LARGE-SCALE MULTIDIMENSIONAL COVERAGE DATABASES

With GIS technology for Web-enabled vector and meta data access becoming mature, the next quest is towards fast, flexible services on extremely large raster data assets. Currently 2-D images (satellite imagery, ortho photos) prevail, but at the horizon higher-dimensional objects appear, such as 3-D time satellite image time series and seismic data, and 4-D climate simulations. At the same time, database researchers are getting aware of the specific challenges such multidimensional coverages pose. Some promising recent work shows that database systems with optimising query languages actually are superior to file-based systems in performance, flexibility, and scalability. We demonstrate three applications: a 2-D ortho photo maintained by the Bavarian State Survey, a 3-D AVHRR time series deployed as experimental Web service by the German Aerospace Association, and a 4-D climate simulation database implemented by Max-Planck-Institute for Meteorology. All of them use the identical underlying database technology, namely RasDaMan / Oracle with the RasDaMan raster query language.