Summarizing multidimensional data streams: A hierarchy-graph-based approach

Abstract

When dealing with potentially infinite data streams, storing the whole data stream history is unfeasible and providing a high-quality summary is required. In this paper1, we propose a summarization method for multidimensional data streams based on a graph structure and taking advantage of the data hierarchies. The summarization method considers the data distribution and thus overcomes a major drawback of the Tilted Time Window common framework. 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. © 2010 Springer-Verlag Berlin Heidelberg.