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NAE-SAT-based probabilistic membership filters
Probabilistic membership filters are a type of data structure designed t...
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Ribbon filter: practically smaller than Bloom and Xor
Filter data structures over-approximate a set of hashable keys, i.e. set...
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Dynamic Partition Bloom Filters: A Bounded False Positive Solution For Dynamic Set Membership (Extended Abstract)
Dynamic Bloom filters (DBF) were proposed by Guo et. al. in 2010 to tack...
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Bloom filter variants for multiple sets: a comparative assessment
In this paper we compare two probabilistic data structures for associati...
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Non-Empty Bins with Simple Tabulation Hashing
We consider the hashing of a set X⊆ U with |X|=m using a simple tabulati...
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Bloom Multifilters for Multiple Set Matching
Bloom filter is a space-efficient probabilistic data structure for check...
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A Case for Partitioned Bloom Filters
In a partitioned Bloom Filter the m bit vector is split into k disjoint ...
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On Occupancy Moments and Bloom Filter Efficiency
Two multivariate committee distributions are shown to belong to Berg's family of factorial series distributions and Kemp's family of generalized hypergeometric factorial moment distributions. Exact moment formulas, upper and lower bounds, and statistical parameter estimators are provided for the classic occupancy and committee distributions. The derived moment equations are used to determine exact formulas for the false-positive rate and efficiency of Bloom filters -- probabilistic data structures used to solve the set membership problem. This study reveals that the conventional Bloom filter analysis overestimates the number of hash functions required to minimize the false-positive rate, and shows that Bloom filter efficiency is monotonic in the number of hash functions.
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