Computing longest duration flocks in trajectory data
J Gudmundsson, M Van Kreveld - Proceedings of the 14th annual ACM …, 2006 - dl.acm.org
Proceedings of the 14th annual ACM international symposium on Advances in …, 2006•dl.acm.org
Moving point object data can be analyzed through the discovery of patterns. We consider the
computational efficiency of computing two of the most basic spatio-temporal patterns in
trajectories, namely flocks and meetings. The patterns are large enough subgroups of the
moving point objects that exhibit similar movement and proximity for a certain amount of
time. We consider the problem of computing a longest duration flock or meeting. We give
several exact and approximation algorithms, and also show that some variants are as hard …
computational efficiency of computing two of the most basic spatio-temporal patterns in
trajectories, namely flocks and meetings. The patterns are large enough subgroups of the
moving point objects that exhibit similar movement and proximity for a certain amount of
time. We consider the problem of computing a longest duration flock or meeting. We give
several exact and approximation algorithms, and also show that some variants are as hard …
Moving point object data can be analyzed through the discovery of patterns. We consider the computational efficiency of computing two of the most basic spatio-temporal patterns in trajectories, namely flocks and meetings. The patterns are large enough subgroups of the moving point objects that exhibit similar movement and proximity for a certain amount of time. We consider the problem of computing a longest duration flock or meeting. We give several exact and approximation algorithms, and also show that some variants are as hard as MaxClique to compute and approximate.