The quality of GIServices(QoGIS)is an important consideration for services sharing and interoperation.However,QoGIS is a complex concept and difficult to be evaluated reasonably.Most of the current studies have focuse...The quality of GIServices(QoGIS)is an important consideration for services sharing and interoperation.However,QoGIS is a complex concept and difficult to be evaluated reasonably.Most of the current studies have focused on static and non-scalable evaluation methods but have ignored location sensitivity subsequently resulting in the inaccurate QoGIS values.For intensive geodata and computation,GIServices are more sensitive to the location factor than general services.This paper proposes a location-aware GIServices quality prediction model via collaborative filtering(LAGCF).The model uses a mixed CF method based on time zone feature from the perspectives of both user and GIServices.Time zone is taken as the location factor and mapped into the prediction process.A time zone-adjusted Pearson correlation coefficient algorithm was designed to measure the similarity between the GIServices and the target,helping to identify highly similar GIServices.By adopting a coefficient of confidence in the final generation phase,the value of the QoGIS most similar to the target services will play a dominant role in the comprehensive result.Two series of experiments on large-scale QoGIS data were implemented to verify the effectivity of LAGCF.The results showed that LAGCF can improve the accuracy of QoGIS prediction significantly.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
基金National Natural Science Foundation of China[grant number 41401464]Open Foundation of LIESMARS[grant number 15I02]Natural Science Foundation of Hubei Province[grant number 2016CFC769].
文摘The quality of GIServices(QoGIS)is an important consideration for services sharing and interoperation.However,QoGIS is a complex concept and difficult to be evaluated reasonably.Most of the current studies have focused on static and non-scalable evaluation methods but have ignored location sensitivity subsequently resulting in the inaccurate QoGIS values.For intensive geodata and computation,GIServices are more sensitive to the location factor than general services.This paper proposes a location-aware GIServices quality prediction model via collaborative filtering(LAGCF).The model uses a mixed CF method based on time zone feature from the perspectives of both user and GIServices.Time zone is taken as the location factor and mapped into the prediction process.A time zone-adjusted Pearson correlation coefficient algorithm was designed to measure the similarity between the GIServices and the target,helping to identify highly similar GIServices.By adopting a coefficient of confidence in the final generation phase,the value of the QoGIS most similar to the target services will play a dominant role in the comprehensive result.Two series of experiments on large-scale QoGIS data were implemented to verify the effectivity of LAGCF.The results showed that LAGCF can improve the accuracy of QoGIS prediction significantly.
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.
