Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-4/W2, 133-138, 2013
https://doi.org/10.5194/isprsarchives-XL-4-W2-133-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
25 Oct 2013
Key Technology Research on Open Architecture for The Sharing of Heterogeneous Geographic Analysis Models
S. S. Yue, Y. N. Wen, G. N. Lv, and D. Hu Key Laboratory of Virtual Geographic Environment (Ministry of Education), Nanjing Normal University, Nanjing, Jiangsu Province, 210046, China
Keywords: Geographic Analysis Model; Resource Sharing; Distributed Computing Abstract. In recent years, the increasing development of cloud computing technologies laid critical foundation for efficiently solving complicated geographic issues. However, it is still difficult to realize the cooperative operation of massive heterogeneous geographical models. Traditional cloud architecture is apt to provide centralized solution to end users, while all the required resources are often offered by large enterprises or special agencies. Thus, it's a closed framework from the perspective of resource utilization. Solving comprehensive geographic issues requires integrating multifarious heterogeneous geographical models and data. In this case, an open computing platform is in need, with which the model owners can package and deploy their models into cloud conveniently, while model users can search, access and utilize those models with cloud facility. Based on this concept, the open cloud service strategies for the sharing of heterogeneous geographic analysis models is studied in this article. The key technology: unified cloud interface strategy, sharing platform based on cloud service, and computing platform based on cloud service are discussed in detail, and related experiments are conducted for further verification.
Conference paper (PDF, 1085 KB)


Citation: Yue, S. S., Wen, Y. N., Lv, G. N., and Hu, D.: Key Technology Research on Open Architecture for The Sharing of Heterogeneous Geographic Analysis Models, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-4/W2, 133-138, https://doi.org/10.5194/isprsarchives-XL-4-W2-133-2013, 2013.

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