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
收藏
页码：1961 / 1964
页数：4
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