DT-KST: Distributed Top-k similarity query on big trajectory streams

During the past decade, with the widespread use of smartphones and other mobile devices, big trajectory data are generated and stored in a distributed way. In this work, we focus on the distributed top-k similarity query over big trajectory streams. Processing such a distributed query is challenging due to the limited network bandwidth. To overcome this challenge, we propose a communication-saving algorithm DT-KST (Distributed Top-K Similar Trajectories). DT-KST utilizes the multi-resolution property of Haar wavelet, and devises a level-increasing communication strategy to tighten the similarity bounds. Then, a local pruning strategy is imported to reduce the amount of data returned from distributed nodes. Theoretical analysis and extensive experiments on a real dataset show that DT-KST outperforms the state-of-the-art approach in terms of communication cost.