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

A technology of task optimization and query optimization, which is applied in the direction of electronic digital data processing, special data processing applications, digital data information retrieval, etc., can solve the problems of high communication cost and processing overhead and inapplicability of servers, so as to improve flexibility and reduce Complexity, the effect of improving query efficiency

Pending Publication Date: 2022-02-08
BEIJING UNIV OF POSTS & TELECOMM
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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.

Method used

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

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Embodiment 1

[0025] figure 1 It is a schematic flowchart of a query task optimization method based on large-scale graph data for scientific and technological consulting provided according to an embodiment of the present application, as shown in figure 1 As shown, the method may include:

[0026] Step 101. Obtain the identifier of the query task.

[0027] It should be noted that, in the embodiments of the present disclosure, the query task may include organization, talent, and industry chain. Among them, in the embodiment of the present disclosure, the organization can be the ID of the company, and the talent can be the personnel

[0028] Wherein, in the embodiments of the present disclosure, the identifier of the query task may be acquired according to the content of the query task. As an example, in the embodiment of the present disclosure, assuming that the query task is to view the company and patent information associated with a certain person, the identifier of the query task is ob...

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PUM

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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...

Claims

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Application Information

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IPC IPC(8): G06F16/2453G06F16/2455G06F16/36
CPCG06F16/2453G06F16/24553G06F16/367
Inventor 鄂海红宋美娜梁静茹刘雨薇魏秋实
Owner BEIJING UNIV OF POSTS & TELECOMM
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