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Large graph database query method based on divide-and-conquer evolutionary algorithm

A query method and evolutionary algorithm technology, applied in other database query, other database retrieval, other database indexing, etc., to achieve the effect of reducing algorithm running time, strong search ability, and quality assurance

Pending Publication Date: 2022-04-15
SOUTH CHINA UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to propose an effective algorithm to solve the query problem of large-scale graph database, and apply the divide-and-conquer evolutionary algorithm to the query of large-scale graph database

Method used

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  • Large graph database query method based on divide-and-conquer evolutionary algorithm
  • Large graph database query method based on divide-and-conquer evolutionary algorithm
  • Large graph database query method based on divide-and-conquer evolutionary algorithm

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Experimental program
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Effect test

Embodiment 1

[0067] Such as figure 1 , figure 2 , image 3 As shown, a large-scale graph database query method based on a divide-and-conquer evolutionary algorithm in this embodiment includes the following steps:

[0068] (1) Randomly initialize each particle in the entire particle swarm. Since the algorithm can integrate different meta-heuristic algorithms, the initialization strategy can be designed according to different meta-heuristic algorithms.

[0069] (2) Calculate the fitness of each particle in the population. Set gbest to the best particle in the population. The calculation of particle fitness is calculated according to the following equation:

[0070]

[0071] where L q Represents all the edges in the query graph, and l belongs to one of the edges. Γ L (l) represents the path that edge l maps to the data graph. The symbol |.| represents the length of the path. l WV (l) represents the weight of the path. The goal of the algorithm is to optimize this value.

[007...

Embodiment 2

[0095] In one embodiment, a multi-level k-way partitioning method is used to divide the query graph Q into k mutually exclusive sub-query graphs of similar size, and the cutting edge between each sub-graph is small.

[0096] In this embodiment, according to the given number of subgraphs sub_num, the query graph is divided into sub_num mutually exclusive subgraphs of similar size. The value of the number of subgraphs sub_num should balance the size of the subgraphs and the dependencies between subgraphs. A larger number means that the size of each subgraph will be small, and the dependence between each subgraph will increase. Based on the above analysis, sub_num is calculated according to the following formula:

[0097]

[0098] where G q Indicates the query graph, sizeof(G q ) represents the number of nodes in the query graph, ρ is used to describe the trend of sub_num, in overlapping decomposition, set it to 10, and in mutual exclusion decomposition, set it to 15, s is a ...

Embodiment 3

[0101] In yet another embodiment, the key nodes of each subgraph are detected by the following rules:

[0102] First, only the nodes connected to a certain subgraph can be the candidate key nodes of this subgraph. Then, if the subgraph has many candidate key nodes, only a part of them can be selected as key nodes. Therefore, some method is needed to select these nodes. Different connection strengths of different connected nodes in a subgraph. In order to detect the connection strength of different nodes, key nodes are selected by CS metric method.

[0103] The CS measurement method not only considers the connection number of nodes, but also considers the connection weights. The more connections a node has and the greater its weight, the greater the connection strength of this node. After the calculation of the CS metric method, it is necessary to sort the CS metric value of each node, and then select the top max_OL nodes, and set these nodes as key nodes. Then the scope o...

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Abstract

The invention discloses a large graph database query method based on a divide-and-conquer evolutionary algorithm. The method comprises the following steps: 1) randomly initializing each particle in a whole particle population; 2) finding out an optimal particle in the population; (3) decomposing the query graph into mutually exclusive sub-query graphs, and outwards extending the range of each sub-query graph by using an overlapping decomposition strategy; (4) mapping is carried out on each sub query graph; 5) complementing the sub-solution obtained after mapping of each sub-query graph, and calculating the fitness of the complemented sub-solution; 6) updating the optimal particle in the population, and if the fitness of the currently found sub-solution is better than that of the optimal particle, setting the optimal particle as the current solution; (7) if all the sub-graphs are optimized, entering the step (8), otherwise, turning to the step (4), and optimizing the next sub-graph; 8) constructing a plurality of competition nodes for overlapped nodes between the sub-graphs; and 9) ending iteration, if a termination condition is met, ending, otherwise, returning to the step 4).

Description

technical field [0001] The invention relates to two major fields of large-scale graph database query and evolutionary algorithm, and mainly relates to a large-scale graph database query method based on divide-and-conquer evolutionary algorithm. [0002] technical background [0003] Graph database is an important tool for querying and modeling complex graph data. It has been widely used with the emergence of many applications such as social network, semantic web, and biological network. Especially in the past ten years, e-commerce has gradually matured. If e-commerce companies want to discover the products that users may like, they must analyze the purchase records of users on the website. Therefore, graph databases have been widely used again. At the same time, the data contained in a graph is also becoming more and more. [0004] Graph database query is a practical application case of the subgraph isomorphism problem. The subgraph isomorphism problem is to match a graph ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/901G06F16/903G06N3/00G06N3/12
Inventor 陈伟能李子星龚月姣郭晓琦
Owner SOUTH CHINA UNIV OF TECH