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Utilize cloud computing to support dust storm forecasting

International Journal of Digital Earth 2013年第4期6卷 338-355页

作者： Qunying Huang Chaowei Yang Karl Benedict Songqing Chen Abdelmounaam Rezgui Jibo Xie Joint center for intelligent spatial computing Geography and Geoinformation ScienceGeorge Mason UniversityFairfaxVAUSA Earth Data Analysis center(EDAC) University of New MexicoAlbuquerqueNMUSA department of Computer science George Mason UniversityFairfaxVAUSA center for Earth Observation and Digital Earth BeijingChina

The simulations and potential forecasting of dust storms are of significant interest to public health and environment *** storms have interannual variabilities and are typical disruptive *** computing platform for a dust storm forecasting operational system should support a disruptive fashion by scaling up to enable high-resolution forecasting and massive public access when dust storms come and scaling down when no dust storm events occur to save energy and *** the capability of providing a large,elastic,and virtualized pool of computational resources,cloud computing becomes a new and advantageous computing paradigm to resolve scientific problems traditionally requiring a large-scale and high-performance *** paper examines the viability for cloud computing to support dust storm *** a holistic study by systematically comparing cloud computing using Amazon EC2 to traditional high performance computing(HPC)cluster,we find that cloud computing is emerging as a credible solution for(1)supporting dust storm forecasting in spinning off a large group of computing resources in a few minutes to satisfy the disruptive computing requirements of dust storm forecasting,(2)performing high-resolution dust storm forecasting when required,(3)supporting concurrent computing requirements,(4)supporting real dust storm event forecasting for a large geographic domain by using recent dust storm event in Phoniex,05 July 2011 as example,and(5)reducing cost by maintaining low computing support when there is no dust storm events while invoking a large amount of computing resource to perform high-resolution forecasting and responding to large amount of concurrent public accesses.

关键词： spatial cloud computing CyberGIS cloud GIS loosely coupled nested model Amazon EC2

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spatial cloud computing: how can the geospatial sciences use and help shape cloud computing?

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International Journal of Digital Earth 2011年第4期4卷 305-329页

作者： Chaowei Yang Michael Goodchild Qunying Huang Doug Nebert Robert Raskin Yan Xu Myra Bambacus Daniel Fay department of geography and geoinformation science and center for intelligent spatial computing George Mason UniversityFairfax VA 22030-4444USA department of geography University of California5707 Ellison HallSanta Barbara CA 93106-4060USA Federal Geographic Data Committee 590 National CenterReston VA 20192USA NASA Jet Propulsion Laboratory 4800 Oak Grove Drive PasadenaCA 91109USA Microsoft Research Connections MicrosoftRedmondWAUSA NASA Goddard Space Flight center Code 700GreenbeltMD 20771USA

The geospatial sciences face grand information technology(IT)challenges in the twenty-first century:data intensity,computing intensity,concurrent access intensity and spatiotemporal *** challenges require the readiness of a computing infrastructure that can:(1)better support discovery,access and utilization of data and data processing so as to relieve scientists and engineers of IT tasks and focus on scientific discoveries;(2)provide real-time IT resources to enable real-time applications,such as emergency response;(3)deal with access spikes;and(4)provide more reliable and scalable service for massive numbers of concurrent users to advance public *** emergence of cloud computing provides a potential solution with an elastic,on-demand computing platform to integrateobservation systems,parameter extracting algorithms,phenomena simulations,analytical visualization and decision support,and to provide social impact and user feedbackthe essential elements of the geospatial *** discuss the utilization of cloud computing to support the intensities of geospatial sciences by reporting from our investigations on how cloud computing could enable the geospatial sciences and how spatiotemporal principles,the kernel of the geospatial sciences,could be utilized to ensure the benefits of cloud *** research examples are presented to analyze how to:(1)search,access and utilize geospatial data;(2)configure computing infrastructure to enable the computability of intensive simulation models;(3)disseminate and utilize research results for massive numbers of concurrent users;and(4)adopt spatiotemporal principles to support spatiotemporal intensive *** paper concludes with a discussion of opportunities and challenges for spatial cloud computing(SCC).

关键词： digital earth Cyber GIS geodynamics space-time high-performance computing geospatial cyberinfrastructure

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Using geospatial social media data for infectious disease studies: a systematic review

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International Journal of Digital Earth 2023年第1期16卷 130-157页

作者： Fengrui Jing Zhenlong Li Shan Qiao Jiajia Zhang Banky Olatosi Xiaoming Li geoinformation and Big Data Research Laboratory Department of GeographyUniversity of South CarolinaColumbiaSCUSA Big Data Health science center University of South CarolinaColumbiaSCUSA department of Health Promotion Educationand BehaviorArnold School of Public HealthUniversity of South CarolinaColumbiaSCUSA department of Epidemiology and Biostatistics Arnold School of Public HealthUniversity of South CarolinaColumbiaSCUSA department of Health Services Policy and Management Arnold School of Public HealthUniversity of South CarolinaColumbiaSCUSA

Geospatial social media(GSM)data has been increasingly used in public health due to its rich,timely,and accessible spatial information,particularly in infectious disease *** review synthesized 86 research articles that use GSM data in infectious diseases published between December 2013 and March *** articles cover 12 infectious disease types ranging from respiratory infectious diseases to sexually transmitted diseases with spatial levels varying from the neighborhood,county,state,and *** categorized these studies into three major infectious disease research domains:surveillance,explanation,and *** the assistance of advanced computing,statistical and spatial methods,GSM data has been widely and deeply applied to these domains,particularly in surveillance and explanation *** further identified four knowledge gaps in terms of contextual information use,application scopes,spatiotemporal dimension,and data limitations and proposed innovation opportunities for future *** will contribute to a better understanding of using GSM data in infectious diseases studies and provide insights into strategies for using GSM data more effectively in future research.

