Given a trajectory T we study the problem of reporting all subtrajectory clusters of T. To measure similarity between trajectory we choose the Frechet distance. We adapt an existing serial algorithm into a GPU parallel algorithm, resulting in substantial speed-ups, in some cases up to 11× faster, and increasing the size of the data that can be handled in reasonable amount of time, tests were performed on trajectories three times the size as previously managed. This is to the best of our knowledge not only the first GPU implementation of a subtrajectory clustering algorithm but also the first implementation using the continuous Frechet distance, instead of the discrete Frechet distance.

Data analysis and knowledge discovery in trajectory databases is an emerging field with a growing number of applications such as managing traffic, planning tourism infrastructures or better understanding wildlife. In this paper, we study the problem of finding flock patterns in trajectory databases. A flock refers to a large enough subset of entities that move close to each other for, at least, a given time interval. We present parallel algorithms, to be run on a Graphics Processing Unit, for reporting three different variants of the flock pattern: (1) all maximal flocks, (2) the largest flock and (3) the longest flock. We also provide their complexity analysis together with experimental results showing the efficiency and scalability of our approach.

Given a trajectory T we study the problem of reporting all subtrajectory clusters of T. To measure similarity between curves we choose the Fréchet distance. We show how the existing sequential algorithm can be modified exploiting parallel algorithms together with the GPU computational power showing substantial speed-ups.