During the development of service-based software systems,Geospatial Service(GS)replacement is often performed,which requires the discovery of functionally similar services in service registries to replace failed servi...During the development of service-based software systems,Geospatial Service(GS)replacement is often performed,which requires the discovery of functionally similar services in service registries to replace failed services.Compared to real-time similarity computations,direct extraction of similar services from constructed similarity relationships can yield higher replacement efficiency.However,missing and inconsistent service-registry information impedes accurate similarity relationship construction.Here,we propose a Graph Neural Network(GNN)-based model for GS Similarity Relationship construction considering service descriptions and tags,which is named GSSR-GNN.As the sparsity of the service similarity relationship graph constructed based on labeled samples limits the information propagation ability,a graph augmentation method for similarity relationship construction among second-order neighbors is proposed.Considering the differences in the semantic-information feature distributions,such as the service descriptions and tags,a feed-forward neural network-based fusion method is designed to embed them into the same vector space.Pre-trained Bidirectional Encoder Representations from Transformers(BERT)and WordNet models are introduced to enhance the service-representation expressiveness.When an enhanced service representation is input to the GNN,the similarity is calculated and the service similarity relationship is obtained.Experimental results show that the proposed model constructs service similarity relationships with high precision,thus improving the service replacement efficiency and reducing the computational cost of service registry during service replacement.展开更多
With the development of Internet and GIS,large volumes of spatial data,powerful computing resources and many spatial data processing functions are published in the form of Web services.Finding suitable geospatial serv...With the development of Internet and GIS,large volumes of spatial data,powerful computing resources and many spatial data processing functions are published in the form of Web services.Finding suitable geospatial services in the composition-oriented environment is a crucial task.The semantic Web provides a kind of technology to find and compose various service resources automatically through the Web.This paper proposes a hybrid method for the semantic matching of geospatial services.The method includes two parts.Part 1 puts forward a multi-level semantic matching approach,which matches single geospatial service at four levels:classification,input/output,precondition/effect and the quality of service(QoS).This multi-level matching approach makes single service matching quicker and more accurate.Part 2 puts forward a matching algorithm for a geospatial service chain based on the context.The algorithm adopts a trace algorithm,taking account of the effect of the context.It restricts the input/output parameters of the current service by the input/output parameters of service chain,pre-service and sub-service.It matches the atomic service dynamically in a composition-oriented environment,and accurately converts the abstract model of geospatial services into an executable geospatial service chain.A case study of the flood analysis for the Poyang Lake illustrates the effectiveness of our context-based matching method for geospatial services.展开更多
In recent years,Representational State Transfer(REST)has been proposed as the architectural style for the World Wide Web.REST promises of scalability and simple deployment of Web Services seem to be particularly appea...In recent years,Representational State Transfer(REST)has been proposed as the architectural style for the World Wide Web.REST promises of scalability and simple deployment of Web Services seem to be particularly appealing for Earth and Space Science(ESS)applications.In fact,most of the available solutions for geospatial data sharing,applying standard interoperability specifications,require complex service-oriented infrastructures;these are powerful and extensible environments,but they usually result in difficult to deploy and manage for ESS research teams.Thus,ESS researchers would gain great benefit from an easy way of sharing geo-information using the international interoperability standards.The variety and complexity of geo-information sharing services poses several architectural issues;in fact these services encompass sensor planning and observation,coverages and features publication and retrieving,models and simulations running,data citation and annotation.Consequently,the adoption of a specific architectural style must be carefully evaluated against these specific requirements.In this work we analyse the existing geospatial services from an architectural perspective and investigate their possible RESTful implementation.Particular attention is paid to the OGC Web Coverage Service(WCS).Possible benefits and drawbacks,along with open issues and possible solutions are discussed.Our investigation suggests that REST may fit well to the typical ESS research usage cases.However,the architectural choice(e.g.Simple Object Access Protocol(SOAP)vs REST)will depend on a case-by-case analysis.Other important factors must be considered,such as the application context:a valuable example in point are the e-Business and e-Government application scenarios which require message based solutions-like those implemented by SOAP.In any case,there is a clear need for harmonization and reconciliation of these two approaches.展开更多
