Abstract (EN)

The data descriptions of the units are called "symbolic" when they are more complex than the standard ones due to the fact that they contain internal variation and are structured. Symbolic data happen from many sources, for instance in order to aggregate huge Relational Data Bases by their underlying concepts. "Extracting knowledge" means getting explanatory results, that why, "symbolic objects" are introduced and studied in this paper. They model concepts and constitute an explanatory output for data analysis. Moreover they can be used in order to define queries of a Relational Data Base and propagate concepts between Data Bases. We define "Symbolic Data Analysis" (SDA) as the extension of standard Data Analysis to symbolic data tables as input in order to find symbolic objects as output. Any SDA is based on four spaces: the space of individuals , the space of concepts, the space of descriptions modelling individuals or classes of individuals, the space of symbolic objects modelling concepts. Based on these four spaces, new problems appear as the quality, robustness and reliability of the approximation of a concept by a symbolic object, the symbolic description of a class, the consensus between symbolic descriptions etc.. In this paper we give an overview on recent development on SDA. We present some tools and methods of SDA and introduce the SODAS software prototype (issued from the work of 17 teams of nine countries involved in an European project of EUROSTAT) .