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Approximate member query method based on hamming distance

A technology of approximate membership query and Hamming distance, which is applied in the field of approximate membership query based on Hamming distance, and can solve problems such as approximate membership query without consideration

Inactive Publication Date: 2018-12-21
NINGBO UNIV
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Problems solved by technology

[0004] However, the above techniques do not consider the approximate membership query problem in the Hamming space

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  • Approximate member query method based on hamming distance

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Embodiment Construction

[0024] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0025] An approximate membership query method based on Hamming distance, the method is as follows: first construct a Bloom filter with m bit vectors, denoted as HLBF, with addresses from 1 to m, where m is the number of bit vectors; and The initial value of each bit is set to 0, HLBF[t]=0, t=1,2,...,m;

[0026] Randomly generate L groups, k random integers in each group, set to h i，j , where i=1,2,...,k,j=1,2,...,L,h i，j ∈[1,w], uniform distribution, w is the length of binary data;

[0027] by the above h i，j For each binary data O of length w in the set Ω y Sampling is a bit group, y=1, 2,..., n, generating L bit strings, namely bitgroup y，j =j$O y [h 1，j ]$O y [h 2，j ]$...$O y [h k，j ], where $ is string connection, j=1, 2,..., L, n is the number of set Ω data, which constitutes the first layer hash of HLBF; by randomly hashing ea...

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Abstract

The invention discloses an approximate member query method based on Hamming distance, characterized in that a locally sensitive hash function (LSH)-bit sampling LSH suitable for Hamming distance metrics is used, combined with the random hash function in the standard Bloom filter (BF), to build the Bloom filter HLBF, for a given query data Q, L bit groups are generated. If the bit bits of b addresses of one bit group in the Bloom filter HLBF are all 1, the bit group is said to pass. If any one of the L bit groups passes, it is determined that the query data Q is an approximate member of the setOmega. The advantage is that the query of the approximate member can be completed in Hamming space.

Description

technical field [0001] The invention relates to an approximate member query method, in particular to an approximate member query method based on Hamming distance. Background technique [0002] There are a large number of set membership query problems in real life, that is, judging whether a query object is a member of a data set. For example, a security officer wants to check whether an unknown substance (with some detectable high-dimensional features) is a listed hazardous chemical; a network administrator wants to know whether a user's behavioral characteristics are harmful; Checking whether a submitted photo is similar to a photo in a large database, the above problems can be collectively referred to as approximate membership query. These queries all need to judge the distance between the query data and the data in the collection. The closer the query data is to the target data, the more valuable the data is. If it is a small low-dimensional data set, it can be solved ...

Claims

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Application Information

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IPC IPC(8): G06F17/30
Inventor 陈叶芳黄志鹏钱江波陈华辉
Owner NINGBO UNIV
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