The Polynomial Complexity of Fully Materialized Coalesced Cubes Yannis Sismanis and Nick Roussopoulos   View Paper (PDF)   Return to RESEARCH SESSION 14:DATA WAREHOUSING Abstract The data cube operator encapsulates all possible groupings of a dataset and has proved to be an invaluable tool in analyzing vast amountsof data. However its apparent exponential complexity has significantly limited its applicability to low dimensional datasets. Recently the idea of the coalesced cube was introduced, and showed that high-dimensional coalesced cubes are orders of magnitudes smaller in size than the original data cubes even when they calculate and store every possible aggregation with 100% precision. In this paper we present an analytical framework for estimating the size of coalesced cubes. By using this framework on uniform coalesced cubes we show that their size and the required computation time scales polynomially with the dimensionality of the data set and, therefore, a full data cube at 100% precision is not inherently cursed by high dimensionality. Additionally, we show that such coalesced cubes scale polynomially (and close to linearly) with the number of tuples on the dataset. We were also able to develop an efficient algorithm for estimating the size of coalesced cubes before actually computing them, based only on metadata about the cubes. Finally, we complement our analytical approach with an extensive experimental evaluation using real and synthetic data sets, and demonstrate that not only uniform but also zipfian and real coalesced cubes scale polynomially. ©2005 Association for Computing Machinery