An Expressive Hadoop MapReduce Framework

被引：0
|
作者：
Shah, Nathar [1 ,3 ]
Messom, Christopher [2 ]
机构：
[1] Multimedia Univ, Fac Comp & Informat, Cyberjaya 63100, Malaysia
[2] Monash Univ, Sch Informat Technol, Clayton, Vic 3800, Australia
[3] Monash Univ, Sch Informat Technol, Bandar Sunway 47500, Malaysia
来源：
ADVANCED SCIENCE LETTERS | 2017年 / 23卷 / 11期
关键词：
Expressive; Hadoop MapReduce; Parallel Trees;
D O I：
10.1166/asl.2017.10250
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
The traditional Hadoop MapReduce framework is a simple programming model for large scale parallel and distributed data processing. However, the model is not structured for semantic-oriented large data processing since it is not expressive. This paper presents a tree-oriented approach to enable expressiveness in the traditional Hadoop MapReduce framework. The new tree based MapReduce structure provides for group based processing, level based processing, and traversal order based processing. Stand-alone or nested, these processing constructs provides the required expressivity for semantic-oriented large data processing. This is accomplished yet preserving the fundamental benefit of traditional MapReduce framework-fault-tolerant processing.
引用
收藏
页码：11197 / 11201
页数：5
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