Research on grid based spatial outliers mining and its implementation

Integrated and deep-seated applications call for the intelligentization of spatial data processing. Spatial data mining is becoming more and more important to satisfy practical application and business intelligence. With the rapid development of distributed sharing infrastructures such as grid and web services, providing knowledge services on grid environment is becoming the trend of knowledge discovery and application technologies. This paper introduces an architecture of geographical knowledge grid platform based on globus (GeoKS-Grid), then puts forward a spatial data integration scheme by the combination of GML and WFS specification, finally, taking spatial outliers mining based on MST clustering as an example to illustrates the design and realization of knowledge service. Experimentation finds that it is feasible and practicable to extend the grid environment by adding more and more distributed high performance knowledge discovery services implemented based on data grid mechanisms and globus toolkit, to meet the requirements of application. For the application in real system, a task-oriented architecture based on KSDM is proposed and an implementation of knowledge service is introduced.