A Demonstration of ST-Hadoop: A MapReduce Framework for Big Spatio-temporal Data

被引：16

作者：

Alarabi, Louai ^{[1
]}

Mokbel, Mohamed F. ^{[1
]}

机构：

[1] Univ Minnesota, Dept Comp Sci & Engn, Minneapolis, MN 55455 USA

来源：

PROCEEDINGS OF THE VLDB ENDOWMENT | 2017年 / 10卷 / 12期

关键词：

D O I：

10.14778/3137765.3137819

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This demo presents ST-Hadoop; the first full-fledged open-source MapReduce framework with a native support for spatio-temporal data. ST-Hadoop injects spatio-temporal awareness in the Hadoop base code, which results in achieving order(s) of magnitude better performance than Hadoop and SpatialHadoop when dealing with spatio-temporal data and queries. The key idea behind ST-Hadoop is its ability in indexing spatio-temporal data within Hadoop Distributed File System (HDFS). A real system prototype of ST-Hadoop, running on a local cluster of 24 machines, is demonstrated with two big-spatio-temporal datasets of Twitter and NYC Taxi data, each of around one billion records.

引用

下载

页码：1961 / 1964

页数：4

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