Spatial data mining is a process used to discover interesting but not explicitly available, highly usable patterns embedded in both spatial and nonspatial data, which are possibly stored in a spatial database. An important application of spatial data mining methods is the extraction of knowledge from a Geographic Information System (GIS). INGENS (INductive GEographic iNformation System) is a pr...

Huge amounts of data have been stored in databases, data warehouses, geographic information systems, and other information repositories , and this data is still growing rapidly 4]. Companies are building data warehouses capable of storing hundreds of terabytes of data related to natural resource exploration; astronomical databases are measured in terabytes in size; and it is expected that the N...

Delivering individualized services that conform to the user’s current situation will form the focus of ubiquitous environments. A description of the networked environment at a semantic level will necessitate contextually oriented knowledge acquisition methods. This then engenders unique challenges for the crucial step of resource discovery. A number of service discovery protocols exist to perfo...

Contributions from researchers in Knowledge Discovery are producing essential tools in order to better understand the typically large amounts of spatial data in Geographical Information Systems. Clustering techniques are proving to be valuable in providing exploratory analysis functionality while supporting approaches for automated pattern discovery in spatially referenced data and for the iden...

Designers, in the process of satisfying design requirements, generally encounter difficulties in, firstly, understanding the problem and secondly, finding a solution [Cross 1998]. Often the process of understanding the problem and developing a feasible solution are developed simultaneously by proposing a solution to gauge the extent to which the solution satisfies the specific requirements. Sup...