薄荷视图:物化网络Top-k视图在传感器网络

D. Zeinalipour-Yazti, P. Andreou, Panos K. Chrysanthis, G. Samaras
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引用次数: 30

摘要

在本文中,我们介绍了MINT(物化网络top-k)视图,这是一个用于优化传感器网络中连续监控查询执行的新框架。典型的物化视图V维护查询Q的完整结果,以最小化未来查询执行的成本。在传感器网络环境中,维护V与底层和分布式基本关系R之间的一致性在通信方面是非常昂贵的。因此,我们的方法侧重于子集V(sub)。V),对于某些用户定义的参数k,它只揭示了sink中排名最高的k个答案。我们还详细描述了用于构建、修剪和维护这种递归定义的网络内视图的节能算法。我们对真实数据集的跟踪驱动实验表明,与其他主流数据采集模型相比,MINT可以显著降低能耗。
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MINT Views: Materialized In-Network Top-k Views in Sensor Networks
In this paper we introduce MINT (materialized in-network top-k) Views, a novel framework for optimizing the execution of continuous monitoring queries in sensor networks. A typical materialized view V maintains the complete results of a query Q in order to minimize the cost of future query executions. In a sensor network context, maintaining consistency between V and the underlying and distributed base relation R is very expensive in terms of communication. Thus, our approach focuses on a subset V(sube. V) that unveils only the k highest-ranked answers at the sink for some user defined parameter k. We additionally provide an elaborate description of energy-conscious algorithms for constructing, pruning and maintaining such recursively- defined in-network views. Our trace-driven experimentation with real datasets show that MINT offers significant energy reductions compared to other predominant data acquisition models.
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