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Spatial Data Mining Architecture and Technologies Team 12 Hari Kishan Bandaru Sneha Anand Yeluguri Parimi VSPVSK Overview Introduction to Spatial Data Mining Related Work Process of Spatial Data Mining Process of Visual Space Data Mining Common Data Mining Architecture Spatial Data Mining Architecture Visualization Data Model Technologies Advantages and Future Work Conclusion References 2/21 What is Spatial Data Mining? Non–trivial search for interesting and unexpected spatial patterns Non-trivial Search: Interesting: Large (e.g. exponential) search space of plausible hypothesis Ex. Asiatic cholera: causes: water, food, air , insects,…; water delivery mechanisms: pumps, rivers, ponds, wells… Useful in certain application domain Ex. Shutting off identified water pumps => saved human life Unexpected: May provide a new understanding of world Ex. Water pump – Cholera connection to the “germ” theory. 3/21 What is not a Spatial Data Mining? Simple querying of Spatial data Testing a hypothesis via a primary analysis Uninteresting or obvious patterns in spatial data Mining of non-spatial data 4/21 Motivation To find new spatial patterns To understand new geographic process for critical questions To analyze the fast growing spatial data To explore large number of geographic hypothesis To reduce plausible hypothesis To discover relationships between spatial and non spatial data To build spatial knowledge-bases 5/21 Problem Statement Rapid development in the technology of spatial data storage, query, display and analysis. Accessing spatial data through access methods often need technology to spatial reasoning, geographic computing and knowledge of space showing. Spatial data mining technology is used to convert spatial information of geographic coordinates into useful knowledge and effective tools. Visualization technology is used to generate graphics from complex multi-dimensional data to display objective things and their intrinsic connections. 6/21 Related Work Transformation of map information mode to an equilateral mode that consists of formalization , cognition and transmission. Geo Visualization aim is to provide an information exchange and feedback mechanisms for the users. Summarized visualization technology into three points: Feature Identification, Feature Comparison and Feature Interpretation. 7/21 Process of Spatial Data Mining Inputting Spatial Datum Feature Extraction and Feature Database Establishment Data Warehouses Establishment Data Extraction and forming Case Set Create and Train Data Mining Model Evaluating the Mined Out Model, discovering hidden knowledge Knowledge Application 8/21 Process of Visualization Spatial Data Mining Filter : Extracting data of interest Mapping : Creating geometric primitives Draw : Translate geometric primitives into image Feedback : Display the image 9/21 Common Data Mining Architecture The efficiency of the data mining should be improved Historical method cant be obtained effective utilization Interoperability between different systems is bad. For different application object, pertinence is not strong. 10/21 Spatial Data Mining Architecture 11/21 Visualization Spatial Data Mining Model Statistical Mapping Technique Color Thematic map visualization techniques 12/21 Four TierJ2EE Technology Applet Call Servlet: Servlet Call Java Bean: 13/21 Four TierJ2EE Technology JavaBean Call JAFMAS components: Return the results from JAFMAS: 14/21 XML and Space Data Warehouse Technology Read data from data warehouse and generate xml document with unified expression form. Change XML into DOM object model serves for upper accessing. 15/21 Advantages Use of thematic map visualization technology helped users to explore spatial data Increase the data processing speed significantly Made abstract data much easier to understand The proposed J2EE four tier architecture had resolved the synergic work between the layers of prototype system and between the components in the layer. 16/21 Future Work Many existing theoretical and technical issues should be further explored and studied. Ex: spatial data mining in structured modeling Treatment of uncertain information Need to explore similarity measure techniques of mining model produced by statistics, fuzzy logic , rough set methods. 17/21 Conclusion Combination of visualization technology and spatial data mining has helped for analysis of spatial data exploration. New process and architecture were presented for spatial datum data mining based on data ware house. The characteristics for spatial datum were analyzed and difference between spatial data and traditional relationship data were analyzed. 18/21 References 1. He Yueshun, Li Xiang; A Study of Spatial Data Mining Technique Based on Web; In the preceedings of International Conference on Engineering Profession, General Topics for Engineers (Math, Science & Engineering); Page 1-4; 2009 2. Xiao Qiang, Yan Wei, Zhang Hanfei; Application of Visualization Technology in Spatial Data Mining; In the preceedings of International Conference on Computing, Control and Industrial Engineering; Page 153-157; 2010 3. He Yueshun, Xu Wei; A study of spatial data mining architecture and technology; In the preceedings of 2nd IEEE International Conference on Computing & Processing (Hardware/Software); Page 163-166; 2009 21/21 Queries 19/20 20/20