People nowadays are relying more and more on OLAP data to find business solutions. A typical OLAP data cube usually contains four to eight dimensions, with two to six hierarchical levels and tens to hundreds of categories for each dimension. It is often too large and has too many levels for users to browse it effectively. In this thesis we propose a system prototype which will guide users to efficiently explore exceptions in data cubes. It automatically computes the degree of exceptions for cube cells at different aggregation levels. When user browses the cube, exceptional cells as well as interesting drilling-down paths that will lead to lower level exceptions are highlighted according to their interestingness. Different statistical methods such as log-linear model, adapted linear model and Z-tests are used to compute the degree of exceptions. We present algorithms and address the issue of improving the performance on large data sets.; Our study on exceptions leads to mining quantitative association rules.; The thesis also introduces an efficient method for implementing boxplots in the DBMiner System. (Abstract shortened by UMI.)
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