Scalable Distributed Subtrajectory Clustering

Trajectory clustering is an important operation of knowledge discovery from mobility data. Especially nowadays, the need for performing advanced analytic operations over massively produced data, such as mobility traces, in efficient and scalable ways is imperative. However, discovering clusters of complete trajectories can overlook significant patterns that exist only for a small portion of their lifespan. In this paper, we address the problem of Distributed Subtrajectory Clustering in an efficient and highly scalable way. The problem is challenging because the subtrajectories to be clustered are not known in advance, but they need to be discovered dynamically based on adjacent subtrajectories in space and time. Towards this objective, we split the original problem to three sub-problems, namely Subtrajectory Join, Trajectory Segmentation and Clustering and Outlier Detection, and deal with each one in a distributed fashion by utilizing the MapReduce programming model. The efficiency and the effectiveness of our solution is demonstrated experimentally over a synthetic and two large real datasets from the maritime and urban domains and through comparison with two state of the art subtrajectory clustering algorithms.