Using compressed index structures for processing moving objects in large spatio-temporal databases

Abstract

This paper develops a novel, compressed B +-tree based indexing scheme that supports the processing of moving objects in one-, two-, and multi- dimensional spaces. The past, current, and anticipated future trajectories of movements are fully indexed and well organized. No parameterized functions and geometric representations are introduced in our data model so that update operations are not required and the maintenance of index structures can be accomplished by basic insertion and deletion operations. The proposed method has two contributions. First, the spatial and temporal attributes of trajectories are accurately preserved and well organized into compact index structures with very efficient memory space utilization and storage requirement. Second, index maintenance overheads are more economical and query performance is more responsive than those of conventional methods. Both analytical and empirical studies show that our proposed indexing scheme outperforms the TPR-tree.