  Joining trajectory datasets is a significant operation in mobility data
analytics and the cornerstone of various methods that aim to extract knowledge
out of them. In the era of Big Data, the production of mobility data has become
massive and, consequently, performing such an operation in a centralized way is
not feasible. In this paper, we address the problem of Distributed
Subtrajectory Join processing by utilizing the MapReduce programming model.
Compared to traditional trajectory join queries, this problem is even more
challenging since the goal is to retrieve all the "maximal" portions of
trajectories that are "similar". We propose three solutions: (i) a
well-designed basic solution, coined DTJb, (ii) a solution that uses a
preprocessing step that repartitions the data, labeled DTJr, and (iii) a
solution that, additionally, employs an indexing scheme, named DTJi. In our
experimental study, we utilize a 56GB dataset of real trajectories from the
maritime domain, which, to the best of our knowledge, is the largest real
dataset used for experimentation in the literature of trajectory data
management. The results show that DTJi performs up to 16x faster compared with
DTJb, 10x faster than DTJr and 3x faster than the closest related state of the
art algorithm.