A geospatial cyberinfrastructure is needed to support advanced GIScience research and education activities.However,the heterogeneous and distributed nature of geospatial resources creates enormous obstacles for buildi...A geospatial cyberinfrastructure is needed to support advanced GIScience research and education activities.However,the heterogeneous and distributed nature of geospatial resources creates enormous obstacles for building a unified and interoperable geospatial cyberinfrastructure.In this paper,we propose the Geospatial Service Web(GSW)to underpin the development of a future geospatial cyberinfrastructure.The GSW excels over the traditional spatial data infrastructure by providing a highly intelligent geospatial middleware to integrate various geospatial resources through the Internet based on interoperable Web service technologies.The development of the GSW focuses on the establishment of a platform where data,information,and knowledge can be shared and exchanged in an interoperable manner.Theoretically,we describe the conceptual framework and research challenges for GSW,and then introduce our recent research toward building a GSW.A research agenda for building a GSW is also presented in the paper.展开更多
t LBS （location-based service） is a remarkable outcome of the development from GIS to geospatial information service. Faced by the requirements of geospatial information from the masses and the opportunity provided ...t LBS （location-based service） is a remarkable outcome of the development from GIS to geospatial information service. Faced by the requirements of geospatial information from the masses and the opportunity provided by the next generation lnternet and Web 2.0, a new model of geospatial information service based on DMI （digital measurable image） is presented. First, the con- cept of LBS and the opportunities of Web 2.0 are introduced, then the characteristic of DMI is discussed. Taking the Image City.Wuhan as an example, the function ofgeospatial information service based on DM! is introduced. Finally, the feasibility for its industrialization is discussed.展开更多
With the increase of different sensors,applications and customers,the demand from data providers and users is for a new geospatial data service model,which supports low cost,high dexterity,and which would provide a co...With the increase of different sensors,applications and customers,the demand from data providers and users is for a new geospatial data service model,which supports low cost,high dexterity,and which would provide a comprehensive service.Based on such requirements and demands,the 21AT TripleSat constellation terminal and data delivery and management system has been developed by a Beijing based high-tech enterprise,Twenty First Century Aerospace Technology Co.,Ltd.(21AT).The company is the first commercial Earth observation satellite operator and service provider in China.This new geospatial data service model allows the user to directly access multi-source satellite data,manage the data order,and carry out automatic massive data production and delivery.The solution also implements safe and hierarchical user management,statistical data analysis,and automatic information reports.In addition,a mobile application is also available for users to easily access system functions.This new geospatial solution has already been successfully applied and installed in many customer sites in China,and is now available globally for international clients interested in fast geospatial solutions.It enables the success of customers’operational services.Besides providing TripleSat Constellation images,the multi-source data access system also allows the users to access other satellite data sources,based on customized agreement.This paper describes and discusses this new geospatial data service model.展开更多
With the complexity of the composition process and the rapid growth of candidate services,realizing optimal or near-optimal service composition is an urgent problem.Currently,the static service composition chain is ri...With the complexity of the composition process and the rapid growth of candidate services,realizing optimal or near-optimal service composition is an urgent problem.Currently,the static service composition chain is rigid and cannot be easily adapted to the dynamic Web environment.To address these challenges,the geographic information service composition(GISC) problem as a sequential decision-making task is modeled.In addition,the Markov decision process(MDP),as a universal model for the planning problem of agents,is used to describe the GISC problem.Then,to achieve self-adaptivity and optimization in a dynamic environment,a novel approach that integrates Monte Carlo tree search(MCTS) and a temporal-difference(TD) learning algorithm is proposed.The concrete services of abstract services are determined with optimal policies and adaptive capability at runtime,based on the environment and the status of component services.The simulation experiment is performed to demonstrate the effectiveness and efficiency through learning quality and performance.展开更多
The enhancement of computing power,the maturity of learning algorithms,and the richness of application scenarios make Artificial Intelligence(AI)solution increasingly attractive when solving Geo-spatial Information Sc...The enhancement of computing power,the maturity of learning algorithms,and the richness of application scenarios make Artificial Intelligence(AI)solution increasingly attractive when solving Geo-spatial Information Science(GSIS)problems.These include image matching,image target detection,change detection,image retrieval,and for generating data models of various types.This paper discusses the connection and synthesis between AI and GSIS in block adjustment,image search and discovery in big databases,automatic change detection,and detection of abnormalities,demonstrating that AI can integrate GSIS.Moreover,the concept of Earth Observation Brain and Smart Geo-spatial Service(SGSS)is introduced in the end,and it is expected to promote the development of GSIS into broadening applications.展开更多
