Massive image data management using HBase and MapReduce

Yuehu Liu, Bin Chen, Wenxi He, Yu Fang
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引用次数: 19

Abstract

With the rapid development of remote sensing and computer technologies, remote sensing image data obtained by satellite isincreasing dramatically [1]. The speed has exceeded one TB each day and will obviously increase in the future. How to manage it efficiently becomes a problem because traditional waysare expensive and difficultto extend. Hence, we need a scalable and parallel processing model. HBaseand MapReduce meet the needs naturally. In this paper, we propose a method to store massive image data in HBase, and process it using MapReduce. Experimental results illustrate that the speeds of data importing and data processing increase obviously as the cluster of HBase grows.
使用HBase和MapReduce进行海量图像数据管理
随着遥感技术和计算机技术的快速发展,卫星获取的遥感图像数据急剧增加[1]。这一速度已经超过了每天1tb,未来还会有明显的增长。由于传统的管理方法昂贵且难以扩展,如何有效地管理它成为一个问题。因此,我们需要一个可伸缩的并行处理模型。hbase和MapReduce自然满足需求。本文提出了一种在HBase中存储海量图像数据的方法,并使用MapReduce对其进行处理。实验结果表明,随着HBase集群的增长,数据导入和数据处理的速度明显提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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