Data integration method and device for heterogeneous database and storage medium

Abstract

The invention discloses a data integration method and device for a heterogeneous database and a storage medium. The method comprises the steps that a first undirected weighted graph model and a secondundirected weighted graph model are established for a first database and a second database; key nodes in the first undirected weighted graph model and the second undirected weighted graph model are extracted respectively, and a first key node set and a second key node set are generated; the method also includes constructing a similarity matrix between all data columns contained in each key node in the first key node set and all data columns contained in each key node in the second key node set; determining a to-be-matched data column, and obtaining a plurality of optimal data columns corresponding to the to-be-matched data column from the similarity matrix to generate a candidate matching list; sorting the plurality of optimal data columns in the candidate matching list in a descending order to generate a plurality of sorted optimal data columns; and determining a data matching result based on the sorted multiple optimal data columns. Therefore, by adopting the embodiment of the invention, the data matching efficiency and matching accuracy during data integration in the heterogeneous database can be improved.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a data integration method, device and storage medium for heterogeneous databases. Background technique [0002] At present, the relational database system is still the mainstream data storage method. With the development of information technology, the amount of data in the relational database corresponding to the software system in various fields has increased sharply. For example, in the same field, the software system in this field corresponds to the There are multiple subsystems, each of which corresponds to its own relational database, so that there are many heterogeneous databases in the software system in this field. Among the various heterogeneous databases, the data size of a single database is small, which has certain limitations on the expression of the entire field. Therefore, researchers are increasingly eager to integrate multiple heterogeneous databases into one d...

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Problems solved by technology

[0004] The disadvantages of the above methods are mainly concentrated in: (1) The complexity of the algorithm is high. When the data source data to be matched is large, the algorithm will also perform a large amount of similarity for many data elements (non-key columns) with low frequency of occurrence. Calculation, consumes a lot of computing resources and time costs

(2) For the columns that have not been matched, the synonyms of these columns are not included in the synonym dictionary, so for the matching of these columns, only a single similarity measurement method based on column data characteristics can be used

(3) The similarity measurement method between two columns is too single, mainly considering the data characteristics of the columns, less considering the semantics of the column names, and not considering the relationship between the columns in the same data table

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