Abstract
Spatial data with geographical properties is one of the major workloads of cloud data storage system. When users query moves objects in high resolution geometric regions in mainstream cloud storage system, they are often transformed into queries in the range of data in sub-areas. This paper introduces an optimization scheme for the storage and processing of traffic spatial data. In spatial data storage, high-dimensional Hilbert space filling curve is used to divide and code spatial regions, and index tables are established. In spatial data query, adaptive aggregation algorithm is used to aggregate the reading of spatial data. In HDFS, a comprehensive evaluation index of nodes is proposed to optimize the selection of nodes for data replica placement. Finally, this paper uses real-world GPS positioning data for evaluation. The experimental results show that the optimization scheme can effectively reduce the time delay of spatial data query.
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