Big Earth Data has experienced a considerable increase in volume in recent years due to improved sensing technologies and improvement of numerical-weather prediction models.The traditional geospatial data analysis wor...Big Earth Data has experienced a considerable increase in volume in recent years due to improved sensing technologies and improvement of numerical-weather prediction models.The traditional geospatial data analysis workflow hinders the use of large volumes of geospatial data due to limited disc space and computing capacity.Geospatial web service technologies bring new opportunities to access large volumes of Big Earth Data via the Internet and to process them at server-side.Four practical examples are presented from the marine,climate,planetary and earth observation science communities to show how the standard interface Web Coverage Service and its processing extension can be integrated into the traditional geospatial data workflow.Web service technologies offer a time-and cost-effective way to access multidimensional data in a user-tailored format and allow for rapid application development or time-series extraction.Data transport is minimised and enhanced processing capabilities are offered.More research is required to investigate web service implementations in an operational mode and large data centres have to become more progressive towards the adoption of geo-data standard interfaces.At the same time,data users have to become aware of the advantages of web services and be trained how to benefit from them most.展开更多
Cloud computing has been considered as the next-generation computing platform with the potential to address the data and computing challenges in geosciences.However,only a limited number of geoscientists have been ada...Cloud computing has been considered as the next-generation computing platform with the potential to address the data and computing challenges in geosciences.However,only a limited number of geoscientists have been adapting this platform for their scientific research mainly due to two barriers:1)selecting an appropriate cloud platform for a specific application could be challenging,as various cloud services are available and 2)existing general cloud platforms are not designed to support geoscience applications,algorithms and models.To tackle such barriers,this research aims to design a hybrid cloud computing(HCC)platform that can utilize and integrate the computing resources across different organizations to build a unified geospatial cloud computing platform.This platform can manage different types of underlying cloud infrastructure(e.g.,private or public clouds),and enables geoscientists to test and leverage the cloud capabilities through a web interface.Additionally,the platform also provides different geospatial cloud services,such as workflow as a service,on the top of common cloud services(e.g.,infrastructure as a service)provided by general cloud platforms.Therefore,geoscientists can easily create a model workflow by recruiting the needed models for a geospatial application or task on the fly.A HCC prototype is developed and dust storm simulation is used to demonstrate the capability and feasibility of such platform in facilitating geosciences by leveraging across-organization computing and model resources.展开更多
How to integrate and disseminate globally distributed multi-source and heterogeneous spatial information is an open problem in integration and sharing service of geographic information. Here we propose a new service a...How to integrate and disseminate globally distributed multi-source and heterogeneous spatial information is an open problem in integration and sharing service of geographic information. Here we propose a new service architecture suitable for integra- tion and sharing of distributed multi-source geographic information. We also propose a global virtual pyramid model, which can be applied in 3D virtual globes. In view of the difficulty of web multi-node geographic information sharing service, we propose a web multi-node service aggregation method, integrated in our autonomously developed virtual globe platform Geo- Globe and introduced in the National Platform for Common GeoSpatial Information Services named ＂T1ANDITU＂. It achieves 2D and 3D integration for geographic information service.展开更多
Finding the right spatially aware web service in a heterogeneous distributed environment using criteria such as service type,version,time,space,and scale has become a challenge in the integration of geospatial informa...Finding the right spatially aware web service in a heterogeneous distributed environment using criteria such as service type,version,time,space,and scale has become a challenge in the integration of geospatial information services.A new method for retrieving Open Geospatial Consortium(OGC)Web Service(OWS)that deals with this challenge using page crawling,link detection,service capability matching,and ontology reasoning,is described in this paper.Its major components are distributed OWS,the OWS search engine,the OWS ontology generator,the ontology-based OWS catalog service,and the ontology-based multi-protocol OWS client.Experimental results show that the execution time of this proposed method equals only 0.26 of that of Nutch’s method.In addition,the precision is much higher.Moreover,this proposed method can carry out complex OWS reasoning-based queries.It is being used successfully for the Antarctica multi-protocol OWS portal of the Geo-Information Web Service Portal of the Polar.展开更多
