Finding, Assessing, and Integrating Statistical Sources for Data Mining

author: Craig A. Knoblock, Information Sciences Institute (ISI), University of Southern California
published: July 15, 2015,   recorded: May 2015,   views: 1789


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As the knowledge discovery process has been widely applied in a variety of domains, there is a growing opportunity to use the Linked Open Data (LOD) cloud as a primary data source for knowledge discovery. The tasks of finding the relevant data from various sources and then using that data for the desired analysis are the key challenges. There is a striking increase on the availability of statistical data and indicators (e.g. social, economic) in the LOD, and the Cube ontology has become the de facto standard for their description according to a multi-dimensional model. In this paper we discuss a detailed scenario for using the LOD as a primary source of data for building analysis models in the Peacebuilding domain. Next, we present an approach to finding potentially relevant cube datasets in the LOD cloud, assessing their compatibility, and then integrating the compatible datasets to enable the application of data mining algorithms

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