海上交通数据空间大数据框架

Lei Bao, Yang Le
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引用次数: 2

摘要

为了对自动识别系统中的海上交通数据进行分析,本文提出了一个基于SpatialHadoop的大数据框架。该框架扩展了传统Hadoop的数据类型、存储、计算和操作层,纳入了海上定位数据。在存储层,引入了两层空间索引结构,可以在Hadoop HDFS存储上建立R-tree或R+ tree空间索引。并在Mapreduce编程中增加了两个新组件,使其适合于海洋空间数据的并行计算。基于这些提供的功能,我们可以对海上大位置数据建立各种空间分析操作,支持各种空间统计或空间数据挖掘应用
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Spatial Big Data Framework for Maritime Traffic Data
In order to analysis maritime traffic data from Automatic Identification System,this paper present a big data framework based on SpatialHadoop. This framework extend the data type, storage, computing and operation layer of traditional Hadoop to incorporate maritime location data. In storage layer, it introduce a two-layer spatial index structure which can establish R-tree or R+-tree spatial index on Hadoop Distributed File System(HDFS) storage. And it add two new components in Mapreduce programming,which make it fitful for parallel computing on maritime spatial data. Based on these function provided, we can build up various spatial analysis operation on big maritime location data, and support various spatial statistical or spatial data mining applications
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