Lecture 10:
More OLAP - Dimensional modeling
www.cl.cam.ac.uk/Teaching/current/Databases/
1
Conceptual Modeling of Data
Warehouses
Modeling data warehouses: dimensions & measures
– Star schema: A fact table in the middle connected to a set of
dimension tables
– Snowflake schema: A refinement of star schema where some
dimensional hierarchy is normalized into a set of smaller
dimension tables, forming a shape similar to snowflake
– Fact constellations: Multiple fact tables share dimension tables,
viewed as a collection of stars, therefore called galaxy schema or
fact constellation
2
Star
product
prodId
p1
p2
name price
bolt
10
nut
5
sale oderId date
o100 1/7/97
o102 2/7/97
105 3/8/97
customer
custId
53
81
111
store storeId
c1
c2
c3
custId
53
53
111
name
joe
fred
sally
prodId
p1
p2
p1
storeId
c1
c1
c3
address
10 main
12 main
80 willow
qty
1
2
5
city
nyc
sfo
la
amt
12
11
50
city
sfo
sfo
la
3
Star Schema
product
prodId
name
price
sale
orderId
date
custId
prodId
storeId
qty
amt
customer
custId
name
address
city
store
storeId
city
4
Terms
• Fact table
• Dimension tables
• Measures
product
prodId
name
price
sale
orderId
date
custId
prodId
storeId
qty
amt
customer
custId
name
address
city
store
storeId
city
5
Another Star Schema
time
item
time_key
day
day_of_the_week
month
quarter
year
Sales Fact Table
time_key
item_key
branch_key
branch
location_key
branch_key
branch_name
branch_type
units_sold
dollars_sold
avg_sales
item_key
item_name
brand
type
supplier_type
location
location_key
street
city
province_or_street
country
Measures
6
Dimension Hierarchies
sType
store
store storeId
s5
s7
s9
city
cityId
sfo
sfo
la
tId
t1
t2
t1
mgr
joe
fred
nancy
 snowflake schema
 constellations
region
sType tId
t1
t2
size
small
large
city cityId pop
sfo
1M
la
5M
location
downtown
suburbs
regId
north
south
region regId
name
north cold region
south warm region
7
Cube
Fact table view:
sale
prodId storeId
p1
c1
p2
c1
p1
c3
p2
c2
Multi-dimensional cube:
amt
12
11
50
8
p1
p2
c1
12
11
c2
c3
50
8
dimensions = 2
8
3-D Cube
Fact table view:
sale
prodId
p1
p2
p1
p2
p1
p1
storeId
c1
c1
c3
c2
c1
c2
Multi-dimensional cube:
date
1
1
1
1
2
2
amt
12
11
50
8
44
4
day 2
day 1
p1
p2 c1
p1
12
p2
11
c1
44
c2
4
c2
c3
c3
50
8
dimensions = 3
9
Aggregates
• Add up amounts for day 1
• In SQL: SELECT sum(amt) FROM SALE
WHERE date = 1
sale
prodId storeId
p1
c1
p2
c1
p1
c3
p2
c2
p1
c1
p1
c2
date
1
1
1
1
2
2
amt
12
11
50
8
44
4
81
10
Aggregates
• Add up amounts by day
• In SQL: SELECT date, sum(amt) FROM SALE
GROUP BY date
sale
prodId storeId
p1
c1
p2
c1
p1
c3
p2
c2
p1
c1
p1
c2
date
1
1
1
1
2
2
amt
12
11
50
8
44
4
ans
date
1
2
sum
81
48
11
Another Example
• Add up amounts by day, product
• In SQL: SELECT date, sum(amt) FROM SALE
GROUP BY date, prodId
sale
prodId storeId
p1
c1
p2
c1
p1
c3
p2
c2
p1
c1
p1
c2
date
1
1
1
1
2
2
amt
12
11
50
8
44
4
sale
prodId
p1
p2
p1
date
1
1
2
amt
62
19
48
rollup
drill-down
12
Aggregates
• Operators: sum, count, max, min,
median, ave
• “Having” clause
• Using dimension hierarchy
– average by region (within store)
– maximum by month (within date)
13
Cube Aggregation
day 2
day 1
p1
p2 c1
p1
12
p2
11
p1
p2
c1
56
11
c1
44
c2
4
c2
c3
Example: computing sums
...
c3
50
8
c2
4
8
rollup
drill-down
c3
50
sum
c1
67
c2
12
c3
50
129
p1
p2
sum
110
19
14
Cube Operators
day 2
day 1
p1
p2 c1
p1
12
p2
11
p1
p2
c1
56
11
c1
44
c2
4
c2
c3
...
c3
50
sale(c1,*,*)
8
c2
4
8
c3
50
sale(c2,p2,*)
sum
c1
67
c2
12
c3
50
129
p1
p2
sum
110
19
sale(*,*,*)
15
Extended Cube
c2
4
8
c312
p1
p2
c1
*
12
p1
p2
c1*
44
c1
56
11
c267
4
c2
44
c3
4
50
11
23
8
8
50
*
62
19
81
*
day 2
day 1
p1
p2
*
c3
50
* 50
48
48
*
110
19
129
sale(*,p2,*)
16
Aggregation Using
Hierarchies
day 2
day 1
p1
p2 c1
p1
12
p2
11
c1
44
c2
4
c2
c3
c3
50
customer
region
8
country
p1
p2
region A region B
56
54
11
8
(customer c1 in Region A;
customers c2, c3 in Region B)
17
Pivoting
Fact table view:
sale
prodId storeId
p1
c1
p2
c1
p1
c3
p2
c2
p1
c1
p1
c2
Multi-dimensional cube:
date
1
1
1
1
2
2
amt
12
11
50
8
44
4
day 2
day 1
p1
p2 c1
p1
12
p2
11
p1
p2
c1
56
11
c1
44
c2
4
c2
c3
c3
50
8
c2
4
8
c3
50
18