Abstract

Trajectory segmentation is a technique of dividing sequential trajectory data into segments. These segments are building blocks to various applications for big trajectory data. Hence a system framework is essential to support trajectory segment indexing, storage, and query. When the size of segments is beyond the computing capacity of a single processing node, a distributed solution is proposed. In this thesis, a distributed trajectory segmentation framework that includes a greedy-split segmentation method is created. This framework consists of distributed in-memory processing and a cluster of graph storage respectively. For fast trajectory queries, distributed spatial R-tree index of trajectory segments is applied. Using the trajectory indexes, this framework builds queries of segments from in-memory processing and from the graph storage. Based on this segmentation framework, two metrics to measure trajectory similarity and chance of collision are defined. These two metrics are further applied to identify moving groups of trajectories. This study quantitatively evaluates the effects of data partition, parallelism, and data size on the system. The study identifies the bottleneck factors at the data partition stage, and validate two mitigation solutions. The evaluation demonstrates the distributed segmentation method and the system framework scale as the growth of the workload and the size of the parallel cluster.