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K-nn Queries in Graph Databases Using M-Trees

机译:使用M树在图数据库中进行K-nn查询

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Metric trees (m-trees) are used to organize and execute fast queries on large databases. In classical schemes based on m-trees, routing information kept in an m-tree node includes a representative or a prototype to describe the sub-cluster. Several research has been done to apply m-trees to databases of attributed graphs. In these works routing elements are selected graphs of the sub-clusters. In the current paper, we propose to use Graph Metric Trees to improve k-nn queries. We present two types of Graph Metric Trees. The first uses a representative (Set Median Graph) as routing information; the second uses a graph prototype. Experimental validation shows that it is possible to improve k-nn queries using m-trees when noise between graphs of the same class is of reasonable level.
机译:度量树(m树)用于在大型数据库上组织和执行快速查询。在基于m树的经典方案中,保留在m树节点中的路由信息​​包括描述子集群的代表或原型。已经进行了一些研究来将m树应用于属性图的数据库。在这些作品中,路由元素是子集群的选定图形。在当前的论文中,我们建议使用图度量树来改进k-nn查询。我们介绍两种类型的图度量树。第一种使用代表(Set Median Graph)作为路由信息;第二个使用图原型。实验验证表明,当同一类图之间的噪声处于合理水平时,可以使用m树改进k-nn查询。

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