Cloud-Based Framework for Spatio-Temporal Trajectory Data Segmentation and Query

Trajectory segmentation is a technique of dividing sequential trajectory into segments. These segments are building blocks to various applications. 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 article, we develop a distributed trajectory segmentation framework that includes a greedy-split segmentation method. This framework consists of distributed in-memory processing and a cluster of graph storage respectively. For fast trajectory queries, we design a distributed spatial R-tree index of trajectory segments. Using the indexes, we build scalable query operations from both in-memory processing and access to graph storage. Based on this framework, we define two metrics to measure trajectory similarity and chance of collision. These two metrics are further applied to identify moving groups of trajectories. We quantitatively evaluate the effects of data partition, parallelism, and data size on the system. We identify the bottleneck factors at the data partition stage and validate two mitigation techniques to data skew. The evaluation demonstrates our distributed segmentation method and the system framework scale as the growth of the workload and the size of the parallel cluster.