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4.2 Data Warehouse Modeling: Data Cube and OLAP 147

location (countries) USA 2000

Canada
location (cities) Toronto 395 time (quarters) Q1 1000
Q2
Vancouver
time (quarters) Q1 605 Q3
Q4
Q2
computer computer security
home phone
home dice for entertainment
entertainment
(location = “Toronto” or “Vancouver”) item (types)
item (types) and (time = “Q1” or “Q2”) and
(item = “home entertainment” or “computer”) roll-up
on location
(from cities
to countries)
location (cities) Toronto Chicago 440
New York 1560
395
Vancouver
time (quarters) Q1 605 825 14 400
Q2
slice Q3
for time=“Q1” Q4 drill-down
on time
(from quarters
computer security
Chicago
location (cities) New York entertainment phone location (cities) New York
to months)
home
item (types)
Chicago
Toronto
Toronto
605 825 14 400
Vancouver
January
computer security Vancouver 150
home phone February 100
entertainment March 150
item (types) April
pivot May
June
July
home 605 time (months) August
entertainment
item (types) computer 825 September
October
14
phone
November
security 400 December
computer security
New York Vancouver
home phone
Chicago Toronto
entertainment
location (cities)
item (types)
Figure 4.12 Examples of typical OLAP operations on multidimensional data.