Query task optimization method based on science and technology consultation large-scale graph data

Abstract

The invention provides a query task optimization method and system based on the science and technology consultation large-scale graph data, and a storage medium. According to the invention, the identification of a query task is obtained, and a corresponding query optimization method is selected according to the identification of the query task; and the query optimization method comprises the steps of graph traversal expansion sequence strategy adjustment, Cardinality reduction, mode advance, view materialization, querying of a graph database by using the query optimization method, and outputting of a query result. Therefore, in the method provided by the invention, the corresponding query optimization method can be selected according to the identifier of the query task, and the flexibility of the query method is improved. Meanwhile, in the method provided by the invention, the query optimization method improves the query efficiency of the query task of the science and technology consultation large-scale graph data in different scenes, reduces the complexity of query calculation, and shortens time spent on query.

Description

technical field [0001] The present application relates to the field of large-scale graph data query, in particular to a query task optimization method, device and storage medium based on scientific and technological consulting large-scale graph data. Background technique [0002] Query tasks on graph data are one of the most basic problems in the field of knowledge graphs, so efficient query processing on large-scale graph data is usually required so that users can quickly obtain query results. [0003] At present, although the query optimization technology on graph data has made great progress, there are still some problems: the graph partition technology for graph query optimization can split graph data into multiple servers, but the communication cost and processing overhead of the server Higher. Moreover, most query optimization technologies are based on social network graph data for query optimization, which is not suitable for graph data with complex topological struc...

AI Technical Summary

Problems solved by technology

[0003] At present, although the query optimization technology on graph data has made great progress, there are still some problems: the graph partition technology for graph query optimization can split graph data into multiple servers, but the communication cost and processing overhead of the server Higher

Moreover, most query optimization technologies are based on social network graph data for query optimization, which is not suitable for graph data with complex topological structures in technology consulting scenarios.

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