Résumé
Considering relational tables as the object of analysis, methods to summarize them can help the analyst to have a starting point to explore the data. Typically, table summarization aims at producing an informative data summary through the use of metadata supplied by attribute taxonomies. Nevertheless, such a hierarchical knowledge is not always available or may even be inadequate when existing. To overcome these limitations, we propose a new framework, named cTabSum, to automatically generate attribute value taxonomies and directly perform table summarization based on its own content. Our innovative approach considers a relational table as input and proceeds in a two-step way. First, a taxonomy for each attribute is extracted. Second, a new table summarization algorithm exploits the automatic generated taxonomies. An information theory measure is used to guide the summarization process. Associated with the new algorithm we also develop a prototype. Interestingly, our prototype incorporates some additional features to help the user familiarizing with the data: (i) the resulting summarized table produced by cTabSum can be used as recommended starting point to browse the data; (ii) some very easy-to-understand charts allow to visualize how taxonomies have been so built; (iii) finally, standard OLAP operators, i.e. drill-down and roll-up, have been implemented to easily navigate within the data set. In addition we also supply an objective evaluation of our table summarization strategy over real data.