A non-intrusive cross-database data fusion method based on API splicing

Abstract

The present invention is applicable to the field of data fusion technology, and provides a non-intrusive cross-database data fusion method based on API splicing. , Multiple data nodes across regions are "spliced" into a whole through the same entity ID, and a "graph network" based on distributed services is dynamically constructed to realize the integration of master data entities in the government big data center and remote storage in business Departmental business data entities are "associated and spliced" to achieve cross-departmental data management and control, data collaboration, and improve the level of data application.

Description

technical field [0001] The invention belongs to the technical field of data fusion, and in particular relates to a non-invasive cross-database data fusion method based on API splicing. Background technique [0002] In recent years, in order to adapt to the construction and development of smart cities, the demand for multi-information collision, correlation, and integration has greatly increased, and the exchange and sharing of government affairs data has continued unabated. In the data usually stored in the government big data center, master data (public data, such as basic population information, basic housing information, etc.) are often used for exchange, but the business data associated with it (population behavior, housing management information, etc.) Factors such as high frequency, privacy, and data security are seldom directly used for exchange, and this business data that can reflect the business status is precisely where the value of the data lies. On the other ha...

AI Technical Summary

Problems solved by technology

For data platform engineers, on the one hand, they need to consume data from other teams, but other teams are not motivated to provide such real, accurate, and meaningful data; on the other hand, they know little about the field of production data , lack of expertise

They also need to provide data for each demand side, but they do not understand the domain knowledge of the data demand side. Data service APIs provided through traditional methods often cannot respond to the demand side's requirements in a timely manner.

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