This paper proposed a geoscience model service integrated workflowbased rainstorm waterlogging analysis method to overcome the defects of conventional waterlogging analysis systems.In this research,we studied a genera...This paper proposed a geoscience model service integrated workflowbased rainstorm waterlogging analysis method to overcome the defects of conventional waterlogging analysis systems.In this research,we studied a general OGC WPS service invoking strategy,an automatic asynchronous invoking mechanism of WPS services in the BPEL workflow,and a distributed waterlogging analysis services integrated workflow to realize the reconstruction of a waterlogging analysis model based on the proposed method.The proposed method can make use of the flexible adjustment capability of the workflow and not only overcomes the inherent defects of conventional geoscience analysis methods but also realizes the integration and calculation of distributed geospatial data,models and computing resources automatically.The method has better construction convenience,execution reliability,extensibility and intelligence potential than a conventional method and has important value for dealing with more natural disasters and environmental challenges.展开更多
The emergence of Cloud Computing technologies brings a new information infrastructure to users.Providing geoprocessing functions in Cloud Computing platforms can bring scalable,on-demand,and costeffective geoprocessi...The emergence of Cloud Computing technologies brings a new information infrastructure to users.Providing geoprocessing functions in Cloud Computing platforms can bring scalable,on-demand,and costeffective geoprocessing services to geospatial users.This paper provides a comparative analysis of geoprocessing in Cloud Computing platformsMicrosoft Windows Azure and Google App Engine.The analysis compares differences in the data storage,architecture model,and development environment based on the experience to develop geoprocessing services in the two Cloud Computing platforms;emphasizes the importance of virtualization;recommends applications of hybrid geoprocessing Clouds,and suggests an interoperable solution on geoprocessing Cloud services.The comparison allows one to selectively utilize Cloud Computing platforms or hybrid Cloud pattern,once it is understood that the current development of geoprocessing Cloud services is restricted to specific Cloud Computing platforms with certain kinds of technologies.The performance evaluation is also performed over geoprocessing services deployed in public Cloud platforms.The tested services are developed using geoprocessing algorithms from different vendors,GeoSurf and Java Topology Suite.The evaluation results provide a valuable reference on providing elastic and cost-effective geoprocessing Cloud services.展开更多
基金supported by the National Natural Science Foundation of China[grant numbers41930107 and U20A2091].
文摘During the development of service-based software systems,Geospatial Service(GS)replacement is often performed,which requires the discovery of functionally similar services in service registries to replace failed services.Compared to real-time similarity computations,direct extraction of similar services from constructed similarity relationships can yield higher replacement efficiency.However,missing and inconsistent service-registry information impedes accurate similarity relationship construction.Here,we propose a Graph Neural Network(GNN)-based model for GS Similarity Relationship construction considering service descriptions and tags,which is named GSSR-GNN.As the sparsity of the service similarity relationship graph constructed based on labeled samples limits the information propagation ability,a graph augmentation method for similarity relationship construction among second-order neighbors is proposed.Considering the differences in the semantic-information feature distributions,such as the service descriptions and tags,a feed-forward neural network-based fusion method is designed to embed them into the same vector space.Pre-trained Bidirectional Encoder Representations from Transformers(BERT)and WordNet models are introduced to enhance the service-representation expressiveness.When an enhanced service representation is input to the GNN,the similarity is calculated and the service similarity relationship is obtained.Experimental results show that the proposed model constructs service similarity relationships with high precision,thus improving the service replacement efficiency and reducing the computational cost of service registry during service replacement.
基金supported by the National High Technology Research&Development Program of China("863"Program)(Grant No.2007AA12Z214)the National Natural Science Foundation of China(Grant No.40601083)
文摘With the development of Internet and GIS,large volumes of spatial data,powerful computing resources and many spatial data processing functions are published in the form of Web services.Finding suitable geospatial services in the composition-oriented environment is a crucial task.The semantic Web provides a kind of technology to find and compose various service resources automatically through the Web.This paper proposes a hybrid method for the semantic matching of geospatial services.The method includes two parts.Part 1 puts forward a multi-level semantic matching approach,which matches single geospatial service at four levels:classification,input/output,precondition/effect and the quality of service(QoS).This multi-level matching approach makes single service matching quicker and more accurate.Part 2 puts forward a matching algorithm for a geospatial service chain based on the context.The algorithm adopts a trace algorithm,taking account of the effect of the context.It restricts the input/output parameters of the current service by the input/output parameters of service chain,pre-service and sub-service.It matches the atomic service dynamically in a composition-oriented environment,and accurately converts the abstract model of geospatial services into an executable geospatial service chain.A case study of the flood analysis for the Poyang Lake illustrates the effectiveness of our context-based matching method for geospatial services.
