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Processing generalized k-nearest neighbor queries on a wireless broadcast stream

机译:在无线广播流上处理广义k最近邻查询

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In this paper, we investigate the problem of processing generalized k-nearest neighbor (GkNN) queries, which involve both spatial and non-spatial specifications for data objects, in a wireless broadcasting system. We present a method for processing GkNN queries on the broadcast stream. In particular, we propose a novel R-tree variant index structure, called the bit-vector R-tree (bR-tree), which stores additional bit-vector information to describe non-spatial attribute values of the data objects. In addition, each node in the bR-tree stores only one pointer to its children, which makes the bR-tree compact. We generate the broadcast stream by multiplexing the bR-tree and the data objects in the broadcasting channel. The corresponding search algorithm for the broadcast stream is also described. Through a series of comprehensive simulation experiments, we prove the efficiency of the proposed method with regard to energy consumption, latency, and memory requirement, which are the major performance concerns in a wireless broadcasting system. Furthermore, we test the practicality of the proposed method in a real prototype system.
机译:在本文中,我们研究了在无线广播系统中处理涉及数据对象的空间和非空间规格的广义k最近邻(GkNN)查询的问题。我们提出了一种在广播流上处理GkNN查询的方法。特别是,我们提出了一种新颖的R树变量索引结构,称为位向量R树(bR树),该结构存储其他位向量信息以描述数据对象的非空间属性值。另外,bR树中的每个节点仅存储一个指向其子节点的指针,这使bR树变得紧凑。我们通过在广播频道中多路复用bR树和数据对象来生成广播流。还描述了广播流的相应搜索算法。通过一系列全面的仿真实验,我们证明了所提出方法在能耗,等待时间和内存需求方面的效率,这是无线广播系统的主要性能问题。此外,我们在实际的原型系统中测试了该方法的实用性。

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