Efficient Top-k Result Diversification for Mobile Sensor Data
Efficient Top-k Result Diversification for Mobile Sensor Data
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DOI：
10.1109/icdcs.2016.27
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发表时间：
2016-06
期刊：
影响因子：
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通讯作者：
Masahiro Yokoyama;T. Hara
中科院分区：
文献类型：
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作者：
Masahiro Yokoyama;T. Hara
由于传感器技术的最新发展，移动传感器设备的使用已经变得普遍，许多研究人员一直在试图利用这些设备收集的数据。我们称这种数据为“移动传感器数据”。移动传感器数据是具有环境属性值的地理参考数据，通过检索环境属性值相对极端的数据（如较高的空气污染指数值），可以确定热点地区的地理分布。Top-k搜索结果在地理空间上的多样化对于此类应用是有效的。但是，数据项的偏好得分与每个用户的兴趣不同，必须从头开始为每个查询计算。在这种情况下，当移动传感器数据量非常大时，朴素方法的计算成本过高。因此，在本文中，我们提出了一种高效的移动传感器数据top-k搜索结果多样化方法。在朴素方法中，在寻找最佳数据时，需要扫描给定查询范围内存在的所有数据。然而，我们提出的方法可以通过利用集群信息来减少扫描数据量，从而可以更快地返回查询结果。此外，许多优化问题可以只用一个集群文件集来解决。实验结果表明，与原始方法相比，该方法缩短了计算时间，降低了磁盘IO开销。
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'. 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 (such as higher air-pollution index values) with comparatively extreme environmental attribute values. Top-k search result diversification in geographical space is valid for applications of this sort. However, the preference scores for data items are different from each user's interest, and must be calculated for each query from scratch. In this case, the computational cost of a naive method is excessively high when the amount of mobile sensor data is very large. Thus, in this paper, we propose an efficient top-k search result diversification method for mobile sensor data. In a naive method, it is necessary to scan all data existing in a given query range when seeking the best data. Our proposed method, however, can reduce the amount of scanned data by exploiting cluster information, and the query result can thereby be returned much more rapidly. Moreover, a number of optimization problems can be solved using only one cluster file set. Experimental results show that our proposed method involves short computation time and reduces the disk IO cost in comparison with a naive method.