文摘In recent years,Representational State Transfer(REST)has been proposed as the architectural style for the World Wide Web.REST promises of scalability and simple deployment of Web Services seem to be particularly appealing for Earth and Space Science(ESS)applications.In fact,most of the available solutions for geospatial data sharing,applying standard interoperability specifications,require complex service-oriented infrastructures;these are powerful and extensible environments,but they usually result in difficult to deploy and manage for ESS research teams.Thus,ESS researchers would gain great benefit from an easy way of sharing geo-information using the international interoperability standards.The variety and complexity of geo-information sharing services poses several architectural issues;in fact these services encompass sensor planning and observation,coverages and features publication and retrieving,models and simulations running,data citation and annotation.Consequently,the adoption of a specific architectural style must be carefully evaluated against these specific requirements.In this work we analyse the existing geospatial services from an architectural perspective and investigate their possible RESTful implementation.Particular attention is paid to the OGC Web Coverage Service(WCS).Possible benefits and drawbacks,along with open issues and possible solutions are discussed.Our investigation suggests that REST may fit well to the typical ESS research usage cases.However,the architectural choice(e.g.Simple Object Access Protocol(SOAP)vs REST)will depend on a case-by-case analysis.Other important factors must be considered,such as the application context:a valuable example in point are the e-Business and e-Government application scenarios which require message based solutions-like those implemented by SOAP.In any case,there is a clear need for harmonization and reconciliation of these two approaches.
基金This work is jointly supported by National Basic Research Program of China(Nos.2012CB719906 and 2011CB707105)National Natural Science Foundation of China(Nos.41023001,40801153 and 40901190).
文摘A geospatial cyberinfrastructure is needed to support advanced GIScience research and education activities.However,the heterogeneous and distributed nature of geospatial resources creates enormous obstacles for building a unified and interoperable geospatial cyberinfrastructure.In this paper,we propose the Geospatial Service Web(GSW)to underpin the development of a future geospatial cyberinfrastructure.The GSW excels over the traditional spatial data infrastructure by providing a highly intelligent geospatial middleware to integrate various geospatial resources through the Internet based on interoperable Web service technologies.The development of the GSW focuses on the establishment of a platform where data,information,and knowledge can be shared and exchanged in an interoperable manner.Theoretically,we describe the conceptual framework and research challenges for GSW,and then introduce our recent research toward building a GSW.A research agenda for building a GSW is also presented in the paper.
文摘t LBS （location-based service） is a remarkable outcome of the development from GIS to geospatial information service. Faced by the requirements of geospatial information from the masses and the opportunity provided by the next generation lnternet and Web 2.0, a new model of geospatial information service based on DMI （digital measurable image） is presented. First, the con- cept of LBS and the opportunities of Web 2.0 are introduced, then the characteristic of DMI is discussed. Taking the Image City.Wuhan as an example, the function ofgeospatial information service based on DM! is introduced. Finally, the feasibility for its industrialization is discussed.
基金supported by the project of Beijing Municipal Science and Technology Commission and Science and Technology Innovation Base of Cultivating and Developing Engineering[grant number Z161100005016069]the National High Technology Research and Development Program[grant number 2013AA12A303].
文摘With the increase of different sensors,applications and customers,the demand from data providers and users is for a new geospatial data service model,which supports low cost,high dexterity,and which would provide a comprehensive service.Based on such requirements and demands,the 21AT TripleSat constellation terminal and data delivery and management system has been developed by a Beijing based high-tech enterprise,Twenty First Century Aerospace Technology Co.,Ltd.(21AT).The company is the first commercial Earth observation satellite operator and service provider in China.This new geospatial data service model allows the user to directly access multi-source satellite data,manage the data order,and carry out automatic massive data production and delivery.The solution also implements safe and hierarchical user management,statistical data analysis,and automatic information reports.In addition,a mobile application is also available for users to easily access system functions.This new geospatial solution has already been successfully applied and installed in many customer sites in China,and is now available globally for international clients interested in fast geospatial solutions.It enables the success of customers’operational services.Besides providing TripleSat Constellation images,the multi-source data access system also allows the users to access other satellite data sources,based on customized agreement.This paper describes and discusses this new geospatial data service model.
