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With the prevalence of data-intensive geospatial applications, massive spatio-temporal sensor data are obtained and the big data have posed grand challenges on existing index methods based on spatial databases due to their intrinsic poor scalability and retrieval efficiency. Motivated by the deficiencies, in this paper, we propose a distributed composite spatio-temporal index scheme called VegaIndexer...
With the emergence of online geospatial applications such as WebGIS services, the large spatio-temporal data and numerous concurrent users have posed grand challenges on the state-of-the-art spatial databases due to their intolerable disk I/O latency. Hence, the clients cannot be served with real-time response. In this paper, we propose VegaCache, a distributed inmemory caching scheme, to provide...
With the rapid growth of geospatial data and concurrent users, the state-of-the-art WebGIS cannot support massive data storage and processing due to poor scalability of underlying centralized systems (e.g., native file systems and SDBMS). In this paper, we propose a novel distributed geospatial data storage and processing framework for large-scale WebGIS. Our proposal contains three significant characteristics...
With the rapid development of information technologies and GIS techniques, raster data amount is growing on an unprecedented scale. Existing WebGIS based on local file systems and RDBMS cannot manage very large raster data efficiently because of limited storage capacity of single node. Although expensive storage devices are used to enlarge capacity, WebGIS is still vulnerable to suffer from single...
The features of mass spatial data and the limit of Internet bandwidth are key issues which restrict the WebGIS application performance. How to implement efficient vector data compression is important during the stage of data transmission. To get high compression ratio, we propose one multilevel vector data compression method which combine lossy compression and lossless compression technology. According...
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