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

[1]  Steven Fortune,et al.  A sweepline algorithm for Voronoi diagrams , 1986, SCG '86.

[2]  Chen Chongcheng,et al.  Research on grid based spatial outliers mining and its implementation , 2008, 2008 3rd International Conference on Intelligent System and Knowledge Engineering.

[3]  Zhuoqun Xu,et al.  A Distributed Parallel Computing Environment for Bioinformatics Problems , 2007, Sixth International Conference on Grid and Cooperative Computing (GCC 2007).

[4]  Yan Ren,et al.  Service-Oriented Knowledge Modeling Method and its Application , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[5]  Mario Cannataro,et al.  Distributed data mining on the grid , 2002, Future Gener. Comput. Syst..

[6]  M. Tahar Kechadi,et al.  Knowledge Map: Toward a New Approach Supporting the Knowledge Management in Distributed Data Mining , 2007, Third International Conference on Autonomic and Autonomous Systems (ICAS'07).

[7]  Leonidas J. Guibas,et al.  Primitives for the manipulation of general subdivisions and the computation of Voronoi diagrams , 1983, STOC.

[8]  Robert L. Scot Drysdale,et al.  A practical algorithm for computing the Delaunay triangulation for convex distance functions , 1990, SODA '90.

[9]  Geoff Leach,et al.  Improving Worst-Case Optimal Delaunay Triangulation Algorithms , 1992 .

[10]  Dongyi Ye,et al.  Minimum Spanning Tree Based Spatial Outlier Mining and Its Applications , 2008, RSKT.

[11]  Hai Zhuge,et al.  China's E-Science Knowledge Grid Environment , 2004, IEEE Intell. Syst..

[12]  Kenneth A. Hawick,et al.  Distributed frameworks and parallel algorithms for processing large-scale geographic data , 2003, Parallel Comput..

[13]  Rex A. Dwyer.  A faster divide-and-conquer algorithm for constructing delaunay triangulations , 1987, Algorithmica.

[14]  Shashi Shekhar,et al.  A Unified Approach to Detecting Spatial Outliers , 2003, GeoInformatica.

[15]  Shashi Shekhar,et al.  Detecting graph-based spatial outliers: algorithms and applications (a summary of results) , 2001, KDD '01.

[16]  Fuling Bian,et al.  SKG: Spatial Knowledge Discovery in Grid Environment , 2006, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06).

[17]  Mario Cannataro,et al.  The knowledge grid , 2003, CACM.