Summarizing Data Cubes Using Blocks
Yeow Wei Choong (HELP University, Malaysia), Anne Laurent (Université Montpellier II, France) and Dominique Laurent (Université de Cergy - Pontoise, France)
Copyright: © 2008
In the context of multidimensional data, OLAP tools are appropriate for the navigation in the data, aiming at discovering pertinent and abstract knowledge. However, due to the size of the data set, a systematic and exhaustive exploration is not feasible. Therefore, the problem is to design automatic tools to ease the navigation in the data and their visualization. In this chapter, we present a novel approach allowing to build automatically blocks of similar values in a given data cube that are meant to summarize the content of the cube. Our method is based on a levelwise algorithm (a la Apriori) whose complexity is shown to be polynomial in the number of scans of the data cube. The experiments reported in the chapter show that our approach is scalable, in particular in the case where the measure values present in the data cube are discretized using crisp or fuzzy partitions.