MapReduce架构下的大规模轨迹数据压缩策略
发布时间：2019-07-20 11:27
【摘要】：车辆GPS轨迹数据中蕴含的轨迹信息具有重要的理论和应用价值.随着生活水平的日益提高,越来越多的汽车都配备了GPS设备,海量的GPS轨迹数据随之产生.为了减少车辆轨迹数据的存储空间,提高数据传输和数据分析速度,提出一种MapReduce架构下的大规模轨迹数据压缩策略.该策略首先提出一种基于综合时空特征的开放窗口轨迹数据压缩方法,再结合MapReduce并行计算模型,在各节点上并行压缩大规模轨迹数据.实验结果表明,本文提出的轨迹数据压缩策略虽然在压缩率上略有下降,但是保留了轨迹特征,减少了压缩误差,提高了压缩速度.
[Abstract]:The trajectory information contained in vehicle GPS trajectory data has important theoretical and application value. With the improvement of living standards, more and more cars are equipped with GPS equipment, and a large number of GPS trajectory data are produced. In order to reduce the storage space of vehicle trajectory data and improve the speed of data transmission and data analysis, a large-scale trajectory data compression strategy based on MapReduce architecture is proposed. In this strategy, an open window trajectory data compression method based on integrated space-time characteristics is proposed, and then combined with MapReduce parallel computing model, large-scale trajectory data is compressed in parallel on each node. The experimental results show that although the compression ratio of the trajectory data proposed in this paper decreases slightly, it preserves the trajectory characteristics, reduces the compression error and improves the compression speed.
【作者单位】： 上海理工大学光电信息与计算机工程学院;
【基金】：国家自然科学基金项目(61003031)资助 上海市自然科学基金项目(10ZR1421100)资助
【分类号】：U495
，
本文编号：2516693
[Abstract]:The trajectory information contained in vehicle GPS trajectory data has important theoretical and application value. With the improvement of living standards, more and more cars are equipped with GPS equipment, and a large number of GPS trajectory data are produced. In order to reduce the storage space of vehicle trajectory data and improve the speed of data transmission and data analysis, a large-scale trajectory data compression strategy based on MapReduce architecture is proposed. In this strategy, an open window trajectory data compression method based on integrated space-time characteristics is proposed, and then combined with MapReduce parallel computing model, large-scale trajectory data is compressed in parallel on each node. The experimental results show that although the compression ratio of the trajectory data proposed in this paper decreases slightly, it preserves the trajectory characteristics, reduces the compression error and improves the compression speed.
【作者单位】： 上海理工大学光电信息与计算机工程学院;
【基金】：国家自然科学基金项目(61003031)资助 上海市自然科学基金项目(10ZR1421100)资助
【分类号】：U495
，
本文编号：2516693
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