Portable High-Performance Indexing for Vector Product Format Spatial Databases

Geo-spatial databases have an overall performance problem because of their complexity and large size. For this reason, many researchers seek new ways to improve the overall performance of geo-spatial databases. Typically, these research efforts are focused on complex indexing structures and query processing methods to capture the relationships between the individual features of fully-functional geo-spatial databases. Visualization applications, such as combat simulators and mission planning tools, suffer from the general performance problems associated with geo-spatial databases. This research focuses on building a high-performance geo-spatial database for visualization applications. The main approach is to simplify the complex data model and to index it with high-performance indexing structures. Complex features are reduced to simple primitives, then indexed using a combination of a disk-based array and B(+)-Trees. Test results show that there is a significant performance improvement gained by the new data model and indexing schema for low to medium zoom levels. For high zoom levels, there is a performance drop due to the indexing schema\u27s overhead

A spatial database as a component of integrated database system

Huge amounts of spatial data have been accumulated by government agencies and other organizations for various kinds of applications, such as land information management, asset and facility management, resource management, and environment management. These data are typically collected, stored and used by different custodians for very specific purposes. The full benefits of using spatial data can be achieved by combining the data from different sources covering a common region. It is, however, a great challenge to use the spatial data collected and organized for one purpose for other purposes. In comparison with integration of traditional relational databases, there are a number of unique problems faced by spatial database integration, such as data resolution and precision, data models and representation, and spatial indexes. In this paper, we will identify these issues and discuss possible solutions