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Neighbor-base similarity matching for graphs

机译:对图形的邻居相似性匹配

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The rapid development of internet and data centers has made cloud data management a major issue in database management system. Various cloud data management related applications require the basic operation of graph pattern matching. Exact matching method for graph pattern matching is too restrictive and it incurs very high computational cost as an NP-complete problem. Thus, it cannot be applied to most cloud applications. So several approximate notions are proposed. However, traditional approximate matching methods are still too restrictive in some situations, and some of them may neglect important nodes in the pattern. To address these problems, we propose a novel notion for graph pattern matching, and show that it can be processed in polynomial time. In addition, our method is flexible, free of thresholds and does not leave out any node in the pattern.
机译:互联网和数据中心的快速发展使云数据管理数据库管理系统中的一个主要问题。各种云数据管理相关应用程序需要图形模式匹配的基本操作。图案模式匹配的完全匹配方法过于限制性,并且它将非常高的计算成本作为NP完整问题。因此,它不能应用于大多数云应用程序。所以提出了几个近似概念。然而,传统的近似匹配方法在某些情况下仍然过于限制,其中一些可能忽略模式中的重要节点。为了解决这些问题,我们提出了一种关于图形模式匹配的小说概念,并表明它可以在多项式时间中处理。此外,我们的方法是灵活的,没有阈值,不会释放模式中的任何节点。

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