基金Supported by the National Natural Science Foundation of China(No.41971356,41671400,41701446)National Key Research and Development Program of China(No.2017YFB0503600,2018YFB0505500)Hubei Province Natural Science Foundation of China(No.2017CFB277)。
文摘With the complexity of the composition process and the rapid growth of candidate services,realizing optimal or near-optimal service composition is an urgent problem.Currently,the static service composition chain is rigid and cannot be easily adapted to the dynamic Web environment.To address these challenges,the geographic information service composition(GISC) problem as a sequential decision-making task is modeled.In addition,the Markov decision process(MDP),as a universal model for the planning problem of agents,is used to describe the GISC problem.Then,to achieve self-adaptivity and optimization in a dynamic environment,a novel approach that integrates Monte Carlo tree search(MCTS) and a temporal-difference(TD) learning algorithm is proposed.The concrete services of abstract services are determined with optimal policies and adaptive capability at runtime,based on the environment and the status of component services.The simulation experiment is performed to demonstrate the effectiveness and efficiency through learning quality and performance.
基金This work was supported in part by the National key R and D plan on strategic international scientific and technological innovation cooperation special project[grant number 2016YFE0202300]the National Natural Science Foundation of China[grant number 61671332,41771452,51708426,41890820,41771454]+1 种基金the Natural Science Fund of Hubei Province in China[grant number 2018CFA007]the Independent Research Projects of Wuhan University[grant number 2042018kf0250].
文摘The enhancement of computing power,the maturity of learning algorithms,and the richness of application scenarios make Artificial Intelligence(AI)solution increasingly attractive when solving Geo-spatial Information Science(GSIS)problems.These include image matching,image target detection,change detection,image retrieval,and for generating data models of various types.This paper discusses the connection and synthesis between AI and GSIS in block adjustment,image search and discovery in big databases,automatic change detection,and detection of abnormalities,demonstrating that AI can integrate GSIS.Moreover,the concept of Earth Observation Brain and Smart Geo-spatial Service(SGSS)is introduced in the end,and it is expected to promote the development of GSIS into broadening applications.
基金the European Union’s Horizon 2020 Framework Programme research and innovation agreement[grant number 654367]。
文摘Big Earth Data has experienced a considerable increase in volume in recent years due to improved sensing technologies and improvement of numerical-weather prediction models.The traditional geospatial data analysis workflow hinders the use of large volumes of geospatial data due to limited disc space and computing capacity.Geospatial web service technologies bring new opportunities to access large volumes of Big Earth Data via the Internet and to process them at server-side.Four practical examples are presented from the marine,climate,planetary and earth observation science communities to show how the standard interface Web Coverage Service and its processing extension can be integrated into the traditional geospatial data workflow.Web service technologies offer a time-and cost-effective way to access multidimensional data in a user-tailored format and allow for rapid application development or time-series extraction.Data transport is minimised and enhanced processing capabilities are offered.More research is required to investigate web service implementations in an operational mode and large data centres have to become more progressive towards the adoption of geo-data standard interfaces.At the same time,data users have to become aware of the advantages of web services and be trained how to benefit from them most.
文摘Cloud computing has been considered as the next-generation computing platform with the potential to address the data and computing challenges in geosciences.However,only a limited number of geoscientists have been adapting this platform for their scientific research mainly due to two barriers:1)selecting an appropriate cloud platform for a specific application could be challenging,as various cloud services are available and 2)existing general cloud platforms are not designed to support geoscience applications,algorithms and models.To tackle such barriers,this research aims to design a hybrid cloud computing(HCC)platform that can utilize and integrate the computing resources across different organizations to build a unified geospatial cloud computing platform.This platform can manage different types of underlying cloud infrastructure(e.g.,private or public clouds),and enables geoscientists to test and leverage the cloud capabilities through a web interface.Additionally,the platform also provides different geospatial cloud services,such as workflow as a service,on the top of common cloud services(e.g.,infrastructure as a service)provided by general cloud platforms.Therefore,geoscientists can easily create a model workflow by recruiting the needed models for a geospatial application or task on the fly.A HCC prototype is developed and dust storm simulation is used to demonstrate the capability and feasibility of such platform in facilitating geosciences by leveraging across-organization computing and model resources.
