Forming a global monitoring mechanism and a spatiotemporal performance model for geospatial services.
Geographic information service (GIService) has become popular in the last decade to develop applications for addressing global challenges. Performance is one of the most important criteria to help users select distributed online GIService for developing geospatial applications including natural hazards and emergency responses. However, performance accuracy is limited by the single-location-based evaluation mechanism while service performance is dynamic in space and time between end-users and services. We propose a spatiotemporal performance evaluation mechanism to improve the accuracy. Specially, a cloud and volunteer computing mechanism is proposed to collect performance information of globally distributed GIServices. A global spatiotemporal performance model is designed to integrate spatiotemporal dynamics for better performance evaluation for users from different regions at different times. This model is tested to support GIService selection in global spatial data infrastructures (SDIs). The experiment confirms that the proposed model provides more accurate evaluations for global users and better supports geospatial resource utilizations in SDIs than previous mechanisms. The methodology can be adopted to improve the services of other regional and global distributed operational systems. [ABSTRACT FROM AUTHOR]/nCopyright of International Journal of Geographical Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Collected monitoring information was archived in a spatiotemporal database with a 2-h interval. Statistics of service performance distributions were generated according to different services, spatial regions and the great circle distance between monitoring site and the service (user–server distance). Temporal characteristics were analysed based on performance curve monthly, each day of a week, and each hour of a day. By analysing this performance information, spatiotemporal service performance patterns were identified.
Method development, test
DistributionControllabilityFrequencyEfficiencyCost
we utilize the cloud computing and volunteer computing technologies and propose a spatiotemporal performance model that provides more accurate performance evaluations to users from different regions at different times.
This model is tested to support GIService selection in global spatial data infrastructures (SDIs)To collect globally distributed performance information, we designed a new monitoring mechanism by utilizing volunteer and cloud computingBy crawling GIService information from a number of popular SDIswe obtained 3188 (WMS), 328 (WFS) and 41 (WCS) addresses, which provide XML-based descriptions of web service interfacesMore than 80,000 spatial data set were retrieved from these OGC
The experiment confirms that the proposed model provides more accurate evaluations for global users and better supports geospatial resource utilizations in SDIs than previous mechanisms.
We propose a spatiotemporal performance evaluation mechanism to improve the accuracy. Specially, a cloud and volunteer computing mechanism is proposed to collect performance information of globally distributed GIServices. A global spatiotemporal performance model is designed to integrate spatiotemporal dynamics for better performance evaluation for users from different regions at different times.
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