GraCT: A Grammar-based Compressed Index for Trajectory Data

Abstract

[Abstract]: We introduce a compressed data structure for the storage of free trajectories of moving objects 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 k2-tree, which is a space- and time-efficient region quadtree. Positions between snapshots are represented as logs of relative movements and compressed using a grammar-based compressor. The non-terminals 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. 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.