Distributed Spatial Data Mining in Geospatial Knowledge Service Grid

As an extension of traditional data mining, distributed data mining (DDM) is a well-known issue that has been extensively studied to extract some hidden, useful and interesting information from data by using various kinds of resources distributed in different geography positions. In the literature, there are many methods and technologies proposed to implement large-scale distributed data mining, but owing to lack of autonomy, intelligentization and the limitation of expansibility, most of the works fail to reach a practical application in industry and commerce fields. With the rapid development of distributed sharing infrastructures and the emergence and mature of grid technology, providing knowledge services in grid environment is becoming the trend of knowledge discovery, and it will certainly make the technology of data processing into a broader way. In this paper, we elaborate on a geographical knowledge services grid test-bed, which is developed to support distributed & parallel geological problems solving, as an example, a distributed spatial outliers mining algorithm based on Delaunay triangulation(D-TIN) is introduced, and the perform scheduling of Delaunay triangulation construction and spatial outliers mining is presented too.