Accelerating Biomedical Data-Intensive Applications using MapReduce

被引：2

作者：

Han, Liangxiu ^{[1
]}

Ong, Hwee Yong ^{[2
]}

机构：

[1] Manchester Metropolitan Univ, Manchester M15 6BH, Lancs, England

[2] Univ Edinburgh, Edinburgh EH8 9YL, Midlothian, Scotland

来源：

2012 ACM/IEEE 13TH INTERNATIONAL CONFERENCE ON GRID COMPUTING (GRID) | 2012年

关键词：

Parallel processing; MapReduce; Cloud computing; Data mining application in Biomedical Science;

D O I：

10.1109/Grid.2012.24

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we investigate how MapReduce and Cloud computing can accelerate performance of applications and scale up the computing resources through a real data mining use case in the Biomedical Sciences. We have prototyped the data mining task using the MapReduce model and evaluated it in the Cloud. A performance evaluation model has been built for assessing the eff ciency of the prototype. The results, from both experiments and the evaluation model, show the performance and scalability can be enhanced through these advanced technologies.

引用

页码：49 / 57

页数：9

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