  We present PPQ-trajectory, a spatio-temporal quantization based solution for
querying large dynamic trajectory data. PPQ-trajectory includes a
partition-wise predictive quantizer (PPQ) that generates an error-bounded
codebook with autocorrelation and spatial proximity-based partitions. The
codebook is indexed to run approximate and exact spatio-temporal queries over
compressed trajectories. PPQ-trajectory includes a coordinate quadtree coding
for the codebook with support for exact queries. An incremental temporal
partition-based index is utilised to avoid full reconstruction of trajectories
during queries. An extensive set of experimental results for spatio-temporal
queries on real trajectory datasets is presented. PPQ-trajectory shows
significant improvements over the alternatives with respect to several
performance measures, including the accuracy of results when the summary is
used directly to provide approximate query results, the spatial deviation with
which spatio-temporal path queries can be answered when the summary is used as
an index, and the time taken to construct the summary. Superior results on the
quality of the summary and the compression ratio are also demonstrated.
