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An approximate member query method based on hamming distance

A technology of approximate member query and Hamming distance, which is applied in the field of approximate member query based on Hamming distance

Inactive Publication Date: 2018-12-18
NINGBO UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0010] The technical problem to be solved by the present invention is to provide an approximate membership query method based on Hamming distance, which can not only solve the approximate membership query problem in Hamming space, but also support different granularities without rebuilding the Bloom filter. query

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  • An approximate member query method based on hamming distance
  • An approximate member query method based on hamming distance
  • An approximate member query method based on hamming distance

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Embodiment Construction

[0029] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0030] An approximate membership query method based on Hamming distance, the method is:

[0031] First construct a Bloom filter with m bit vectors, denoted as HLBF, the address is 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;

[0032] Randomly generate L groups, k random integers in each group, set to h t，f , where i=1,2,...,k,j=1,2,...,L,h i，j ∈[1,w], uniform distribution, w is the length of binary data;

[0033] by the above h i，j For each binary data O of length w in the set Ω y , y=1,2,...,n are sampled as bit groups to generate 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 each bi...

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Abstract

The invention discloses an approximate member query method based on Hamming distance, which is characterized in that a locally sensitive hash function (LSH)-bit sampling LSH suitable for Hamming distance metrics is used; based on the random hash function in the standard Bloom filter (BF), the Bloom filter HLBF is built; for a given query data Q, C virtual data are generate by randomly flipping s bit on Q, and L bit strings are generated for each virtual data; if the bits of b addresses of a bit string in the Bloom filter HLBF are all 1, this bit string is said to pass; if there are c virtual data, and each virtual data has L bit string, namely any one of that c*L bit strings passes, it is determined that the query data Q is an approximate member of the set omega, and the advantage is thatthe query of the approximate member can be completed in Hamming space, meanwhile, the query of different granularity can be supported without rebuilding the Bloom filter by creating a virtual object.

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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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/22
Inventor 钱江波黄志鹏陈叶芳陈华辉
Owner NINGBO UNIV