Towards Adaptive Continuous Trajectory Clustering Over a Distributed Web Data Stream

Abstract

With the popularity of modern mobile devices and GPS technology, big web stream data with location are continuously generated and collected. The sequential positions form a trajectory, and the clustering analysis on trajectories is beneficial to a wide range of applications, e.g., route recommendation. In the past decades, extensive efforts have been made to improve the efficiency of static trajectory clustering. However, trajectory stream data is received incrementally, and the continuous trajectory clustering inevitably faces the following two problems: (1) physical structure design for trajectory representation leads to severe space overhead, and (2) dynamic maintenance of trajectory semantics and its retrieval structure brings intensive computation. To overcome the above problems, an adaptive continuous trajectory clustering framework (ACTOR) is proposed in this paper. Overall, it covers three key components: (1) Simplifier represents trajectory with a well-designed PT structure. (2) Partitioner utilizes a hexagonal-based indexing strategy to enhance the local computational efficiency. (3) Executor accommodates an adaptive selection of P-clustering and R-clustering approaches according to the ROC (rate of change) matrix. Empirical studies on real-world data validate the usefulness of our proposal and prove the huge advantage of our approach over available solutions in the literature.