A graph data processing method and system for massive time series data
A technology of time series data and processing methods, which is applied in electrical digital data processing, special data processing applications, relational databases, etc., can solve the problem of insufficient consideration of graph data storage, waste of computing time and storage space of graph data processing programming model, and graph data. The processing technology cannot express the time-series interaction relationship and other problems, so as to achieve the effect of saving computing time, saving memory space, efficient storage and query performance
Active Publication Date: 2016-11-02
INST OF INFORMATION ENG CHINESE ACAD OF SCI
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[0010] The technical problem to be solved by the present invention is to provide a graph data processing method and system for massive time-series data. The vertex-based graph data processing programming model has a huge waste of computing time and storage space.
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[0065] The principles and features of the present invention are described below in conjunction with the accompanying drawings, and the examples given are only used to explain the present invention, and are not intended to limit the scope of the present invention.
[0066] Such as figure 1 As shown, a graph data processing method for massive time series data includes the following steps:
[0067] Step 1: Preprocess the social network data, and abstract the graph structure with vertices representing characters and several edges with time stamps representing the interaction between characters;
[0068] Step 2: Divide the graph structure into several graph structure blocks according to the predetermined Euclidean distance according to the celebrity effect, and number the graph structure blocks and their internal vertices;
[0069] Step 3: Assign graph structure blocks to different nodes for processing, and each node imports its allocated graph structure blocks into corresponding ...
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The invention relates to a graph data processing method and system for massive time series data. The graph data processing method for the massive time series data comprises the steps of carrying out preprocessing on social network data, and abstracting a graph structure which uses vertexes to represent figures, and uses a plurality of edges with timestamps to represent the interactive relationship among the figures, wherein the representing method can effectively represent a social network relationship with an interactive sequential relationship; dividing the graph structure into a plurality of graph structure blocks according to the celebrity charm and a preset Euclidean distance, and numbering the graph structure blocks and the vertexes of the interiors of the graph structure blocks; leading the graph structure blocks in corresponding positions of a memory according to the organization mode of the memory, wherein the storage mode of the memory makes full use of the distribution characteristics of graph data, and efficient storage performance and efficient query performance can be achieved. According to the graph data processing method for the massive time series data, an original programming model using the vertexes as calculation units is improved on the basis of the principle that the calculation time and the memory space are saved, and a programming model using messages as calculation units is adopted, so that the calculation time is saved through the mode to a large extent, and the storage space is also saved.
Description
technical field [0001] The invention relates to the field of large-scale graph data processing, in particular to a graph data processing method and system for massive time series data. Background technique [0002] In recent years, with the rapid development of the Internet, social networking platforms have also developed rapidly and become popular. Graph data is used to represent the relationship between characters in social networks, and the hidden information in the relationship between characters in social networks can be mined by combining graph algorithms. Therefore, with the development of social networks, graph data has once again ushered in a boom. [0003] However, with the gradual popularization of social networks, social network data also shows an exponential growth trend. Therefore, it is impossible to use a stand-alone layout data processing program to realize the analysis and processing of these large-scale map data. [0004] With the popularity of social n...
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IPC IPC(8): G06F17/30
CPCG06F16/288G06F16/958
Inventor 周薇高赟冉攀峰韩冀中
Owner INST OF INFORMATION ENG CHINESE ACAD OF SCI