The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data Management in Location-Dependent Information Services
IEEE Pervasive Computing
IEEE Transactions on Computers
Modeling and Querying Moving Objects
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Moving Objects Databases: Issues and Solutions
SSDBM '98 Proceedings of the 10th International Conference on Scientific and Statistical Database Management
On the Generation of Spatiotemporal Datasets
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
The TPR*-tree: an optimized spatio-temporal access method for predictive queries
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Hi-index | 0.00 |
The TPR*-tree is most popularly accepted as an index structure for processing future-time queries in moving object databases. In the TPR*-tree, the future locations of moving objects are predicted based on the CBR(Conservative Bounding Rectangle). Since the areas predicted from CBRs tend to grow rapidly over time, CBRs thus enlarged lead to serious performance degradation in query processing. Against the problem, we propose a novel method to adjust CBRs to be tight, thereby improving the performance of query processing. Our method examines whether the adjustment of a CBR is necessary when accessing a leaf node for processing a user query. Thus, it does not incur extra disk I/Os in this examination. Also, in order to make a correct decision, we devise a cost model that considers the I/O overhead for the CBR adjustment and the performance gain in the future-time owing to the CBR adjustment. With the cost model, we can prevent unusual expansions of BRs even when updates on nodes are infrequent and also avoid unnecessary execution of the CBR adjustment. For performance evaluation, we conducted a variety of experiments. The results show that our method improves the performance of the original TPR*-tree significantly.