Résumé
Nowadays, many applications have to deal with potentially infinite data streams.Thus, storing the whole data stream history is un- feasible and providing a high-quality summary is required for decision makers. In this paper, we propose a summarization method for multidi- mensional data streams based on a graph structure and taking advantage of the data hierarchies. The summarization method we propose takes into account the data distribution and thus overcomes a ma jor draw- back of the Tilted Time Window common framework. Finally, we adapt this structure for synthesizing frequent itemsets extracted on temporal windows. Thanks to our approach, as users do not analyze any more nu- merous extraction results, the result processing is improved. Experiments conducted on both synthetic and real datasets show that our approach can be applied on data streams.