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Semantic Integration and Knowledge Discovery for Environmental Research
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Author(s): Zhiyuan Chen (University of Maryland, Baltimore County (UMBC), USA), Aryya Gangopadhyay (University of Maryland, Baltimore County (UMBC), USA), George Karabatis (University of Maryland, Baltimore County (UMBC), USA), Michael McGuire (University of Maryland, Baltimore County (UMBC), USA)and Claire Welty (University of Maryland, Baltimore County (UMBC), USA)
Copyright: 2008
Pages: 23
Source title:
Successes and New Directions in Data Mining
Source Author(s)/Editor(s): Pascal Poncelet (Ecole des Mines d'Ales, France), Florent Masseglia (Project AxIS-INRIA, France)and Maguelonne Teisseire (Universite Montpellier, France)
DOI: 10.4018/978-1-59904-645-7.ch010
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Abstract
Environmental research and knowledge discovery both require extensive use of data stored in various sources and created in different ways for diverse purposes. We describe a new metadata approach to elicit semantic information from environmental data and implement semantics-based techniques to assist users in integrating, navigating, and mining multiple environmental data sources. Our system contains specifications of various environmental data sources and the relationships that are formed among them. User requests are augmented with semantically related data sources and automatically presented as a visual semantic network. In addition, we present a methodology for data navigation and pattern discovery using multi-resolution browsing and data mining. The data semantics are captured and utilized in terms of their patterns and trends at multiple levels of resolution. We present the efficacy of our methodology through experimental results.
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