A dynamic statistical model for geospatial data access laws based on cloud computing

The strategy of storage and organization can be adjusted utilizing the access and distribution law of the spatial data, which will significantly improve system performance of spatial data services. The access and distribution law of the spatial data based on Hotmap and Zipf-like are statics, and can not reflect its global information real-time. A dynamic statistical method based on collaborative cloud is proposed in this paper to resolve above-mentioned problems. The nodes service capabilities are calculated in our algorithm. The node agents with good service capabilities are chosen preferentially in the group to fuse dynamic statistical information. The experimental results show that the performance of our algorithm can be improved by about 29% compared with the algorithm of random nodes. The algorithm can meet the need of dynamic statistics in large scale cloud mode with high efficiency.