Integer-Granularity Locality-Sensitive Bloom Filter

Numerous network applications require a fast and efficient way to check whether a given (query) object is close to at least one data object in a particular set to reduce unnecessary transmissions. Some variants of the Bloom filter, such as the multi-granularity locality-sensitive Bloom filter (MLBF)...

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Bibliographic Details
Published inIEEE communications letters Vol. 20; no. 11; pp. 2125 - 2128
Main Authors Qian, Jiangbo, Zhu, Qiang, Chen, Huahui
Format Journal Article
LanguageEnglish
Published New York IEEE 01.11.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Animals
Bloom filter
false positive/negative rates
Gaussian distribution
Indexes
Information filtering
locality-sensitive hashing
Object recognition
Probability density function
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Summary:Numerous network applications require a fast and efficient way to check whether a given (query) object is close to at least one data object in a particular set to reduce unnecessary transmissions. Some variants of the Bloom filter, such as the multi-granularity locality-sensitive Bloom filter (MLBF), have been suggested to meet this requirement. Nevertheless, the MLBF was designed to only filter (query) objects with multiple logarithmic distance granularities. In this letter, we propose a novel Bloom filter structure, called the integer-granularity locality-sensitive Bloom filter (ILBF), which can filter objects with multiple integer distance granularities. Theoretical analyses for the property of the ILBF are presented. Experiments show that the theoretical estimates are quite accurate and the ILBF structure is efficient and effective.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2016.2600670