The rise of GPS and broadband-speed wireless devices has led to tremendous excitement about a range of applications broadly characterized as “location based services”. Current database storage systems, however, are inadequate for manipulating the very large and dynamic spatio-temporal data sets required to support such services. Proposals in the literature either present new indices without discussing how to cluster data, potentially resulting in many disk seeks for lookups of densely packed objects, or use static quadtrees or other partitioning structures, which become rapidly suboptimal as the data or queries evolve. As a result of these performance limitations, we built TrajStore, a dynamic storage system optimized for efficiently retrieving all data in a particular spatiotemporal region. TrajStore maintains an optimal index on the data and dynamically co-locates and compresses spatially and temporally adjacent segments on disk. By letting the storage layer evolve with the index, the system adapts to incoming queries and data and is able to answer most queries via a very limited number of I/Os, even when the queries target regions containing hundreds or thousands of different trajectories
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