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Range of Query Processing in Peer to Peer Networks

机译:对等网络中查询处理的范围

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Peer to peer databases are becoming prevalent on the Internet for distribution and sharing of documents, applications, and other digital media. The problem of answering large-scale ad hoc analysis queries, such as aggregation queries, these databases poses unique challenges. Exact solutions can be time consuming and difficult to implement, given the distributed and dynamic nature of P2P databases. In this paper, we present novel sampling-based techniques for approximate answering of ad hoc aggregation queries in such databases. Computing a high-quality random sample of the database efficiently in the P2P environment is complicated due to several factors: the data is distributed across many peers, within each peer, the data is often highly correlated, and, moreover, even collecting a random sample of the peers is difficult to accomplish. To counter these problems, we have developed an adaptive two-phase sampling approach based on random walks of the P2P graph, as well as block-level sampling techniques. We present extensive experimental evaluations to demonstrate the feasibility of our proposed solution.
机译:对等数据库在Internet上变得越来越普遍,用于文档,应用程序和其他数字媒体的分发和共享。这些数据库在回答大规模临时分析查询(例如聚合查询)时会遇到独特的挑战。鉴于P2P数据库的分布式和动态特性,精确的解决方案可能既耗时又难以实施。在本文中,我们提出了基于新颖的基于采样的技术来近似回答此类数据库中的即席聚合查询。由于以下几个因素,在P2P环境中有效地计算高质量的数据库随机样本非常复杂:数据分布在许多对等点之间,每个对等点内的数据通常高度相关,而且甚至收集随机样本的同行很难实现。为了解决这些问题,我们开发了一种基于P2P图的随机游动以及块级采样技术的自适应两阶段采样方法。我们提出了广泛的实验评估,以证明我们提出的解决方案的可行性。

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