| Technologies dealing with location such as GPS are producing more and more data of moving objects. Spatio-temporal databases store information about the positions of individual objects over time. Real-world applications of spatio-temporal data include vehicle navigation, migration of people, tracking and monitoring air-based, sea or landbased vehicles. Also the location technologies, such as GPS and telegraphy, are producing more and more data of moving objects. Spatio-temporal database is needed to manage these data, so as to solve the problems in spatio-temporal applications. A spatio-temporal database adopts an exhaustive search strategy for querying the trajectories. This is very time-consuming when processing large datasets for the given spatio-temporal query conditions. As a result, efficient Spatio-Temporal indexing methods are highly demanded to improve the performance of the system in searching such large datasets.; Referring to Fixed Network R-Tree (FNR-Tree) and the trajectory oriented indexing method Trajectory Bundle-Tree (TB-Tree), in this thesis, an indexing structure is proposed for trajectories to efficiently support spatio-temporal called the FNR-TB Tree. This indexing structure will help in analyzing space-time relationships among space-time paths, an example of spatio-temporal data, in a large dataset. This technique discriminates the space dimension from the time dimension for indexing and thus is expected to give better performance than the existing methods. Furthermore, since this method utilizes the advantages of the TB-Trees, it yields a better performance for spatio-temporal queries than other existing methods for retrieving trajectories. |
