Graph model data processing method and device, and terminal equipment

Abstract

The invention provides a graph model data processing method and a device, and terminal equipment. The method comprises the steps of extracting candidate graph attribute information capable of servingas graph dimensions and / or graph metrics from graph model data according to graph attributes of the graph model data; wherein the graph attributes of the graph model data are attributes associated with points and / or edges in the graph model data; screening the candidate graph attribute information according to a preset statistical rule and a machine learning model to obtain target graph attributeinformation; and generating a data cube with a preset dimension according to the target graph attribute information. By means of the mode, graph model data can be automatically subjected to online analysis processing, statistical rules and a machine learning model are fused in the processing process so as to efficiently screen attribute information, and the data cubes generated in the mode are also beneficial to subsequent screening of the data cubes; according to the method, the analysis and processing efficiency and accuracy of graph model data can be improved, so that a data processing result has higher value.

Description

technical field [0001] The present invention relates to the technical field of data processing, in particular to a method, device and terminal equipment for processing graph model data. Background technique [0002] Different from the single mode of relational data, graph model data has the property of highly heterogeneous and non-fixed mode. Based on this property, it is difficult to apply the OLAP (Online Analytical Processing) method of relational data for analysis and processing of graph model data. . In related technologies, the processing of graph model data is mostly implemented by self-designed programs, and more manual processing methods need to be introduced at the same time. However, with the advent of the big data era, the scale of graph model data is also increasing. The common Large-scale graph model data may have billions of points and tens of billions of edges. The complexity of the graph model data is high, so that the processing method of the above graph ...

AI Technical Summary

Problems solved by technology

The complexity of the graph model data is high, which makes the processing method of the above graph model data not only low in processing efficiency, but also low in accuracy, resulting in low value of data processing results

Images