Data Warehouse Concepts - Data Warehouse Systems: Design and Implementation - page 62

Our user now wants to explore alternative visualizations of the cube to
better understand the data contained in it. For this, she sorts the products
by name, as shown in Fig. 3.4 d. Then, she wants to see the cube with the
Time dimension on the x -axis. Therefore, she takes the original cube and
rotates the axes of the cube without changing granularities. This is called
pivoting and is shown in Fig. 3.4 e.
Continuing her browsing of the original cube, the user then wants to
visualize the data only for Paris. For this, she applies a slice operation
that results in the subcube depicted in Fig. 3.4 f. Here, she obtained a two-
dimensional matrix, where each column represents the evolution of the sales
quantity by category and quarter, that is, a collection of time series.
As her next operation, our user goes back to the original cube and builds
a subcube, with the same dimensionality, but only containing sales figures
for the first two quarters and for the cities Lyon and Paris. This is done with
a dice operation, as shown in Fig. 3.4 g.
Our user now wants to compare the sales quantities in 2012 with those in
2011. For this, she needs the cube in Fig. 3.4 h, which has the same structure
as the one for 2012 given in Fig. 3.4 a. She wants to have the measures in
the two cubes consolidated in a single one. Thus, she uses the drill-across
operation that, given two cubes, builds a new one with the measures of both
in each cell. This is shown in Fig. 3.4 i.
The user now wants to compute the percentage change of sales between the
2 years. For this, she takes the cube resulting from the drill-across operation
above, and applies to it the add measure operation, which computes a new
value for each cell from the values in the original cube. The new measure is
shown in Fig. 3.4 j.
After all these manipulations, the user wants to aggregate data in various
ways. Given the original cube in Fig. 3.4 a, she first wants to compute to
total sales by quarter and city. This is obtained by the sum aggregation
operation, whose result is given in Fig. 3.4 k. Then, the user wants to obtain
the maximum sales by quarter and city, and for this, she uses the max
operation to obtain the cube in Fig. 3.4 l. After seeing the result, she decides
that she needs more information; thus, she computes the top two sales by
quarter and city, which is also obtained with the max operation yielding the
cube in Fig. 3.4 m.
In the next step, the user goes back to the original cube in Fig. 3.4 aand
computes the quarterly sales that amount to 70% of the total sales by city and
category. She explores this in two possible ways: according to the ascending
order of quarters, as shown in Fig. 3.4 n, and according to the descending
order of quantity, as shown in Fig. 3.4 o. In both cases, she applies the top
percent aggregation operation. She also wants to rank the quarterly sales
by category and city in descending order of quantity, which is obtained in
Fig. 3.4 p.