Parallel trajectory search based on distributed index
Parallel trajectory search based on distributed index
复制标题
基于分布式索引的并行轨迹搜索
DOI：
10.1016/j.ins.2017.01.016
复制
发表时间：
2017-05
影响因子：
8.1
通讯作者：
Belhassena Amina
中科院分区：
文献类型：
--
作者：
Wang Hongzhi;Belhassena Amina
由于公众可用的支持GPS的移动的设备中包含大量基于位置的信息，存在存储和查询处理的问题。轨迹搜索处理近年来受到越来越多的关注。为了组织庞大的信息，许多研究使用索引大型轨迹数据集。然而，现有的研究大多集中在一个集中的系统，而空间Hadoop提出了在分布式系统中通过MapReduce方法处理空间数据。但是，这种方法不适合在不将数据存储在磁盘上的情况下重用数据。因此，我们开发了一种新的R树索引在分布式系统中称为分布式轨迹R树（DTR-Tree）与Apache Spark基于MapReduce模型。此外，我们还研究了一个新的问题的分布式轨迹查询搜索活动。在给定查询q的情况下，轨迹的每个点所涉及的活动集合和距离阈值，则作为结果返回轨迹，该轨迹包括最小距离所需的所有活动。为了优化轨迹处理搜索，我们使用了一种新的策略分离较小的R-树，以获得子轨迹匹配的基础上的空间距离的q。本文提出了一种平衡的分布式索引DTR-Tree，以保证系统的可扩展性和容错性。实验结果表明，该算法具有较高的效率.
With the massive location-based information contained in public available GPS enabled mobile devices, there are problems of storage and queries processing. Trajectory search processing has received increasing attention in recent years. To organize the huge information, many studies have used indexing large trajectory data sets. However, the majority of existing studies have focused on a centralized system, while Spatial Hadoop is proposed to handle the spatial data by MapReduce method in a distributed system. However, this method is not suitable for reusing data without storing it on a disk. Therefore, we have developed a new R-tree index in the distributed system called the Distributed Trajectory R-Tree (DTR-Tree) with Apache Spark based on the MapReduce model. Furthermore, we have investigated a novel problem of a distributed trajectory query search with activities. Where a query q is given with a set of activities involved in each point of the trajectory and a threshold of distance, then the trajectory, which includes all the activities required with a minimal distance, is returned as a result. To optimize the trajectory processing search, we have used a novel strategy on separated smaller R-trees to obtain the sub-trajectories matching by q based on the spatial distance. In this paper, a balanced distributed index DTR-Tree is proposed to ensure the scalability and fault tolerance. Experimental results show the high efficiency of the proposed algorithms.
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DOI：
10.1109/ssdm.1999.787622
发表时间：
1999-07
期刊：
Proceedings. Eleventh International Conference on Scientific and Statistical Database Management
影响因子：
--
作者：
Bernd Schnitzer;Scott T. Leutenegger
通讯作者：
Bernd Schnitzer;Scott T. Leutenegger
DOI：
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发表时间：
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期刊：
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影响因子：
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作者：
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通讯作者：
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DOI：
10.1109/time.2007.59
发表时间：
2007-06
期刊：
14th International Symposium on Temporal Representation and Reasoning (TIME'07)
影响因子：
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作者：
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通讯作者：
N. Pelekis;Ioannis Kopanakis;Gerasimos Marketos;Eirini Ntoutsi;G. Andrienko;Y. Theodoridis
DOI：
10.1109/geoinformatics.2013.6626052
发表时间：
2013-06
期刊：
2013 21st International Conference on Geoinformatics
影响因子：
--
作者：
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通讯作者：
L. Wang;Bin Chen;Yuehu Liu
影响因子：
2
作者：
Martin Erwig;R. H. Güting;Markus Schneider;M. Vazirgiannis
通讯作者：
Martin Erwig;R. H. Güting;Markus Schneider;M. Vazirgiannis