Using a generic spatial access method for caching and efficient retrieval of vario-scale data in a server-client architecture

This paper presents a methodology for using a generic data-driven spatial access method as a communication mechanism for vario-scale data in a server-client setting. As a complete data set is often quite large, it is managed at the server side and supporting different scale levels is important. We show that a generic R-tree like grouping method,...

Exploring the use of a generic spatial access method for caching and efficient retrieval of vario-scale data in a client-server architecture

Vario-scale data structures make it possible to derive maps at arbitrary scale. When requesting a map, data has to be transferred from server to client. This takes time, which affects the responsiveness of the system, and sometimes costs can be involved for every byte that is send over the network. Redundant data transfers should thus be avoided...

Parallel creation of vario-scale data structures for large datasets

Processing massive datasets which are not fitting in the main memory of computer is challenging. This is especially true in the case of map generalization, where the relationships between (nearby) features in the map must be considered. In our case, an automated map generalization process runs offline to produce a dataset suitable for...