Summarizing Multidimensional Data Streams: A Hierarchy-Graph-Based Approach

With the rapid development of information technology, many applications have to deal with potentially infinite data streams. In such a dynamic context, storing the whole data stream history is unfeasible and providing a high-quality summary is required for decision makers. In this paper, we propose a summarization method for multidimensional 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 major drawback 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 numerous 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.

• Type: Journal
• Year: 2010
• Where: PAKDD
• Authors: Yoann Pitarch, Anne Laurent, Pascal Poncelet
• Keywords: Data Mining, Data Streams, Infinite Data Streams, PAKDD 2010, Tilted Time Window