Indexing mobile objects using dual transformations

With the recent advances in wireless networks, embedded systems, and GPS technology, databases that manage the location of moving objects have received increased interest. In this paper, we present indexing techniques for moving object databases. In particular, we propose methods to index moving objects in order to efficiently answer range queries about their current and future positions. This problem appears in real-life applications such as predicting future congestion areas in a highway system or allocating more bandwidth for areas where a high concentration of mobile phones is imminent. We address the problem in external memory and present dynamic solutions, both for the one-dimensional and the two-dimensional cases. Our approach transforms the problem into a dual space that is easier to index. Important in this dynamic environment is not only query performance but also the update processing, given the large number of moving objects that issue updates. We compare the dual-transformation approach with the TPR-tree, an efficient method for indexing moving objects that is based on time-parameterized index nodes. An experimental evaluation shows that the dual-transformation approach provides comparable query performance but has much faster update processing. Moreover, the dual method does not require establishing a predefined query horizon.