A Parallel Approach to Profile Query Based on Uncertain Preference Relations

Abstract

The invention discloses a parallel method for skyline query on the basis of an uncertain preference relation. The parallel method comprises the following steps: inputting a data set D; initializing a label length |L| of the data set D; generating 2L different labels according to the label length |L|; according to every selected current data tuple O traversed from the data set D; carrying out parallel calculation on values of 2L disjoint crystal lattices on skyline query Sky (O) including 2n items of the current data tuple O by using 2L processes, wherein every process calculates a crystal lattice; and adding results obtained by calculating the processes to obtain the skyline query Sky (O) of the current data tuple O and outputting the skyline query Sky (O). The parallel method for skyline query on the basis of the uncertain preference relation has the advantages that the number of the parallel processes is controllable, the parallel efficiency can be maximized, required time is reduced along with label length indexes, the query time can be greatly shortened, and efficient query can be realized.

Description

technical field [0001] The present invention relates to the research on the parallel realization of the contour query method of uncertain preference in application fields such as large-scale data contour query and recommendation system, and specifically relates to a parallel method of contour query based on uncertain preference relationship. Background technique [0002] Contour query, also known as Skyline query, was first studied as a vector maximization problem. It is a typical multi-objective optimization problem and has attracted widespread attention in the database field. Skyline query refers to selecting a subset from a given object set S in an N-dimensional space. None of the points in the subset can be dominated by other points in S. Points that satisfy this condition are called SP (Skyline Point) . The dominance relationship here means that in the N-dimensional space, if there are two objects P and Q, each dimension of the N-dimensional data of the object P is bet...

AI Technical Summary

Problems solved by technology

However, due to the continuous expansion of query requirements and the exponential relationship between the computational time complexity and the size of the data set, the existing centralized methods are difficult to meet the query efficiency requirements of real-world applications.

[0004] Due to the characteristics of uncertain preference Skyline modeling and complex probability representation and calculation, Skyline queries with uncertain preference face severe challenges

And because of the exponential time complexity of skyline queries based on uncertain preferences, Skyline queries with uncertain preferences face the problem of query practicability

The current skyline query based on uncertain preference is a serial method, and its performance cannot meet the needs of large-scale query. At present, there is no parallel query method based on uncertain preference Skyline

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