Top-k arrangement query method based on metric space in distributed environment

Abstract

The invention discloses a top-k arrangement query method based on a metric space in a distributed environment. The top-k arrangement query method sequentially comprises the following steps of: (1), giving a query input set Q and a distance formula d() in the metric space, wherein the distance formula is used for measuring the distance between a whole data object and a query object Q; and (2), providing a parallel algorithm based on a set ANN and k-skyband according to the step (1). Characteristics of parallel calculation among various nodes are sufficiently utilized in the distributed environment; the top-k arrangement query performance based on the metric space in a large dataset environment is greatly improved by pruning and sorting; the query speed is enhanced; and services are provided for decision of users.

Description

technical field [0001] The invention relates to a query method, in particular to a parallel top-k dominance query method based on metric space in a distributed environment of mass data concentration. Background technique [0002] As an important complex query, the top-k domination query based on metric space is getting more and more attention. It returns a part of the data that meets the user's needs from the massive multi-dimensional data set. This type of query provides decision-making for users, for example, it has a wide range of applications in web search, multimedia retrieval, e-commerce and other fields. This query does not require the user to give an evaluation function and the result set is controllable. It calculates the dominance score of each object and returns the k result sets with the highest dominance scores. [0003] A top-k dominance query based on a metric space is defined as follows: with O={o 1 ,o 2 ,...,o n} represents the collection of all data obj...

AI Technical Summary

Problems solved by technology

Tiakas E and others first proposed this concept, but it was only studied in the traditional stand-alone mode. At present, with the rapid increase of data sets, the traditional stand-alone algorithm encounters performance bottlenecks, and Tiakas E and others use M-tree. The index storage structure is completely inapplicable for large data sets, which will lead to a large amount of data redundancy, so it is imminent to study the parallel top-k dominance algorithm based on metric space

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