查看文章

Logistic regression is an important technique for analyzing and predicting data with categorical attributes. In this paper, We consider supporting online analytical processing (OLAP) of logistic regression analysis for multi-dimensional data in a data cube where it is expensive in time and space to build logistic regression models for each cell from the raw data. We propose a novel scheme to compress the data in such a way that we can reconstruct logistic regression models to answer any OLAP query without accessing the raw data. Based on a first-order approximation to the maximum likelihood estimating equations, we develop a compression scheme that compresses each base cell into a small compressed data block with essential information to support the aggregation of logistic regression models. Aggregation formulae for deriving high-level logistic regression models from lower level component cells are given …