GraCT: A Grammar-based Compressed Index for Trajectory Data

TL;DR

GraCT is a novel compressed index structure for trajectory data that enables efficient storage and fast spatio-temporal queries, significantly reducing space and improving query speed over traditional methods.

Contribution

Introduces GraCT, a grammar-based compressed index for trajectory data supporting direct query access and outperforming traditional storage methods in space and speed.

Findings

GraCT compresses trajectory data more than traditional compressors.

Supports direct access to trajectories without full decompression.

Achieves 2 orders of magnitude space reduction and faster query times.

Abstract

We introduce a compressed data structure for the storage of free trajectories of moving objects (such as ships and planes) that efficiently supports various spatio-temporal queries. Our structure, dubbed GraCT, stores the absolute positions of all the objects at regular time intervals (snapshots) using a $k^2$-tree, which is a space- and time-efficient version of a region quadtree. Positions between snapshots are represented as logs of relative movements and compressed using Re-Pair, a grammar-based compressor. The nonterminals of this grammar are enhanced with MBR information to enable fast queries. The GraCT structure of a dataset occupies less than the raw data compressed with a powerful traditional compressor such as p7zip. Further, instead of requiring full decompression to access the data like a traditional compressor, GraCT supports direct access to object trajectories or to their position at specific time instants, as well as spatial range and nearest-neighbor queries on time instants and/or time intervals. Compared to traditional methods for storing and indexing spatio-temporal data, GraCT requires two orders of magnitude less space, and is competitive in query times. In particular, thanks to its compressed representation, the GraCT structure may reside in main memory in situations where any classical uncompressed index must resort to disk, thereby being one or two orders of magnitude faster.