Knowledge-free table summarization
Résumé de table relationnelle sans connaissance apriori
Ienco, D. ; Pitarch, Y. ; Poncelet, P. ; Teisseire, M.
Type de document
Communication scientifique avec actes
Affiliation de l'auteur
IRSTEA MONTPELLIER UMR TETIS FRA ; CNRS LIRIS LYON FRA ; IRSTEA MONTPELLIER UMR TETIS FRA ; IRSTEA MONTPELLIER UMR TETIS FRA
Résumé / Abstract
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.
15th International Conference, DaWaK 2013, 26/08/2013 - 29/08/2013, Prague, CZE
Data Warehousing and Knowledge Discovery
Springer Berlin Heidelberg