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DOI:	10.1371/journal.pone.0116781
论文题名:	Enabling Big Geoscience Data Analytics with a Cloud-Based, MapReduce-Enabled and Service-Oriented Workflow Framework
作者:	Zhenlong Li; Chaowei Yang; Baoxuan Jin; Manzhu Yu; Kai Liu; Min Sun; Matthew Zhan
刊名:	PLOS ONE
ISSN:	1932-6203
出版年:	2015
发表日期:	2015-3-5
卷:	10, 期:3
语种:	英语
英文关键词:	Earth sciences ; Data processing ; Cloud computing ; Data management ; Clouds ; Computer software ; Prototypes ; Climate modeling
英文摘要:	Geoscience observations and model simulations are generating vast amounts of multi-dimensional data. Effectively analyzing these data are essential for geoscience studies. However, the tasks are challenging for geoscientists because processing the massive amount of data is both computing and data intensive in that data analytics requires complex procedures and multiple tools. To tackle these challenges, a scientific workflow framework is proposed for big geoscience data analytics. In this framework techniques are proposed by leveraging cloud computing, MapReduce, and Service Oriented Architecture (SOA). Specifically, HBase is adopted for storing and managing big geoscience data across distributed computers. MapReduce-based algorithm framework is developed to support parallel processing of geoscience data. And service-oriented workflow architecture is built for supporting on-demand complex data analytics in the cloud environment. A proof-of-concept prototype tests the performance of the framework. Results show that this innovative framework significantly improves the efficiency of big geoscience data analytics by reducing the data processing time as well as simplifying data analytical procedures for geoscientists.
URL:	http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0116781&type=printable
Citation statistics:
资源类型:	期刊论文
标识符:	http://119.78.100.158/handle/2HF3EXSE/20217
Appears in Collections:	过去全球变化的重建影响、适应和脆弱性科学计划与规划气候变化与战略全球变化的国际研究计划气候减缓与适应气候变化事实与影响

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journal.pone.0116781.PDF（5424KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	View Download

作者单位:

NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, United States of America;NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, United States of America;NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, United States of America;Yunnan Provincial Geomatics Center, Yunnan Bureau of Surveying, Mapping, and GeoInformation, Kunming,Yunnan, China;NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, United States of America;NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, United States of America;NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, United States of America;NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, United States of America;Department of Computer Science, University of Texas—Austin, Austin, Texas, United States of America

Recommended Citation:

Zhenlong Li,Chaowei Yang,Baoxuan Jin,et al. Enabling Big Geoscience Data Analytics with a Cloud-Based, MapReduce-Enabled and Service-Oriented Workflow Framework[J]. PLOS ONE,2015-01-01,10(3)

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