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The amount of information in spatial databases is growing as more data is made available. Spatial databases mainly store two types of data: raster data (satellite/aerial digital images), and vector data (points, lines, polygons). The complexity and nature of spatial databases makes them ideal for applying parallel processing. MapReduce is an emerging massively parallel computing model, proposed by Google. In this paper, the authors present their experiences in applying the MapReduce model to solve two important spatial problems: bulk-construction of R-Trees and aerial image quality computation, which involve vector and raster data, respectively.
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