Relation graph updating method based on Neo4j high-performance graph database

An update method and database technology, applied in the field of big data analysis, can solve the problems of loose organizational structure, heterogeneity and diversity, and achieve the effect of solving the problem of timed update

Pending Publication Date: 2021-05-11
CHINA CHANGFENG SCI TECH IND GROUPCORP
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AI Technical Summary

Problems solved by technology

Due to the characteristics of large-scale, heterogeneous and diverse, and loose organizational structure of big data, it poses challenges for people to effectively obtain information and knowledge

Method used

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  • Relation graph updating method based on Neo4j high-performance graph database

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Embodiment Construction

[0028] Such as figure 1 As shown, the present invention includes a data acquisition module, a data warehouse module, a data processing module, a data backup module, and a graph database storage module. The functions of data collection, analysis, and storage; the data warehouse module standardizes the collected source data to form a data warehouse for full data storage; the data processing module realizes data extraction from the resource library or data warehouse, Convert, upload, and import to the graph database; the data backup module is to back up and store the data files formed by the data processing module; the graph database storage module is to store the extracted entities, relationships, attributes and other information in the form of triples , supporting query and analysis of relational graphs.

[0029] The present invention utilizes the Kettle tool by building a Neo4j graph database cluster to efficiently realize timing incremental or full data extraction, conversio...

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Abstract

The invention provides a relational graph updating method based on a Neo4j high-performance graph database, which comprises the following steps: carrying out data acquisition, analysis and storage on file data such as dispersed database data and sensor data; carrying out standardization processing on the acquired source data to form a data warehouse to carry out full-amount data storage, so that data from a resource library or a data warehouse is extracted, converted, uploaded and imported into a graph database; backing up the formed data files and storing same, storing extracted information such as entities, relations and attributes in a triple form, and supporting query and analysis of a relation graph. According to the invention, a Neo4j graph database cluster is established, and a Kettle tool is utilized, so that timed increment or total data extraction, conversion, uploading and importing of data such as entities, relationships and attributes from a source database to the Neo4j graph database are efficiently realized, and the problem of timed updating of large-scale data can be effectively solved.

Description

technical field [0001] The invention belongs to the field of big data analysis, and relates to a method for updating a relational graph based on a Neo4j high-performance graph database. Background technique [0002] With the development of the Internet, multi-source mass data content is showing explosive growth. Due to the characteristics of large-scale, heterogeneous and diverse, and loose organizational structure, big data poses challenges for people to effectively obtain information and knowledge. Relational graph (Knowledge Graph), with its powerful semantic processing ability and open organization ability, has laid the foundation for knowledge organization and intelligent application in the Internet era. A knowledge graph aims to describe the entities that exist in the real world and the relationships between entities. With the development and application of artificial intelligence technology, knowledge graph, as one of the key technologies, has been widely used in bi...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/901G06F16/25G06F16/28G06F16/23
CPCG06F16/23G06F16/25G06F16/254G06F16/283G06F16/9024
Inventor 张丹普董雪梅赵曼
Owner CHINA CHANGFENG SCI TECH IND GROUPCORP
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