Retrieving the Relative Kernel Dataset from Big Sensory Data for Continuous Query
Retrieving the Relative Kernel Dataset from Big Sensory Data for Continuous Query
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DOI：
10.1007/978-3-319-94268-1_59
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
Tongxin Zhu;Jinbao Wang;Siyao Cheng;Yingshu Li;Jianzhong Li
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文献类型：
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作者：
Tongxin Zhu;Jinbao Wang;Siyao Cheng;Yingshu Li;Jianzhong Li
随着无线传感器网络（WSN）的快速发展，传感数据量呈现爆炸式增长。目前，一些无线传感器网络产生的传感数据超过TB或PB，已经超出了无线传感器网络的计算和传输能力。幸运的是，给定查询的有价值数据量通常很小。对于给定的查询Q，与其高度相关的数据集称为Q的相关核数据集。在本文中，我们研究了从大传感数据中检索相关内核数据集以进行连续查询的问题。理论分析和仿真结果表明，我们提出的算法在精度和资源消耗方面具有较高的性能。
With the rapid development of Wireless Sensor Networks (WSNs), the amount of sensory data manifests an explosive growth. Currently, the sensory data generated by some WSNs is more than terabytes or petabytes, which has already exceeded the computation and transmission abilities of a WSN. Fortunately, the volume of valuable data for a given query is usually small. For a given queryQ, the dataset which is highly related to it is called the relative kernel datasetofQ. In this paper, we study the problem of retrieving relative kernel dataset from big sensory data for continuous queries. The theoretical analysis and simulation results show that our proposed algorithms have high performance in term of accuracy and resource consumption.