Explaining differences in multidimensional aggregates

Our goal is to enhance multidimensional database systems with advanced mining primitives. Current Online Analytical Processing (OLAP) products provide a minimal set of basic aggregate operators like sum and average and a set of basic navigational operators like drill-downs and roll-ups. These operators have to be driven entirely by the analyst's intuition. Such ad hoc exploration gets tedious and error-prone as data dimensionality and size increases. In earlier work we presented one such advanced primitive where we pre-mined OLAP data for exceptions, summarized the exceptions at appropriate levels, and used them to lead the analyst to the interesting regions. In this paper we present a second enhancement: a single operator that lets the analyst get summarized reasons for drops or increases observed at an aggregated level. This eliminates the need to manually drill-down for such reasons. We develop an information theoretic formulation for expressing the reasons that is compact and easy to interpret. We design a dynamic programming algorithm that requires only one pass of the data improving significantly over our initial greedy algorithm that required multiple passes. In addition, the algorithm uses a small amount of memory independent of the data size. This allows easy integration with existing CLAP products. We illustrate with our prototype on the DB2/UDB ROLAP product with the Excel Pivot-table frontend. Experiments on this prototype using the OLAP data benchmark demonstrate (1) scalability of our algorithm as the size and dimensionality of the cube increases and (2) feasibility of getting interactive answers even with modest hardware resources