An Efficient Prediction Technique for Range Aggregation of Moving Objects

Predictive range aggregate (PRA) queries are one of the important researching areas in the moving object database. In this paper an efficient prediction technique, PRA-tree, is presented for range aggregation of moving objects. PRA-tree splits the velocity domain regularly, and classifies moving objects into different velocity buckets by their velocities. Then a TPR-tree, which is based on the TPR-tree structure and added with aggregate information in intermediate nodes, is used to index the moving objects in each buckets, thus reducing the disk accesses of PRA queries. A PRA-tree is supplemented by a hash index on leaf nodes, and uses bottom-up delete algorithm, thus having a good update performance and concurrency. Also developed for the PRA tree is an enhanced predictive range aggregate (EPRA) query algorithm which uses a more precise branch and bound searching strategy, reducing the disk I/O greatly. Experimental results and analysis show that the EPRA algorithm for PRA-tree has a good query performance and outperforms the popular TPR\+*tree index.