Sequential pattern mining from trajectory data

Elio Masciari, Gao Shi, Carlo Zaniolo
2013 Proceedings of the 17th International Database Engineering & Applications Symposium on - IDEAS '13  
In this paper, we study the problem of mining for frequent trajectories, which is crucial in many application scenarios, such as vehicle traffic management, hand-off in cellular networks, supply chain management. We approach this problem as that of mining for frequent sequential patterns. Our approach consists of a partitioning strategy for incoming streams of trajectories in order to reduce the trajectory size and represent trajectories as strings. We mine frequent trajectories using a sliding
more » ... windows approach combined with a counting algorithm that allows us to promptly update the frequency of patterns. In order to make counting really efficient, we represent frequent trajectories by prime numbers, whereby the Chinese reminder theorem can then be used to expedite the computation.
doi:10.1145/2513591.2513653 dblp:conf/ideas/MasciariGZ13 fatcat:fouwnt72ozdhvpll56hl6si2pm