Author(s):  
Zhiyuan Chen ◽  
Aryya Gangopadhyay ◽  
George Karabatis ◽  
Michael McGuire ◽  
Claire Welty

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.


Author(s):  
Zhiyuan Chen

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.


2007 ◽  
Vol 18 (1) ◽  
pp. 43-68 ◽  
Author(s):  
Zhiyuan Chen ◽  
Aryya Gangopadhyay ◽  
George Karabatis ◽  
Michael McGuire ◽  
Claire Welty

Author(s):  
Matthew Perry ◽  
Amit Sheth ◽  
Ismailcem Budak Arpinar ◽  
Farshad Hakimpour

The amount of digital data available to researchers and knowledge workers has grown tremendously in recent years. This is especially true in the geography domain. As the amount of data grows, problems of data relevance and information overload become more severe. The use of semantics has been proposed to combat these problems (Berners-Lee et al., 2001; Egenhofer, 2002). Semantics refer to the meaning of data rather than its syntax or structure. Systems which can understand and process data at a semantic level can achieve a higher level of automation, integration, and interoperability. Applications generally use semantic technology for three basic purposes: (1) semantic integration, (2) semantic search and contextual browsing, and (3) semantic analytics and knowledge discovery (Sheth & Ramakrishnan, 2003).


1981 ◽  
Author(s):  
Ross L. Pepper ◽  
Robert S. Kennedy ◽  
Alvah C. Bittner ◽  
Steven F. Wiker

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