Monitoring MaxRS in Spatial Data Streams
Monitoring MaxRS in Spatial Data Streams
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DOI：
10.5441/002/edbt.2016.30
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Daichi Amagata;T. Hara
中科院分区：
文献类型：
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作者：
Daichi Amagata;T. Hara
由于启用GPS的设备和许多基于位置的服务的增加，因此可以不断生成空间对象。本文解决了在空间数据流中监视MAXRS（最大化范围总和）的问题。给定一组加权空间（2维）对象，此问题是监视给定用户指定尺寸的矩形的位置，其中最大化了矩形对象的权重之和。许多现实生活中的应用程序从监视MaxR中获得了好处，例如，在Urban Sensing中的交通分析和事件检测，但是到目前为止尚未解决此问题。尽管已经提出了一些用于静态对象的算法，但是每当生成新对象时，执行这种算法在计算上都是昂贵的。这些激发了我们开发一种可以有效监视MAXR的有效算法。在本文中，我们首先设计了一种基于索引框架并逐步更新结果的基本算法。然后，我们增强算法，并表明增强算法可以处理错误保证的近似和监视TOP-K MAXR。我们的实验结果证实了我们方法的效率。
Due to the increase of GPS enabled devices and a lot of locationbased services, spatial objects are continuously generated. This paper addresses a problem of monitoring MaxRS (Maximizing Range Sum) in spatial data streams. Given a set of weighted spatial (2dimensional) objects, this problem is to monitor a location of a given user-specified sized rectangle where the sum of the weights of the objects covered by the rectangle is maximized. Many real life applications obtain a benefit from monitoring MaxRS, e.g., traffic analysis and event detection in urban sensing, but this problem has not yet been addressed so far. Although some algorithms for static objects have been proposed, executing such an algorithm whenever new objects are generated is computationally expensive. These motivate us to develop an efficient algorithm that can monitor MaxRS efficiently. In this paper, we first design a basic algorithm that is based on an index framework and incrementally updates the result. We then enhance the algorithm and show that the enhanced algorithm can deal with error-guaranteed approximation and monitoring top-k MaxRS. Our experimental results confirm the efficiency of our approach.