CEUS 2014 - Special Issue on Cloud Computing and Big Data for CEUS

Big Data are becoming national and international challenge for scientific advancements and application developments. Big Data are produced on a daily bases from Earth observations, social networks, in-situ sensors, model simulations, scientific research, application analyses, and many other ways. The data are big and complex in that they are not well structured and that many relationships among different data are buried by the volume of data or the way data are collected, modeled, or managed. To generate unprecedented information that can be processed from the Big Data, new computing and analyses methods are required to manage, discover, access, and process the Big Data for intuitive decision support of scientific research, application operations, and individual living.
The emerging of Cloud Computing as a new generation computing infrastructure provides potential computing solutions to the management, discovery, access, and processing of the Big Data for intuitive decision support knowledge. Cloud Computing and Big Data share similar intrinsic features, such as distribution, parallelization, space-time, and being geographically dispersed. Utilizing these intrinsic features would help provide Cloud Computing solutions for Big Data with computing infrastructure capability to process and obtain unprecedented information. At the same time, Big Data pose grand challenges as opportunities to advance Cloud Computing. In the geospatial information science domain, many scientists conducted active research to address urban, environment, social, climate, population, and other problems related to Big Data using Cloud Computing. This special issue of Computers, Environment, and Urban Systems (CEUS) is one of the first efforts to capture the latest adva
ncements in this direction and try to develop an initial research agenda for the domain of research. Topics solicited include but not limited to:
Education or vision of Cloud Computing and Big Data
Big Data mining using Cloud Computing
Cloud Computing tools, methods, technologies, and applications for Big Data
Cloud Computing research for collecting, processing, and visualizing Big Data
New computing architecture for processing Big Data
Interoperability Issues for sharing and managing Big Data
New methods for processing and visualizing Big Data
Big Data applications in social science, climate science, Earth science, and other science domains
Assessment of the types of problems amenable to Cloud Computing and Big Data
Implementation and optimization of Cloud Computing for Big Data
Any other research, development, and education related to Cloud Computing and Big Data
This CEUS special issue is to capture the cutting-edge research, development, education, and application of Cloud Computing and Big Data. The special issue is especially interested in “how Cloud Computing enabled Big Data processing for scientific research, application building, and educational activities” and “how Big Data challenges foster advancement, enablement, and optimization of Cloud Computing platforms”.
We invite extended 300-word abstract submissions, which will be reviewed to select full paper submissions, of which accepted papers will be published in early 2014 after the journal's peer review process.
About the journal
CEUS is a SCI journal with its latest impact factor of 1.795 in 2012. CEUS is an interdisciplinary journal publishing cutting-edge and innovative computer-based research on environmental and urban systems that privileges the geospatial perspective. The journal welcomes original high quality scholarship of a theoretical, applied or technological nature, and provides a stimulating presentation of perspectives, research developments, overviews of important new technologies and uses of major computational, information-based, and visualization innovations. Applied and theoretical contributions demonstrate the scope of computer-based analysis fostering a better understanding of environmental and urban systems, their spatial scope and their dynamics. Interdisciplinary perspectives are strongly encouraged. Application areas include environmental analysis, modeling and management, urban planning, economic development, emergency response and hazards, housing, land and resource managem
ent, infrastructure and facilities management, physical planning and urban design, transportation, business, and service planning. Examples of methodological approaches include geographic information systems, decision support systems, geocomputation, spatial statistical analysis, complex systems and artificial intelligence, visual analytics and geovisualization, ubiquitous computing, virtual rendering and simulation. Contributions emphasizing the development and enhancement of computer-based technologies for the analysis and modeling, policy formulation, planning, and management of environmental and urban systems that enhance sustainable futures are especially sought. The journal also encourages research on the modalities through which information and other computer-based technologies mold environmental and urban systems.
Deadlines
Feb., 15, 2013, abstract submission
Mar. 1, 2013, full paper submission invited
Jul.15, 2013, full paper submission
Nov.30, 2013, paper acceptance notification
Jan. 15, 2014, paper in final form
Mar.15, 2014, special issue published
Abstracts and inquiries are welcome and should be addressed to the guest editor:
Chaowei Yang (cyang3-AT-gmu.edu), Joint Center of Intelligent Spatial Computing for Water/Energy Sciences, College of Science, George Mason University, Fairfax, VA; http://cpgis.gmu.edu/homepage/
Recommended Readings:
1. Agrawal D., Das S., El Abbadi A., Big data and Cloud Computing: current state and future opportunities, Proceedings of the 14th International Conference on Extending Database Technology, March 21-24, 2011, Uppsala, Sweden. Doi:10.1145/1951365.1951432.
2. Armbrust, M, Fox, A., Griffith R., Joseph A., Katz, R. and etc, 2009. Above the Cloud: A Berkeley View of Cloud Computing, Technical Report No. UCB/EECS-2009-28.
3. LaValle S., Lesser E., Shockley R., Hopkins M.S., and Kruschwitz N., 2011. Big Data, Analytics, and the Path from Insights to Value, MITSloan Management Review, 52 (2): 20-31.
4. Lynch C., 2008. Big data: How do your data grow? Nature 455, 28-29, doi:10.1038/455028a.
5. NIST, Big Data and Cloud Computing Workshop, http://www.nist.gov/itl/cloud/, January 15-17, 2013.
6. Schadt E.E., Linderman M.D., Sorenson J., Lee L., Nolan G.P., 2010. Computational solutions to large-scale data management and analysis, Nature Reviews Genetics, 11, 647-657.
7. Yang C., Goodchild M., Huang Q., Nebert D., Raskin R., Xu Y., Bambacus M., Fay D., 2011, Spatial Cloud Computing: How Can Geospatial Sciences Use and Help to Shape Cloud Computing, International Journal of Digital Earth. DOI: 10.1080/17538947.2011.587547.
8. Yang C., Wu H., Li Z., Huang Q., Li J., 2011, Utilizing Spatial Principles to Optimize Distributed Computing for Enabling Physical Science Discoveries, Proceedings of National Academy of Sciences of the United States of America, 108(14), 5498-5503. http://www.pnas.org/content/early/2011/03/21/09093...