Journal of Geo-information Science >
GeoKSCloud：Motivation, Design and Application
Received date: 2013-05-15
Revised date: 2013-09-23
Online published: 2014-03-10
Currently, it is one of the most challenging issues to discover and organize diverse distributed geospatial services for geosciences problem resolving, knowledge innovation and sharing. These services include geospatial data services, geospatial analysis services and geospatial data mining services, etc. Facing this challenge and considering the key points of geospatial information processing, knowledge discovery and sharing, in this paper, the concept of geospatial knowledge cloud is depicted, and a novel cloud-based geographical knowledge service platform named as GeoKSCloud is proposed. Based on cloud computing technology, GeoKSCloud tries to create a unified framework to aggregate a broad variety cross-node and cross-platform geospatial services for end-users. With aim to deal with the compute-intensive and data-intensive challenge of geospatial data processing, the platform adopts the idea of virtualization to construct a scalable computation environment. Five main components of data aggregation, service management, geosciences problem solving, platform control and portal are designed to provide functions of services registry, discovery, composition, execution and data integration. Moreover, supported by natural language understanding, ontology and data visualization technology, the platform offers intelligent reasoning and visualization tools to help users to perform problem solving task more efficiently. The key technologies associated with platform realization are discussed, which includes massive geospatial data cloud storage and management technology, knowledge service management and composition technology, and intelligent geospatial problem solving technology, et al. Finally, a use case of historical seismic influence field analysis is proposed to demonstrate the interoperation of platform components, and the representative user interfaces of platform are illustrated. The case study reveals that GeoKSCloud could reduce the complexity and overhead of geosciences problem solving by coordinating multiple distributed and heterogeneous services.
WU Xiaozhu, CHEN Chongcheng, LIN Jianfeng, WU Jianwei, LIN Jiaxiang, LEI Delong, CAI Zhiming . GeoKSCloud：Motivation, Design and Application[J]. Journal of Geo-information Science, 2014 , 16(2) : 273 -281 . DOI: 10.3724/SP.J.1047.2014.00273
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