Driven Exploration Of Data Cubes

Introduction:

As studied in previous sections, a data cube may have a large number of cuboids, and each cuboid may contain a large number of (aggregate) cells. With such an overwhelmingly large space, it becomes a burden for users to even just browse a cube, let alone think of exploring it thoroughly. Tools need to be developed to assist users in intelligently exploring the huge aggregated space of a data cube.

Discovery-driven exploration is such a cube exploration approach. In discovery driven exploration, pre computed measures indicating data exceptions are used to guide the user in the data analysis process, at all levels of aggregation. We hereafter refer to these measures as exception indicators. Intuitively, an exception is a data cube cell value that is significantly different from the value anticipated, based on a statistical model. The model considers variations and patterns in the measure value across all of the dimensions to which a cell belongs. For example, if the analysis of item-sales data reveals an increase in sales in December in comparison to all other months, this may seem like an exception in the time dimension. However, it is not an exception if the item dimension is considered, since there is a similar increase in sales for other items during December. The model considers exceptions hidden at all aggregated group-by’s of a data cube. Visual cues such as background color are used to reflect the degree of exception of each cell, based on the pre computed exception indicators. Efficient algorithms have been proposed for cube construction, as discussed in Section 4.1. The computation of exception indicators can be overlapped with cube construction, so that the overall construction of data cubes for discovery-driven exploration is efficient.

Three measures are used as exception indicators to help identify data anomalies. These measures indicate the degree of surprise that the quantity in a cell holds, with respect to its expected value. The measures are computed and associated with every cell, for all levels of aggregation. They are as follows:

SelfExp: This indicates the degree of surprise of the cell value, relative to other cells at the same level of aggregation.

InExp: This indicates the degree of surprise somewhere beneath the cell, if we were to drill down from it.

PathExp: This indicates the degree of surprise for each drill-down path from the cell.