Vessel trajectory compression algorithm based on MapReduce

Abstract

With the development of shipping industry, vessels generate huge amount of vessel trajectory data in shipping production activities, which brings huge pressure to the storage and analysis of data. In order to improve the compression efficiency of a large amount of vessel trajectory data, a MapReduce-based vessel trajectory compression algorithm is proposed. The algorithm uses a twice-divided method to pre-process the vessel trajectory, dividing the original trajectory into several sub-trajectory segments. Based on the classical trajectory data compression algorithm, the algorithm introduces the MapReduce parallelization processing model and the processing idea of local processing combined with global optimization to compress each trajectory segment. In Map stage, the online compression algorithm is used to get the local optimal solution of the compressed trajectory, and in Reduce stage, the batched compression algorithm is used to globally optimize the merged compressed trajectory and get the final compressed trajectory. The experimental results show that the MapReduce-based vessel trajectory compression algorithm can reduce the impact caused by data skew and significantly improve the compression efficiency of vessel trajectory data. And for the compression of large-scale vessel trajectory data, the compression algorithm has obvious advantages over the traditional centralized trajectory compression algorithm.