Massive Geo-spatial Data Cloud Storage and Services Based on NoSQL Database Technique

  • Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Spatial Information Research Centre of Fujian, Fuzhou University, Fuzhou 350002, China

Received date: 2012-11-19

  Revised date: 2012-12-31

  Online published: 2013-04-18


In recent years, how to implement a efficient storage management on massive geo-spatial data and ulteriorly web service for a broad variety of users, has becomes an increasingly hot issue in the field of geographical information science, with the explosive growth of Earth Observation System(EOS) data and the flourish of the new geography paradigm. A cloud storage system to provide distributed cloud-enabled storage management and services for massive geo-spatial data with an integrity of both vector and raster formats is proposed in this paper in the light of their intrinsic differences. Based on three-tier layer architecture, we put forward its implementation strategy and method of cloud storage management for raster and vector data respectively based on NoSQL database system, followed by a universal data access interface. The novel technolgies, which include distribute graph database-Neo4J and parralel graph compute framework on massive vector data storage and process were introduced. In our research, using the distributed file system-HDFS and the column family database-HBase as a container to store massive raster data with a distributed space index technique, and the distributed graph database system-Neo4J is used to store massive vector data in view of the constraints of ACID with a R-tree space index. Under the unified framework of Geographical Knowledge Cloud platform GeoKSCloud developed by our research group as a successor of GeoKSCloud, its core components — spatial data aggregation centre (GeoDAC) software has been in shape with aim to provide some distributed spatial data storage management and access services for all types of end users. A tesbed is established with serveral 5 physical nodes and accordingly 7 virtual nodes with different areas and operational systems. We carried out an elaborate comparison between GeoDAC and open source GIS software — PostGIS to validate vector data reading & writing performance. The preliminary results indicated that, although GeoDAC has no accelerated write performance than PostGIS, but it gains significant powerful reading or spatial query performance than PostGIS. Inside GeoDAC, space-partitioned massive data is distributed on the cluster and spatial query operation is implemented in parallel, consequently an enhanced rate of spatial query is gained. The achieved techniques and system in our work will provide a variety of users a powerful tool for further in-depth processing and owns a broad application prospects.

Cite this article

CHEN Chong-Cheng, LIN Jian-Feng, TUN Xiao-Zhu, WU Jian-Wei, LIAN Hui-Qun . Massive Geo-spatial Data Cloud Storage and Services Based on NoSQL Database Technique[J]. Journal of Geo-information Science, 2013 , 15(2) : 166 -174 . DOI: 10.3724/SP.J.1047.2013.00166


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