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138 Chapter 4 Data Warehousing and Online Analytical Processing 3:17 Page 138 #14

location (cities) Chicago 854 882 89 623

New York
Toronto 818 1087 968 43 38 591 872
746
Vancouver 698
925
Q1 605 825 14 400 682 1002 789
time (quarters) Q2 680 1023 31 512 728 984 870
952
Q3
501
812
30
784
Q4 927 1038 38 580
computer security
home phone
entertainment
item (types)

Figure 4.3 A 3-D data cube representation of the data in Table 4.3, according to time, item, and location.
The measure displayed is dollars sold (in thousands).

location (cities) Chicago supplier = “SUP1” supplier =“SUP2” supplier=“SUP3”

New York
Toronto
Vancouver
time (quarters) Q1 605 825 14 400
Q2
Q3
Q4
computer security computer security computer security
home phone home phone home phone
entertainment entertainment entertainment
item (types) item (types) item (types)

Figure 4.4 A 4-D data cube representation of sales data, according to time, item, location, and supplier.
The measure displayed is dollars sold (in thousands). For improved readability, only some of
the cube values are shown.

and location, summarized for all suppliers. The 0-D cuboid, which holds the highest level
of summarization, is called the apex cuboid. In our example, this is the total sales, or
dollars sold, summarized over all four dimensions. The apex cuboid is typically denoted
by all.