基金supported by the National Natural Science Foundation of China(Grant No.41023001)National Basic Research Program of China(Grant No.2012CB719906)Innovative Research Groups Supported Project of the National Natural Science Foundation of China(Grant No.41021061)
文摘How to integrate and disseminate globally distributed multi-source and heterogeneous spatial information is an open problem in integration and sharing service of geographic information. Here we propose a new service architecture suitable for integra- tion and sharing of distributed multi-source geographic information. We also propose a global virtual pyramid model, which can be applied in 3D virtual globes. In view of the difficulty of web multi-node geographic information sharing service, we propose a web multi-node service aggregation method, integrated in our autonomously developed virtual globe platform Geo- Globe and introduced in the National Platform for Common GeoSpatial Information Services named ＂T1ANDITU＂. It achieves 2D and 3D integration for geographic information service.
基金This work has been supported in part by the National Basic Research Program of China(973 Program)under Grant 2011CB707101the National Natural Science Foundation of China under Grant 41023001,41021061the ShenZhen R&D Foundation under Grant CXB200903090023A.
文摘Finding the right spatially aware web service in a heterogeneous distributed environment using criteria such as service type,version,time,space,and scale has become a challenge in the integration of geospatial information services.A new method for retrieving Open Geospatial Consortium(OGC)Web Service(OWS)that deals with this challenge using page crawling,link detection,service capability matching,and ontology reasoning,is described in this paper.Its major components are distributed OWS,the OWS search engine,the OWS ontology generator,the ontology-based OWS catalog service,and the ontology-based multi-protocol OWS client.Experimental results show that the execution time of this proposed method equals only 0.26 of that of Nutch’s method.In addition,the precision is much higher.Moreover,this proposed method can carry out complex OWS reasoning-based queries.It is being used successfully for the Antarctica multi-protocol OWS portal of the Geo-Information Web Service Portal of the Polar.
基金funded by the National Key Research and Development Program of China[grant number 2018YFB2100504]the National Science Foundation of China(NSFC)[grant number 41871312]+4 种基金the National Key Research and Development Program of China[grant number 2017YFB0504202]the Fundamental Research Funds for the Central Universities[grant number 2042019kf0226]the Hubei Natural Science Foundation[grant number 2017CFB433]Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing[grant number KLIGIP-2017A09]the Beijing Key Laboratory of Urban Spatial Information Engineering[grant number 2017209].
文摘This paper proposed a geoscience model service integrated workflowbased rainstorm waterlogging analysis method to overcome the defects of conventional waterlogging analysis systems.In this research,we studied a general OGC WPS service invoking strategy,an automatic asynchronous invoking mechanism of WPS services in the BPEL workflow,and a distributed waterlogging analysis services integrated workflow to realize the reconstruction of a waterlogging analysis model based on the proposed method.The proposed method can make use of the flexible adjustment capability of the workflow and not only overcomes the inherent defects of conventional geoscience analysis methods but also realizes the integration and calculation of distributed geospatial data,models and computing resources automatically.The method has better construction convenience,execution reliability,extensibility and intelligence potential than a conventional method and has important value for dealing with more natural disasters and environmental challenges.
基金This work was funded jointly by National Basic Research Program of China(2011CB707105)Project 41271397 and 41023001 supported by NSFCLIESMARS(Wuhan University)Special Research Funding.
文摘The emergence of Cloud Computing technologies brings a new information infrastructure to users.Providing geoprocessing functions in Cloud Computing platforms can bring scalable,on-demand,and costeffective geoprocessing services to geospatial users.This paper provides a comparative analysis of geoprocessing in Cloud Computing platformsMicrosoft Windows Azure and Google App Engine.The analysis compares differences in the data storage,architecture model,and development environment based on the experience to develop geoprocessing services in the two Cloud Computing platforms;emphasizes the importance of virtualization;recommends applications of hybrid geoprocessing Clouds,and suggests an interoperable solution on geoprocessing Cloud services.The comparison allows one to selectively utilize Cloud Computing platforms or hybrid Cloud pattern,once it is understood that the current development of geoprocessing Cloud services is restricted to specific Cloud Computing platforms with certain kinds of technologies.The performance evaluation is also performed over geoprocessing services deployed in public Cloud platforms.The tested services are developed using geoprocessing algorithms from different vendors,GeoSurf and Java Topology Suite.The evaluation results provide a valuable reference on providing elastic and cost-effective geoprocessing Cloud services.
