Long sequence data dimensionality reduction method used for approximate query

Abstract

A similarity query-oriented long sequence data dimension reduction method is provided, which comprises adopting sequence embedding technology to convert sequence data into an embedded tree and extracting a plurality of sets; extracting a plurality of corresponding principal components based on the embedded tree and a plurality of sets and bringing forward distance convergence-based sequence data dimension reduction principle on the basis; on the basis of character of dimension reduction, constructing index structures facing sequence similarity query, SEM-tree and putting forward a high efficiency similarity query method facing long sequence data on the basis of the index structures and on the principle of sequence distance double bounds (maximum upper bound and minimum bound). The invention can be widely applied in the similarity query facing long sequence data, such as finding targets being searched through similarity search from a sea of Internet text data and carrying out similarity query and analysis on gene fragments from large-scale genetic data. The invention can forecast gaining obvious economic and social benefits.

Description

technical field [0001] The invention relates to a long sequence data dimensionality reduction method for approximate query, in particular to a sequence embedding (Sequential Embedding) method supporting efficient long sequence approximate query, which belongs to the technical field of computer data management. Background technique [0002] Long sequence data exists in various scientific research and application fields, such as text data on the Internet, gene sequence data in the biological field, and so on. Approximation query for these long sequence data has a very wide range of application requirements. Such as the search of Internet text data, the similarity exploration of genes in gene databases, and the sequence query in the multimedia field. Approximation query based on long sequence data has become a concern in the field of international data management research. [0003] At present, there are some methods and technologies that support the similarity query of sequen...

AI Technical Summary

Problems solved by technology

However, due to the complexity of the distance similarity measure in the original sequence space, the complexity of constructing the embedding space is too high to be feasible

[0010] From the above analysis, it can be seen that the existing methods that support long sequence data for similarity query cannot well solve the technical problems faced in current practical applications. Dimension method, used to support similarity query of long sequence data

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