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Geospatial cloud computing and big data
CEUS-01048; No of Page 1 Computers, Environment and Urban Systems xxx (2016) xxx
Contents lists available at ScienceDirect
Computers, Environment and Urban Systems journal homepage: www.elsevier.com/locate/ceus
Editorial
Geospatial cloud computing and big data
Big data and cloud computing emerge as two popular frontiers in information technology and the geospatial communities (Yang, Xu, & Nebert, 2013). This special issue originates from several sessions organized at annual meetings of AAG, ESIP, AGU and the international GIScience conference to capture the latest advancements on using cloud computing to tackle big geospatial data challenges. Seeing rapid growth of the two directions and the interactions between them, Professor Jean-Claude Thill, editor of CEUS, welcomed this special issue in 2012. The call for paper was distributed in late 2012 via a general announcement and to representative authors based on the session presentations. Approximately 35 abstracts were received and 15 invited manuscripts were submitted. Through a double blinded peer review process, seven papers were accepted for final publication. In the past four years, big data and cloud computing are accelerating in growth and becoming self-sustaining, dominant research domains. To capture what has been achieved and what remains to be investigated, I invited several emerging leaders specializing on different aspects of cloud computing and big data to co-author a survey paper on innovation opportunities and challenges for big data and cloud computing (Yang et al. this issue). The selected papers cover a broad domain including transportation, climate, remote sensing, end user profiling, data access, gazetteer and projection. Zhou et al. (2015) proposed an efficient data processing framework for mining massive trajectories of moving objects using big taxi GPS data as an example. Li, Yang, et al. (2014) offered a framework to facilitate the preparing, running, archiving, and visualization of climate modelling in a Model as a Service (MaaS) fashion. Xia, Karimi, and Meng (2014) proposed a cloud based parallelization approach for Kaufman clustering to better handle remote sensing data. Li, Feng, et al. (2014) utilized the user profile and access patterns to build a distributed high-speed caching system for high-performance geospatial data service. Gao, Li, Li, Janowicz, and Zhang (2014) developed a Hadoop and big volunteered social media data to construct gazetteers, while Tang and Feng, 2014 utilized cloud computing and GPU to parallelize re-projection process for big vector data. Finally, Schnase et al. (2014) proposed using cloud computing to enable climate analytics as a service to address big data challenges in climate sciences. From a big data process perspective, the papers covered data preparation, data mining, data service, and data management and phenomena simulation. Professor Jean-Claude Thill provided valuable assistance with handling the review of submitted manuscripts and managed the review process for the survey paper. I hope you find the papers challenging
and timely as represent the leading edge of cloud computing and big data practice in the past few years and point to the future research directions in this emerging field. References Gao, S., Li, L., Li, W., Janowicz, K., & Zhang, Y. (2014). Constructing gazetteers from volunteered big geo-data based on Hadoop. Computers, Environment and Urban Systems. http://dx.doi.org/10.1016/j.compenvurbsys.2014.02.004. Li, R., Feng, W., Wu, H., & Huang, Q. (2014a). A replication strategy for a distributed high-speed caching system based on spatiotemporal access patterns of geospatial data. Computers, Environment and Urban Systems. http://dx.doi.org/ 10.1016/j.compenvurbsys.2014.02.009. Li, Z., Yang, C., Huang, Q., Liu, K., Sun, M., & Xia, J. (2014b). Building model as a service to support geosciences. Computers, Environment and Urban Systems. http://dx.doi.org/ 10.1016/j.compenvurbsys.2014.06.004. Schnase, J. L., Duffy, D. Q., Tamkin, G. S., Nadeau, D., Thompson, J. H., Grieg, C. M., ... Webster, W. P. (2014). MERRA analytic services: Meeting the big data challenges of climate science through cloud-enabled climate analytics-as-a-service. Computers, Environment and Urban Systems. http://dx.doi.org/10.1016/j.compenvurbsys.2013. 12.003. Tang, W., & Feng, W. (2014). Parallel map projection of vector-based big spatial data: Coupling cloud computing with graphics processing units. Computers, Environment and Urban Systems. http://dx.doi.org/10.1016/j.compenvurbsys.2014.01.001. Xia, H., Karimi, H. A., & Meng, L. (2014). Parallel implementation of Kaufman's initialization for clustering large remote sensing images on clouds. Computers, Environment and Urban Systems.. http://dx.doi.org/10.1016/j.compenvurbsys.2014.06.002. Yang, C., Li, Z., Huang, Q., Liu, K., & Hu, F. (2016). Cloud computing and big data: Opportunities and challenges for innovation. Computers, Environment and Urban Systems (this issue). Yang, C., Xu, Y., & Nebert, D. (2013). Redefining the possibility of digital earth and geosciences with spatial cloud computing. International Journal of Digital Earth, 6(4), 297–312. Zhou, Y., Zhang, Y., Ge, Y., Xue, Z., Fu, Y., Guo, D., ... Li, J. (2015). An efficient data processing framework for mining the massive trajectory of moving objects. Computers, Environment and Urban Systems. http://dx.doi.org/10.1016/j.compenvurbsys.2015. 03.004.
Chaowei Phil Yang NSF Spatiotemporal Innovation Center, and the Center for Intelligent Spatial Computing, United States Geography and Geoinformation Sciences, College of Science, George Mason University, Fairfax, VA 22030–4444, United States. URL: http://www.stcenter.net/
Available online xxxx
http://dx.doi.org/10.1016/j.compenvurbsys.2016.05.001 0198-9715/© 2016 Elsevier Ltd. All rights reserved.
