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A Scalable Multi-attribute Range Query Approach on Cluster-Based Hybrid Overlays

机译:基于聚类的混合叠加的可扩展多属性范围查询方法

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Resource discovery in distributed computing systems is a critical issue to find and retrieve distributed resources rapidly. In general, most of previous proposed strategies focus on developing keyword searching approaches with preserving system scalability. In this paper, we propose a cluster-based hybrid overlay, which supports efficient keyword searching with the highly churn rate. The cluster-based hybrid overlay groups the nodes with the same attributes to form unstructured attribute-groups, and then clusters these attribute-groups with similar attributes to form attribute-clusters. Our proposed hybrid overlay could provide efficient multi-attribute and range-query searches with load balancing in large-scale P2P networks. Experimental results show that the proposed overlay performs well.
机译:分布式计算系统中的资源发现是快速查找和检索分布式资源的关键问题。通常,大多数以前提出的策略都集中在开发具有系统可伸缩性的关键字搜索方法上。在本文中,我们提出了一种基于集群的混合覆盖,它支持以高流失率进行有效的关键字搜索。基于群集的混合覆盖将具有相同属性的节点分组以形成非结构化属性组,然后将具有相似属性的这些属性组进行群集以形成属性组。我们提出的混合覆盖可以在大型P2P网络中通过负载平衡提供有效的多属性和范围查询搜索。实验结果表明,提出的叠加效果良好。

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