On-Demand Big Data Analysis in Digital Repositories: A Lightweight Approach

被引：2
|
作者：
Xie, Zhiwu [1 ]
Chen, Yinlin [1 ]
Jiang, Tingting [1 ]
Speer, Julie [1 ]
Walters, Tyler [1 ]
Tarazaga, Pablo A. [2 ]
Kasarda, Mary [2 ]
机构：
[1] Virginia Polytech Inst & State Univ, Univ Lib, Blacksburg, VA 24061 USA
[2] Virginia Polytech Inst & State Univ, Dept Mech Engn, Blacksburg, VA 24061 USA
来源：
DIGITAL LIBRARIES: PROVIDING QUALITY INFORMATION | 2015年 / 9469卷
关键词：
Big data; Data management; Docker; Scholarly digital divide; Use and reuse driven approach;
D O I：
10.1007/978-3-319-27974-9_29
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We describe a use and reuse driven digital repository integrated with lightweight data analysis capabilities provided by the Docker framework. Using building sensor data collected from the Virginia Tech Goodwin Hall Living Laboratory, we perform evaluations using Amazon EC2 and Container Service with a Fedora 4 repository backed with storage in Amazon S3. The results confirm the viability and benefits of this approach.
引用
收藏
页码：274 / 277
页数：4
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