关键词： Public health social media infectious diseases geography spatial analysis

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Towards intelligent geospatial data discovery:a machine learning framework for search ranking

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International Journal of Digital Earth 2018年第9期11卷 956-971页

作者： Yongyao Jiang Yun Li Chaowei Yang Fei Hu Edward MArmstrong Thomas Huang David Moroni Lewis J.McGibbney Christopher J.Finch NSF Spatiotemporal Innovation center and department of geography and geoinformation science George Mason UniversityFairfaxVAUSA NASA Jet Propulsion Laboratory California Institute of TechnologyPasadenaCAUSA

Current search engines in most geospatial data portals tend to induce users to focus on one single-data characteristic dimension(*** and release date).This approach largely fails to take account of users’multidimensional preferences for geospatial data,and hence may likely result in a less than optimal user experience in discovering the most applicable *** study reports a machine learning framework to address the ranking challenge,the fundamental obstacle in geospatial data discovery,by(1)identifying a number of ranking features of geospatial data to represent users’multidimensional preferences by considering semantics,user behavior,spatial similarity,and static dataset metadata attributes;(2)applying a machine learning method to automatically learn a ranking function;and(3)proposing a system architecture to combine existing search-oriented open source software,semantic knowledge base,ranking feature extraction,and machine learning *** show that the machine learning approach outperforms other methods,in terms of both precision at K and normalized discounted cumulative *** an early attempt of utilizing machine learning to improve the search ranking in the geospatial domain,we expect this work to set an example for further research and open the door towards intelligent geospatial data discovery.

关键词： Learning to rank semantic search user behavior search engine big data metadata data relevancy data portal

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Adopting cloud computing to optimize spatial web portals for better performance to support Digital Earth and other global geospatial initiatives

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International Journal of Digital Earth 2015年第6期8卷 451-475页

作者： Jizhe Xia Chaowei Yang Kai Liu Zhipeng Gui Zhenlong Li Qunying Huang Rui Li department of geography and geoinformation science NSF Spatiotemporal Innovation CenterGeorge Mason UniversityFairfaxVAUSA School of Remote Sensing and Information Engineering Wuhan UniversityWuhanHubeiP.R.China LIESMARS Wuhan UniversityWuhanHubeiP.R.China

A spatial web portal(SWP)provides a web-based gateway to discover,access,manage,and integrate worldwide geospatial resources through the Internet and has the access characteristics of regional to global interest and *** various technologies have been adopted to improve SWP performance,enabling high-speed resource access for global users to better support Digital Earth remains challenging because of the computing and communication intensities in the SWP operation and the dynamic distribution of end *** paper proposes a cloud-enabled framework for high-speed SWP access by leveraging elastic resource pooling,dynamic workload balancing,and global *** results demonstrate that the new SWP framework outperforms the traditional computing infrastructure and better supports users of a global system such as Digital *** methodologies and framework can be adopted to support operational geospatial systems,such as monitoring national geographic state and spanning across regional and global geographic extent.

关键词： CyberGIS/WebGIS GeoPortal spatial cloud computing spatiotemporal thinking and computing GEO national geographic state

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From Digitalized to intelligentized Surveying and Mapping: Fundamental Issues and Research Agenda

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Journal of Geodesy and geoinformation science 2022年第2期5卷 148-160页

作者： Jun CHEN Zhilin LI Songnian LI Wanzeng LIU Hao WU Li YAN National Geomatics center of China Beijing100830China Faculty of Geosciences&State-Province Joint Engineering Laboratory of spatial Information Technology for High-speed Railway Safety Southwest Jiaotong UniversityChengdu 611756China department of Land Surveying and Geo-Informatics Hong Kong Polytechnic UniversityHong Kong 999077China department of Civil Engineering Toronto Metropolitan UniversityToronto M5B 1E5Canada School of Geodesy and Geomatics Wuhan UniversityWuhan 430079China

Nowadays Surveying and Mapping(S&M)production and services are facing some serious challenges such as real-timization of data acquisition,automation of information processing,and intellectualization of service *** main reason is that current digitalized S&M technologies,which involve complex algorithms and models as the core,are incapable of completely describing and representing the diverse,multi-dimensional and dynamic real world,as well as addressing high-dimensional and nonlinear spatial problems using simple algorithms and *** order to address these challenges,it is necessary to explore the use of natural intelligence in S&M,and to develop intelligentized S&M technologies,which are knowledge-guided and *** paper first discusses the basic concepts and ideas of intelligentized S&M,and then analyzes and defines its fundamental issues in the analysis and modeling of natural intelligence in S&M,the construction and realization of hybrid intelligent computing paradigm,and the mechanism and path of empowering *** research directions are then proposed in the four areas,including knowledge systems,technologies and methodologies,application systems,and instruments and equipments of intelligentized S&***,some institutional issues related to promoting scientific research and engineering applications in this area are discussed.

关键词： Surveying and Mapping intelligentization natural intelligence hybrid intelligent computing

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