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An Approximate Membership Query Method Based on High Dimensional Data Filter

A technology of approximate member query and high-dimensional data, applied in the field of approximate member query based on high-dimensional data filter, it can solve problems such as difficult and unacceptable query results, and achieve the effect of reducing false negative rate and space cost.

Active Publication Date: 2017-07-28
宁波宁变电力科技股份有限公司
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AI Technical Summary

Problems solved by technology

[0005] However, both techniques of DSBF and LSBF to filter AMQ queries have a limitation, that is, they can only filter AMQ queries with a given distance
However, it is not easy to give an appropriate distance, too large or too small a distance value may lead to unacceptable query results

Method used

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  • An Approximate Membership Query Method Based on High Dimensional Data Filter
  • An Approximate Membership Query Method Based on High Dimensional Data Filter
  • An Approximate Membership Query Method Based on High Dimensional Data Filter

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Embodiment Construction

[0017] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0018] We use the real handwritten digit letter recognition dataset to evaluate and compare the method of the present invention and the existing LSBF method. The data set contains 5,620 data, and each data represents handwritten Arabic numerals with 64-dimensional features, namely, '0', '1',...,'9'. Eigenvalues ​​range from 0 to 16 integers. Divide the '0' data into two groups, a group of 10 data as a set Ω, and the other group as a test data q to test the false negative rate; in addition, take the 10 data as '1' as a set Ω, Other data is used as test data q to test the false positive rate. The experimental results are the average of 10000 random calculations.

[0019] A filter-based approximate membership query method for high-dimensional data, the target data set is defined as Ω, and the distance-sensitive hash function H is defined as ...

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Abstract

The invention discloses an approximate membership query method based on a high-dimension data filter. The method has the advantages that a new structure supported by a new distance sensitive hash function is defined to respectively present multi-dimension data and to-be-inquired multi-dimension data in a target data set, so the reconstruction of the filter is not needed, approximate membership queries with more filtering distance parameters can be supported, and the space cost is greatly reduced; a plurality of function groups are utilized, each function group contains multiple functions, an and-or combining method is utilized to judge when whether a membership is the approximate membership in a target data set omega or not is finally determined, and the false negative rate of the filter is reduced.

Description

technical field [0001] The invention relates to an approximate member query method, in particular to an approximate member query method based on a high-dimensional data filter. Background technique [0002] In many application fields, the closer the query data is to the target data, the higher the value of the data. 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; Check that submitted photos are similar to photos in one of the large databases. These queries all need to judge the distance between the query data and the data in the (target data) collection. If it is a small low-dimensional data set, it can be solved by linear search, but it will be very time-consuming to use linear search and matching for a massive high-dimensional data set, and in many cases it cannot meet r...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/30
CPCG06F16/2462
Inventor 陈叶芳钱江波陈华辉
Owner 宁波宁变电力科技股份有限公司
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