TrajStore: An Adaptive Storage System for Very Large Trajectory Data Sets

Phillipe Cudre-Mauroux, MIT
Eugene Wu, MIT
Samuel Madden, MIT

Abstract—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 spatiotemporal 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 are building TrajStore, a dynamic storage system optimized for efficiently retrieving all data in a particular geo-spatial/temporal 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 or 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.

To appear, 26th International Conference on Data Engineering (ICDE 2010) [full paper available soon]