  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.
