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