Stronger compact representations of object trajectories

Abstract

ABSTRACT GraCT and ContaCT were the first compressed data structures to represent object trajectories, demonstrating that it was possible to use orders of magnitude less space than classical indexes while staying competitive in query times. In this paper we considerably enhance their space, query capabilities, and time performance with three contributions. (1) We design and evaluate algorithms for more sophisticated nearest neighbor queries, finding the trajectories closest to a given trajectory or to a given point during a time interval. (2) We modify the data structure used to sample the spatial positions of the objects along time. This improves the performance on the classic spatio-temporal and the nearest neighbor queries, by orders of magnitude in some cases. (3) We introduce RelaCT, a tradeoff between the faster and larger ContaCT and the smaller and slower GraCT, offering a new relevant space-time tradeoff for large repetitive datasets of trajectories.