ASQT: An Efficient Index for Queries on Compressed Trajectories

Nowadays, the amount of GPS-equipped devices is increasing dramatically and they generate raw trajectory data constantly. Many location-based services that use trajectory data are becoming increasingly popular in many fields. However, the amount of raw trajectory data is usually too large. Such a large amount of data is expensive to store, and the cost of transmitting and processing is quite high. To address these problems, the common method is to use compression algorithms to compress trajectories. This paper proposes a high efficient spatial index named ASQT, which is a quadtree index with adaptability. And based on ASQT, we propose a range query processing algorithm and a top-k similarity query processing algorithm. ASQT can effectively speed up both the trajectory range query processing and similarity query processing on compressed trajectories. Extensive experiments are done on a real dataset and results show the superiority of our methods.