Data Warehouse Concepts - Data Warehouse Systems: Design and Implementation - page 61

q r

Köln Köln

Berlin Berlin

Lyon Lyon

Paris Paris

Jan Jan

Feb Feb

Mar Mar

... ...

Dec Dec

Produce Seafood Produce Seafood

Beverages Beverages

Condiments Condiments

Product (Category) Product (Category)

s

t Madrid

Bilbao

Köln Köln

Berlin Berlin

Lyon Lyon

Paris Paris

Q1 Q1

Q2 Q2

Q3 Q3

Q4 Q4

Produce Seafood Produce Seafood

Beverages Condiments Beverages Condiments

Product (Category) Product (Category)

Fig. 3.4 (continued) ( q ) Three-month moving average. ( r ) Year-to-date compu-
tation. ( s ) Union of the original cube and another cube with data from Spain.
( t ) Difference of the original cube and the cube in ( m )

Our user then notices that sales of the category Seafood in France are
significantly higher in the first quarter compared to the other ones. Thus,
shefirsttakesthecubebacktothe City aggregation level and then applies a
drill-down along the Time dimension to the Month level to find out whether
this high value occurred during a particular month. In this way, she discovers
that, for some reason yet unknown, sales in January soared both in Paris and
in Lyon, as shown in Fig. 3.4 c.