Adaptive index management for future location-based queries
Many location-based applications have arisen in various areas including mobile communications, traffic control and military command and control (C2) systems. And one of the important research issue in these areas is tracking and managing moving objects through spatiotemporal indexing for the efficient location-based services. However, managing exact geometric location information is difficult to be achieved due to continual changes of moving objects. Traditionally spatiotemporal index structures focus on optimizing the node accesses during construction and massive updates that do not refer on-line movement updates. In this paper we propose an indexing framework for future location queries based on space partitioning and the dual transformation. Our method provides a constraint database approach for constructing indexes to improve the efficiency of spatiotemporal query answering. In addition, the performance enhancement is achieved by our cost-based dynamic management algorithm that determines the appropriate index reorganization probabilistically induced by various mobility models and query cost functions. This approach can be applied for the predictive range queries on moving object's trajectories specifically in the mobile communication environments. We evaluate our method and compare the performance with the related spatiotemporal index structures in the simulated environments.
indexing moving object dynamic management spatiotemporal databases