Parallel distributed trajectory pattern mining using MapReduce

This paper proposes a new approach to trajectory pattern mining, which attempts to discover frequent movement patterns from the trajectories of moving objects. For dealing with a large volume of trajectory data, traditional approaches quantize them by a grid with a fixed resolution. However, an appropriate resolution often varies across different areas of trajectories. Simply increasing the resolution cannot capture broad patterns and consumes unnecessarily large computational resources. To solve the problem, we propose a hierarchical grid-based approach with quadtree search. The approach initially searches for frequent patterns with a coarse grid and drills down into a finer grid level to discover more minute patterns. The algorithm is naturally parallelized and implemented in the MapReduce programming model to accelerate the computation. Our evaluative experiments on real-word data show the effectiveness of our approach in mining complex patterns with lower computational cost than the previous work.