  Mining movement data to reveal interesting behavioral patterns has gained
attention in recent years. One such pattern is the convoy pattern which
consists of at least m objects moving together for at least k consecutive time
instants where m and k are user-defined parameters. Existing algorithms for
detecting convoy patterns, however do not scale to real-life dataset sizes.
Therefore a distributed algorithm for convoy mining is inevitable. In this
paper, we discuss the problem of convoy mining and analyze different data
partitioning strategies to pave the way for a generic distributed convoy
pattern mining algorithm.
