Metadata Management In Statistical Information Processing
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Metadata Management in Statistical Information Processing
As the integration of statistical data collected in various subject matter domains becomes more and more important in several socio-economic etc. investigation areas the management of so-called metadata – a formal digital processing of information about data – gains tremendously increasing relevance. Unlike current information technologies (e.g., database systems, computer networks, ...) facilitating merely the technical side of data collation, a coherent integration of empirical data still remains cumbersome, and thus rather costly, very often because of a lack of powerful semantic data models capturing the very meaning and structure of statistical data sets. Recognizing this deficiency, "Metadata Management" proposes a general framework for the computer-aided integration and harmonization of distributed heterogeneous statistical data sources, aiming at a truly comprehensive statistical meta-information system.
Symbolic Data Analysis and the SODAS Software
Symbolic data analysis is a relatively new field that provides a range of methods for analyzing complex datasets. Standard statistical methods do not have the power or flexibility to make sense of very large datasets, and symbolic data analysis techniques have been developed in order to extract knowledge from such data. Symbolic data methods differ from that of data mining, for example, because rather than identifying points of interest in the data, symbolic data methods allow the user to build models of the data and make predictions about future events. This book is the result of the work f a pan-European project team led by Edwin Diday following 3 years work sponsored by EUROSTAT. It includes a full explanation of the new SODAS software developed as a result of this project. The software and methods described highlight the crossover between statistics and computer science, with a particular emphasis on data mining.