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 OLAP 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.