A Quick Way to Find Linked Data

Abstract

The invention provides a method for rapidly searching relevant data. The method comprises the following steps: carrying out set partitioning on data to be processed; storing set data into a two-dimensional table and setting a sequence numbering field; sequencing the two-dimensional table respectively according to a set field and an element field, so as to obtain different tables A and B, and meanwhile, introducing an index field and a reverse index field; repeatedly searching the tables A and B through a reverse index until all records of all the relevant sets are searched, and setting sequence numberings corresponding to the records of all the relevant sets as the same numerical value; and rapidly searching all the relevant sets according to the same sequence numbering to obtain all the relevant records, so as to obtain all relevant data groups. With the adoption of the method for rapidly searching the relevant data, the efficiency of data classification and data relevance search can be improved.

Description

technical field [0001] The invention relates to the field of data search, in particular to a method for quickly searching associated data. Background technique [0002] In big data mining, some related data needs to be integrated, for example, the telecom package tariff needs to be stored and associated with user or package information, and all related information can be obtained as needed, and for example, when setting some recommendation functions (menu Recommendations, music recommendations, etc.), it is also necessary to obtain a large amount of relevant information for processing. At present, for the collection and processing of related data, it is generally classified through collections and then associated, and the efficiency of merging and association between collections directly affects the efficiency of data processing. [0003] When data is classified and represented by sets, capital letters are usually used to indicate the sets, and lowercase letters are used to...

AI Technical Summary

Problems solved by technology

[0004] The logic of the above scheme is relatively simple and clear, but the efficiency of this scheme is average. During the processing, it will be found that the slowest part of the processing is the intersection judgment in the set operation. When the number of sets is huge (more than one million), and the When the degree of correlation is large (for example, more than 100,000 sets have mutual correlations and will eventually form an intersection), the processing performance will drop sharply

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