Please cite this article as: Yang, C.P., Geospatial cloud computing and big data, Computers, Environment and Urban Systems (2016), http:// dx.doi.org/10.1016/j.compenvurbsys.2016.05.001
Contents lists available at ScienceDirect
Computers, Environment and Urban Systems journal homepage: www.elsevier.com/locate/ceus
Editorial
Geospatial cloud computing and big data
Big data and cloud computing emerge as two popular frontiers in information technology and the geospatial communities (Yang, Xu, & Nebert, 2013). This special issue originates from several sessions organized at annual meetings of AAG, ESIP, AGU and the international GIScience conference to capture the latest advancements on using cloud computing to tackle big geospatial data challenges. Seeing rapid growth of the two directions and the interactions between them, Professor Jean-Claude Thill, editor of CEUS, welcomed this special issue in 2012. The call for paper was distributed in late 2012 via a general announcement and to representative authors based on the session presentations. Approximately 35 abstracts were received and 15 invited manuscripts were submitted. Through a double blinded peer review process, seven papers were accepted for final publication. In the past four years, big data and cloud computing are accelerating in growth and becoming self-sustaining, dominant research domains. To capture what has been achieved and what remains to be investigated, I invited several emerging leaders specializing on different aspects of cloud computing and big data to co-author a survey paper on innovation opportunities and challenges for big data and cloud computing (Yang et al. this issue). The selected papers cover a broad domain including transportation, climate, remote sensing, end user profiling, data access, gazetteer and projection. Zhou et al. (2015) proposed an efficient data processing framework for mining massive trajectories of moving objects using big taxi GPS data as an example. Li, Yang, et al. (2014) offered a framework to facilitate the preparing, running, archiving, and visualization of climate modelling in a Model as a Service (MaaS) fashion. Xia, Karimi, and Meng (2014) proposed a cloud based parallelization approach for Kaufman clustering to better handle remote sensing data. Li, Feng, et al. (2014) utilized the user profile and access patterns to build a distributed high-speed caching system for high-performance geospatial data service. Gao, Li, Li, Janowicz, and Zhang (2014) developed a Hadoop and big volunteered social media data to construct gazetteers, while Tang and Feng, 2014 utilized cloud computing and GPU to parallelize re-projection process for big vector data. Finally, Schnase et al. (2014) proposed using cloud computing to enable climate analytics as a service to address big data challenges in climate sciences. From a big data process perspective, the papers covered data preparation, data mining, data service, and data management and phenomena simulation. Professor Jean-Claude Thill provided valuable assistance with handling the review of submitted manuscripts and managed the review process for the survey paper. I hope you find the papers challenging
and timely as represent the leading edge of cloud computing and big data practice in the past few years and point to the future research directions in this emerging field. References Gao, S., Li, L., Li, W., Janowicz, K., & Zhang, Y. (2014). Constructing gazetteers from volunteered big geo-data based on Hadoop. Computers, Environment and Urban Systems. http://dx.doi.org/10.1016/j.compenvurbsys.2014.02.004. Li, R., Feng, W., Wu, H., & Huang, Q. (2014a). A replication strategy for a distributed high-speed caching system based on spatiotemporal access patterns of geospatial data. Computers, Environment and Urban Systems. http://dx.doi.org/ 10.1016/j.compenvurbsys.2014.02.009. Li, Z., Yang, C., Huang, Q., Liu, K., Sun, M., & Xia, J. (2014b). Building model as a service to support geosciences. Computers, Environment and Urban Systems. http://dx.doi.org/ 10.1016/j.compenvurbsys.2014.06.004. Schnase, J. L., Duffy, D. Q., Tamkin, G. S., Nadeau, D., Thompson, J. H., Grieg, C. M., ... Webster, W. P. (2014). MERRA analytic services: Meeting the big data challenges of climate science through cloud-enabled climate analytics-as-a-service. Computers, Environment and Urban Systems. http://dx.doi.org/10.1016/j.compenvurbsys.2013. 12.003. Tang, W., & Feng, W. (2014). Parallel map projection of vector-based big spatial data: Coupling cloud computing with graphics processing units. Computers, Environment and Urban Systems. http://dx.doi.org/10.1016/j.compenvurbsys.2014.01.001. Xia, H., Karimi, H. A., & Meng, L. (2014). Parallel implementation of Kaufman's initialization for clustering large remote sensing images on clouds. Computers, Environment and Urban Systems.. http://dx.doi.org/10.1016/j.compenvurbsys.2014.06.002. Yang, C., Li, Z., Huang, Q., Liu, K., & Hu, F. (2016). Cloud computing and big data: Opportunities and challenges for innovation. Computers, Environment and Urban Systems (this issue). Yang, C., Xu, Y., & Nebert, D. (2013). Redefining the possibility of digital earth and geosciences with spatial cloud computing. International Journal of Digital Earth, 6(4), 297–312. Zhou, Y., Zhang, Y., Ge, Y., Xue, Z., Fu, Y., Guo, D., ... Li, J. (2015). An efficient data processing framework for mining the massive trajectory of moving objects. Computers, Environment and Urban Systems. http://dx.doi.org/10.1016/j.compenvurbsys.2015. 03.004.
Chaowei Phil Yang NSF Spatiotemporal Innovation Center, and the Center for Intelligent Spatial Computing, United States Geography and Geoinformation Sciences, College of Science, George Mason University, Fairfax, VA 22030–4444, United States. URL: http://www.stcenter.net/
Available online xxxx
http://dx.doi.org/10.1016/j.compenvurbsys.2016.05.001 0198-9715/© 2016 Elsevier Ltd. All rights reserved.
Please cite this article as: Yang, C.P., Geospatial cloud computing and big data, Computers, Environment and Urban Systems (2016), http:// dx.doi.org/10.1016/j.compenvurbsys.2016.05.001