the point query (find all objects that contain a given search point) and the region query (find all objects that overlap a given search region). More than ten years of spatial database research have resulted in a great variety of multidimensional access methods to support such operations. We give an overview of that work. After a brief survey of spatial data management in general, we first present the class of point access methods, which are used to search sets of points in two or more dimensions. The second part of the paper is devoted to spatial access methods to handle extended objects, such as rectangles or polyhedra. We conclude with a discussion of theoretical and experimental results concerning the relative performance of various approaches.

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Section: One-dimensional Access Methodsmentioning

confidence: 99%

Multidimensional access methods

1998

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“…It is certainly not in the scope of this text the design of hash functions that are most appropriate to each case, a subject that requires statistical tools and specific study [15]. The algorithms presented in this paper, however, do not depend upon complicated, custom-tailored hash functions.…”

Section: Hashingmentioning

confidence: 99%

Some Illustrative Examples on the Use of Hash Tables

2015

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ABSTRACT.Hash tables are among the most important data structures known to mankind. Through hashing, the address of each stored object is calculated as a function of the object's contents. Because they do not require exorbitant space and, in practice, allow for constant-time dictionary operations (insertion, lookup, deletion), hash tables are often employed in the indexation of large amounts of data. Nevertheless, there are numerous problems of somewhat different nature that can be solved in elegant fashion using hashing, with significant economy of time and space. The purpose of this paper is to reinforce the applicability of such technique in the area of Operations Research and to stimulate further reading, for which adequate references are given. To our knowledge, the proposed solutions to the problems presented herein have never appeared in the literature, and some of the problems are novel themselves.

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“…This is important because index permutation is a common error when code is parallelised and evolved. (In applications that might wish to ignore permutations this attribute could be excluded from the desirable list or properties) According to [24], hash functions such as the Bob Jenkins' hash function, FNV hash functions, the SHA hash family, or the MD hash family all exhibit the above properties [24,25], with advanced hashing evaluation and design techniques discussed in [26][27][28]. As a result, we have chosen Bob Jenkins' hash function and FNV hash function, because they are relatively simple to implement, work consistently on different data types, and promise very efficient performance, while being hardware and platform independent.…”

Section: Desirable Hash Function Propertiesmentioning

confidence: 99%