A spatiotemporal indexing approach for efficient processing of big array-based climate data with MapReduce-Reference-Cited by-同舟云学术

A spatiotemporal indexing approach for efficient processing of big array-based climate data with MapReduce

Published:2016-01-12 Issue:1 Volume:31 Page:17-35
ISSN:1365-8816
Container-title:International Journal of Geographical Information Science
language:en
Short-container-title:International Journal of Geographical Information Science

Author:

Li Zhenlong¹²,Hu Fei¹,Schnase John L.³,Duffy Daniel Q.⁴,Lee Tsengdar⁵,Bowen Michael K.⁴,Yang Chaowei¹

Affiliation:

1. NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, USA

2. Department of Geography, University of South Carolina, Columbia, SC, USA

3. Office of Computational and Information Sciences and Technology, NASA Goddard Space Flight Center, Greenbelt, MD, USA

4. NASA Center for Climate Simulation, Goddard Space Flight Center, Greenbelt, MD, USA

5. Earth Science Division, NASA Headquarters, Washington, DC, USA

Funder

This research was supported

Publisher

Informa UK Limited

Subject

Library and Information Sciences,Geography, Planning and Development,Information Systems

Link

https://www.tandfonline.com/doi/pdf/10.1080/13658816.2015.1131830

Reference27 articles.

1. Bloom, S. et al. 2005. Documentation and validation of the Goddard Earth Observing System (GEOS) data assimilation system - version 4. Technical Report Series on Global Modeling and Data Assimilation 104606, v26.

2. Anomaly detection

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