Directed label graph adaptive index building method based on structure summary model

A construction method and self-adaptive technology, applied in the field of graph data management, can solve problems such as large-scale graph data query

Active Publication Date: 2016-12-07
NANKAI UNIV
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  • Abstract
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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem of querying large-scale graph data, and to provide a method for constructing an adaptive index for directed label graphs based on a structural summary model

Method used

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  • Directed label graph adaptive index building method based on structure summary model
  • Directed label graph adaptive index building method based on structure summary model
  • Directed label graph adaptive index building method based on structure summary model

Examples

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Embodiment 1

[0077] Example 1: Construction Method of Adaptive Index of Directed Labeled Graph Based on Structural Summary Model

[0078] 1. Divide the data graph into vertex equivalence classes

[0079] We are as follows figure 1 The directed label graph is divided according to the method of the present invention, the rank value of each node is calculated, and then the vertices with the same label and the same rank value are divided into one class, because

[0080] rank(C 1 ) = rank(C 2 ) = rank(C 3 )=0

[0081] Therefore vertex C 1 、C 2 、C 3 is divided into an equivalence class, and similarly

[0082] rank(A 1 ) = rank(A 2 ) = rank(A 3 )=2

[0083] Vertex A 1 、A 2 、A 3 are also divided into the same equivalence class, resulting in figure 2 The vertex equivalence class partition graph shown.

[0084] 2. Construct the indexes of vertices and edges respectively according to the structure summary model

[0085] First map the vertices in the data graph to a set of serial nu...

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Abstract

Along with the development and popularization of Internet technology, the data scale in the relevant fields of information technologies such as social networks and semantic networks explosively increases, but the subgraph matching inquiry problem becomes a hotspot research problem of graph data management. In order to improve the subgraph matching inquiry efficiency of a large-scale data graph, a matching inquiry method based on a graph analog mode can be used; the data graph needs to be compressed, and indexes are built for inquiring the data graph; and by aiming at a directed label graph, the invention provides an adaptive index building method based on a structure summary model. The method comprises the steps of performing equivalence class partitioning on the graph data; building the structure summary model; building vertex indexes, and building edge indexes; and performing adaptive updating on the indexes.

Description

technical field [0001] The invention belongs to the technical field of graph data management. Background technique [0002] Graph is the most commonly used abstract data structure in computer science. It is more complex in structure and semantics than linear tables and trees, and has more general representation capabilities. Many application scenarios in the real world need to be represented by a graph structure, and graph-related processing and applications are almost ubiquitous. [0003] Large-scale graph data usually contains more than one million vertices, and the time and space overheads of storage, update, search and other processing far exceed the capacity of traditional graph data management. Efficient management of large-scale graph data, such as storage, indexing, update, query, search, etc., has become an urgent problem to be solved, especially the problems closely related to the application of large-scale graph data, such as sub-graph query of large-scale graph ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
Inventor 张海威李仲伟解晓芳袁晓洁
Owner NANKAI UNIV
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