Parallel Discovery of Trajectory Companions from Heterogeneous Streaming Data

Trajectorystreams consist of large volumes of time-stamped spatial data that are constantly generated from diverse and geographically distributed sources. Discovery of traveling patterns on trajectory streams such as gathering and companies needs to process each record when it arrives and correlates across multiple records near real-time. Thus techniques for handling high-speed trajectory streams should scale on distributed cluster computing. The main issues encapsulate three aspects, namely a data model to represent the continuous trajectory data, the parallelism of a discovery algorithm, and end-to-end performance improvement. In this paper, we propose a parallel discovery method that consists of 1) a model of partitioning trajectories sampled on different time intervals; 2) definition on distance measurements of trajectories; and 3) a parallel discovery algorithm. We develop this method in a stream processing workflow. From parallelization point of view, we investigate system performance, scalability, stability. Our method discovers trajectory gathering patterns with low latency and scales as the size of trajectory data grows.