PPQ-Trajectory: Spatio-temporal Quantization for Querying in Large Trajectory Repositories

TL;DR

PPQ-trajectory introduces a novel spatio-temporal quantization method that efficiently compresses large trajectory datasets, enabling fast and accurate approximate and exact queries with significant performance improvements.

Contribution

It proposes a new predictive quantization approach with indexing techniques for efficient querying of large dynamic trajectory data, outperforming existing methods.

Findings

Significant improvements in query accuracy and speed.

Higher compression ratios and better summary quality.

Enhanced support for both approximate and exact spatio-temporal queries.

Abstract

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.