Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Using space-time grid for efficient management of moving objects
Proceedings of the 2nd ACM international workshop on Data engineering for wireless and mobile access
Storage and Retrieval of Moving Objects
MDM '01 Proceedings of the Second International Conference on Mobile Data Management
MDM '01 Proceedings of the Second International Conference on Mobile Data Management
Indexing the Current Positions of Moving Objects Using the Lazy Update R-tree
MDM '02 Proceedings of the Third International Conference on Mobile Data Management
Q+Rtree: Efficient Indexing for Moving Object Databases
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
SINA: scalable incremental processing of continuous queries in spatio-temporal databases
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Supporting frequent updates in R-trees: a bottom-up approach
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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 |
Although several sophisticated index structures for moving objects have been proposed, the hashing method based on a simple grid has been widely employed due to its simplicity. Since the performance of the hashing is largely affected by the size of a grid cell, it should be carefully decided with regard to the workload. In many real applications, however, the workload varies dynamically as time, for example the traffic in the commuting time vs. that in the night. The basic hashing cannot handle this dynamic workload because the cell size cannot be changed during the execution. In this paper, we propose the adaptive multi-level hashing to support the dynamic workload efficiently. The proposed technique maintains two levels of the hashes, one for fast moving objects and the other one for quasi-static objects. A moving object changes its level adaptively according to the degree of its movement.