Efficient trajectory compression and range query processing

Nowadays, there are ubiquitousness of GPS sensors in various devices collecting, transmitting and storing tremendous trajectory data. However, such an unprecedented scale data has posed urgent demand for not only effective storage mechanism but also efficient query mechanism. Line simplification online mode, searving as a mainstream compression method, plays important role to attack this issue. But the existing algorithms, either their time cost is extremely high, or accuracy loss after completely unacceptable. To issue, we propose $\epsilon \_$Region based Online Compression with Error bounded (ROCE short), which makes best balance among loss, rate. The range serves primitive, yet quite essential operation on analyzing trajectories. Each usually seen sequence discrete points, most previous work, judged be overlapped region R iff at least one point falling R. traditional criteria suitable when queried trajectories compressed, because may hundreds points discarded between each two adjacent compressed sparse. And many could missing result set. address this, paper, new probability Range Query processing algorithm Compressed RQC proposed. In addition, index \emph{ASP\_tree} lots novel techniques presented accelerate queries obviously. Extensive experiments have been done multiple real datasets, results demonstrate superior performance our methods.