Network Architecture
Network Architecture to Identify Spatial Knowledge for Epidemic
(An application of datamining)
Abstract: Recent developments in information technology have enabled collection and processing of vast amounts of personal data, business data and spatial data. It has been widely recognized that spatial data analysis capabilities have not kept up with the need for analyzing the increasingly large volumes of geographic data of various themes that are currently being collected and archived. Our study is carried out on the way to provide the mission-goal strategy (requirements) to predict the disaster. The co-location rules of spatial data mining are proved to be appropriate to design nuggets for disaster identification and the state-of-the-art and emerging scientific applications require fast access of large quantities of data. Here both resources and data are often distributed in a wide area networks with components administrated locally and independently, a framework has been suggested for the above. Our contribution in this paper is to design network architecture for disaster identification. 1. INTRODUCTION:
Geography is an integrative discipline and geographic data under analysis often span across multiple domains. The complexity of spatial data and geographic problems, together with intrinsic spatial relationships, constitute an enormous challenge to conventional data mining methods and call for both theoretical research and development of new techniques to assist in deriving information from large and heterogeneous spatial datasets. (Han and Kamber 2001; Miller and Han 2001; Gahegan and Brodaric 2002).. ‘health’ maps have become available as the use of geographical information systems in health related contexts increased.
A formula implemented as Hazard science to Risk Science, towards understanding the hazards and their consequences (risks), following a probabilistic approach using spatial data mining .Due to larger heterogeneity
(An application of datamining)
Abstract: Recent developments in information technology have enabled collection and processing of vast amounts of personal data, business data and spatial data. It has been widely recognized that spatial data analysis capabilities have not kept up with the need for analyzing the increasingly large volumes of geographic data of various themes that are currently being collected and archived. Our study is carried out on the way to provide the mission-goal strategy (requirements) to predict the disaster. The co-location rules of spatial data mining are proved to be appropriate to design nuggets for disaster identification and the state-of-the-art and emerging scientific applications require fast access of large quantities of data. Here both resources and data are often distributed in a wide area networks with components administrated locally and independently, a framework has been suggested for the above. Our contribution in this paper is to design network architecture for disaster identification. 1. INTRODUCTION:
Geography is an integrative discipline and geographic data under analysis often span across multiple domains. The complexity of spatial data and geographic problems, together with intrinsic spatial relationships, constitute an enormous challenge to conventional data mining methods and call for both theoretical research and development of new techniques to assist in deriving information from large and heterogeneous spatial datasets. (Han and Kamber 2001; Miller and Han 2001; Gahegan and Brodaric 2002).. ‘health’ maps have become available as the use of geographical information systems in health related contexts increased.
A formula implemented as Hazard science to Risk Science, towards understanding the hazards and their consequences (risks), following a probabilistic approach using spatial data mining .Due to larger heterogeneity
References: [1] Chawla, Shekkar, Spatial Databases: A Tour, 2003, Prentice all. (ISBN – 013 – 017 480 – 7) [2] Huang, Shekkar, Xiong, Discovery Collocation Patterns from Spatial Data Sets: A General Approach, IEEE-KDE,volume 16,No:12, dec 2004. [3] Knorr and Ng, Extraction of Spatial Proximity Patterns by Concept Generalization, proceedings second international conference of KDD, pp. 347 – 350, aug 1996.