Issue No. 12 - December (2006 vol. 18)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2006.196
As OLAP engines are widely used to support multidimensional data analysis, it is desirable to support in data cubes advanced statistical measures, such as regression and filtering, in addition to the traditional simple measures such as count and average. Such new measures allow users to model, smooth, and predict the trends and patterns of data. Existing algorithms for simple distributive and algebraic measures are inadequate for efficient computation of statistical measures in a multidimensional space. In this paper, we propose a fundamentally new class of measures, compressible measures, in order to support efficient computation of the statistical models. For compressible measures, we compress each cell into an auxiliary matrix with a size independent of the number of tuples. We can then compute the statistical measures for any data cell from the compressed data of the lower-level cells without accessing the raw data. Time- and space-efficient lossless aggregation formulae are derived for regression and filtering measures. Our analytical and experimental studies show that the resulting system, regression cube, substantially reduces the memory usage and the overall response time for statistical analysis of multidimensional data
data analysis, data compression, data mining, data warehouses, regression analysis,regression cubes, lossless compression, lossless aggregation, OLAP engines, multidimensional data analysis, data cubes, statistical analysis,Multidimensional systems, Filtering, Engines, Data analysis, Predictive models, Distributed computing, Extraterrestrial measurements, Size measurement, Loss measurement, Delay,Aggregation, compression, data cubes, OLAP.
"Regression Cubes with Lossless Compression and Aggregation", IEEE Transactions on Knowledge & Data Engineering, vol. 18, no. , pp. 1585-1599, December 2006, doi:10.1109/TKDE.2006.196