Distributed Multidimensional Data Optimization Model in Big Data Environment

With the gradual transformation of spatial information service to spatio-temporal big data service, the quality and timeliness of spatio-temporal data are guaranteed. Efficient query, mining and analysis of spatio-temporal big data provide decision response support for complex scenes. This paper mainly studies the distributed multidimensional data optimization model under the big data environment. Based on the architecture of distributed computing and the characteristics of data storage, this paper analyzes the characteristics of spatio-temporal data and the key problems of realizing efficient storage and parallel processing in distributed environment. Build distributed space-time data storage organization model based on HBase, based on the S2-Geometry algorithm’s coding and operation power in geographic space, a generalized hierarchical grid was constructed to manage the spatio-temporal objects, and the Compact Hilbert Index algorithm was introduced to optimize the generation method of spatio-temporal mixed index value, reduce the structure and the index of the coupling between the spatio-temporal data, combined with the characteristics of data distribution and logical organization of hierarchical block, to design a table, a data storage model of multiple index of mixed management, to provide efficient query of spatiotemporal data high availability data storage structure.