Efficient diversified set monitoring for mobile sensor stream environments
Efficient diversified set monitoring for mobile sensor stream environments
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DOI：
10.1109/bigdata.2017.8257964
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
Masahiro Yokoyama;T. Hara;S. Madria
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文献类型：
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作者：
Masahiro Yokoyama;T. Hara;S. Madria
由于传感器技术的最新发展，移动的传感器设备的使用已经变得广泛，并且许多研究人员一直在尝试利用由这些设备收集的数据。我们称这样的数据为“移动的传感器数据”;以及移动的传感器数据连续到达的环境，“移动的传感器流”环境。移动的传感器数据是具有环境属性值的地理参考数据;它们使我们能够通过检索具有相对极端的环境属性值（例如较高的空气污染指数值）的数据来确定热点的地理分布。地理空间中的Top-k搜索结果多样化对于这种应用是有效的。通过监测移动的传感器流的多样化集合，我们可以跟踪热点分布的变化。然而，当我们必须监控大量的移动的传感器数据时，维护这样多样化的集合的计算成本很高。因此，在本文中，我们提出了一个有效的多样化的集合监测方法的移动的传感器流环境。我们所提出的方法可以减少检查的数据量，通过利用我们提出的定期网格为基础的数据结构，和多样化的集合，从而可以更有效地维护。我们的实验结果证实，所提出的方法涉及更短的计算时间相比，基线方法。
Due to recent developments in sensor technologies, mobile sensor device use has become widespread, and many researchers have been attempting to leverage data collected by these devices. We call such data ‘mobile sensor data’; and environments where mobile sensor data arrive continuously, ‘mobile sensor stream’ environments. Mobile sensor data are geo-referenced data with environmental attribute values; and they enable us to determine the geographical distribution of hot spots by retrieving data with comparatively extreme environmental attribute values (such as higher air-pollution index values). Top-k search result diversification in geographical space is valid for applications of this sort. By monitoring a diversified set over mobile sensor streams, we can trace changes in the distribution of hot spots. However, the computation costs for maintaining such diversified sets are high when we have to monitor a large amount of mobile sensor data. Thus, in this paper, we propose an efficient diversified set monitoring method for mobile sensor stream environments. Our proposed method can reduce the amount of examined data by exploiting our proposed regular grid-based data structure, and the diversified set can thereby be maintained much more efficiently. Our experimental results confirm that the proposed method involves much shorter computation time in comparison with the baseline method.