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Space Efficient Hash Tables With Worst Case Constant Access Time
 In STACS
, 2003
"... We generalize Cuckoo Hashing [23] to dary Cuckoo Hashing and show how this yields a simple hash table data structure that stores n elements in (1 + ffl) n memory cells, for any constant ffl ? 0. Assuming uniform hashing, accessing or deleting table entries takes at most d = O(ln ffl ) probes ..."
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Cited by 47 (4 self)
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We generalize Cuckoo Hashing [23] to dary Cuckoo Hashing and show how this yields a simple hash table data structure that stores n elements in (1 + ffl) n memory cells, for any constant ffl ? 0. Assuming uniform hashing, accessing or deleting table entries takes at most d = O(ln ffl ) probes and the expected amortized insertion time is constant. This is the first dictionary that has worst case constant access time and expected constant update time, works with (1 + ffl) n space, and supports satellite information. Experiments indicate that d = 4 choices suffice for ffl 0:03. We also describe variants of the data structure that allow the use of hash functions that can be evaluted in constant time.
On Universal Classes of Extremely Random Constant Time Hash Functions and Their TimeSpace Tradeoff
"... A family of functions F that map [0; n] 7! [0; n], is said to be hwise independent if any h points in [0; n] have an image, for randomly selected f 2 F , that is uniformly distributed. This paper gives both probabilistic and explicit randomized constructions of n ffl wise independent functions, ..."
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Cited by 25 (0 self)
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A family of functions F that map [0; n] 7! [0; n], is said to be hwise independent if any h points in [0; n] have an image, for randomly selected f 2 F , that is uniformly distributed. This paper gives both probabilistic and explicit randomized constructions of n ffl wise independent functions, ffl ! 1, that can be evaluated in constant time for the standard random access model of computation. Simple extensions give comparable behavior for larger domains. As a consequence, many probabilistic algorithms can for the first time be shown to achieve their expected asymptotic performance for a feasible model of computation. This paper also establishes a tight tradeoff in the number of random seeds that must be precomputed for a random function that runs in time T and is hwise independent. Categories and Subject Descriptors: E.2 [Data Storage Representation]: Hashtable representation; F.1.2 [Modes of Computation]: Probabilistic Computation; F2.3 [Tradepffs among Computational Measures]...
Toward a usable theory of Chernoff Bounds for heterogeneous and partially dependent random variables
, 1992
"... Let X be a sum of real valued random variables and have a bounded mean E[X]. The generic ChernoffHoeffding estimate for large deviations of X is: P rfX \GammaE[X ] ag min 0 e \Gamma(a+E[X]) E[e X ], which applies with a 0 to random variables with very small tails. At issue is how to use this ..."
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Cited by 6 (1 self)
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Let X be a sum of real valued random variables and have a bounded mean E[X]. The generic ChernoffHoeffding estimate for large deviations of X is: P rfX \GammaE[X ] ag min 0 e \Gamma(a+E[X]) E[e X ], which applies with a 0 to random variables with very small tails. At issue is how to use this method to attain sharp and useful estimates. We present a number of ChernoffHoeffding bounds for sums of random variables that may have a variety of dependent relationships and that may be heterogeneously distributed. AMS classifications 60F10, Large deviations, 68Q25 Analysis of algorithms, 62E17, Approximations to distributions (nonasymptotic), 60E15, Inequalities. Key words: Hoeffding bounds, Chernoff bounds, dependent random variables, Bernoulli trials. This research was supported, in part, by grants NSFCCR8902221, NSFCCR8906949, and NSFCCR9204202. 1 Summary In the analysis of probabilistic algorithms, some of the following problems may arise, possibly in complex combinations....
Closed Hashing is Computable and Optimally Randomizable with Universal Hash Functions
"... Universal hash functions that exhibit c log nwise independence are shown to give a performance in double hashing, uniform hashing and virtually any reasonable generalization of double hashing that has an expected probe count of 1 1\Gammaff +O( 1 n ) for the insertion of the ffnth item into a ta ..."
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Cited by 6 (1 self)
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Universal hash functions that exhibit c log nwise independence are shown to give a performance in double hashing, uniform hashing and virtually any reasonable generalization of double hashing that has an expected probe count of 1 1\Gammaff +O( 1 n ) for the insertion of the ffnth item into a table of size n, for any fixed ff ! 1. This performance is optimal. These results are derived from a novel formulation that overestimates the expected probe count by underestimating the presence of local items already inserted into the hash table, and from a very sharp analysis of the underlying stochastic structures formed by colliding items. Analogous bounds are attained for the expected rth moment of the probe count, for any fixed r, and linear probing is also shown to achieve a performance with universal hash functions that is equivalent to the fully random case. Categories and Subject Descriptors: E.1 [Data]: Data Structuresarrays; tables; E.2 [Data]: Data Storage Representationsha...
Double Hashing is Computable and Randomizable with Universal Hash Functions
"... Universal hash functions that exhibit c log nwise independence are shown to give a performance in double hashing and virtually any reasonable generalization of double hashing that has an expected probe count of 1/(1alpha) + epsilon for the insertion of the alpha nth item into a table of size n, f ..."
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Cited by 3 (1 self)
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Universal hash functions that exhibit c log nwise independence are shown to give a performance in double hashing and virtually any reasonable generalization of double hashing that has an expected probe count of 1/(1alpha) + epsilon for the insertion of the alpha nth item into a table of size n, for any fixed alpha 0. This performance is within epsilon of optimal. These results are derived from a novel formulation that overestimates the expected probe count by underestimating the presence of partial items already inserted into the hash table, and from a sharp analysis of the underlying stochastic structures formed by colliding items.