1. Data Warehousing Multidimensional Analysis
Joachim Hammer

2. Data Warehousing Architecture
Data Warehouse
Server
Information Sources
OLAP Servers
Clients
MOLAP
Analysis
Semistructured
Sources
Data
Warehouse
extract
transform
load
refresh
etc.
Query/Reporting
serve
ROLAP
Operational
DB’s
Data Mining
Data Marts

3. Multidimensional Modeling
Support ad-hoc querying for business analyst
Think in terms of spreadsheets
View sales data by geography, time, or product
Away from traditional ER models
“One fact in one location”
Entities are inter-related through a series of joins

4. Multidimensional Data Model
Database is a set of facts in multidimensional space
Measures
Numerical data being tracked
Stored in central fact table
E.g., sales, inventory, expenditures
Dimensions
Business parameters
E.g., time, geography, account data

5. Key columns joining fact table
Example
“Sales by product line manager over the past six months”
Key columns joining fact table
to dimension tables
Numerical Measures
Account Info
Prod Code Time Code Acct Code Sales Qty
Product Info
Time Info
. . .

6. Multidimensional Data Model Cont’d
Dimensions have attributes
Organized into hierarchies
E.g., Time dimension: days  weeks  quarters
E.g., Product dimension: product  product line  brand
Operators to navigate the hierarchies
“Roll-up”
Drill-down is the opposite of roll-up
Slice (defines a subcube)
Various visualization ops (e.g., pivot)
Physical architecture of dimensional model is described by “star” schema

7. Star Schema Single fact table and a single table for each dimension
Dimension tables are denormalized
E.g., dimension attributes may be stored multiple times
ProductCode ProductName ProductColor BrandCode
Normalized Representation
BrandCode BrandMgr
ProductCode ProductName ProductColor BrandCode BrandMgr
Denormalized
Example:
100 separate products
5 brands
lots of redundancies

8. Advantages of Star Schema
Reduces the number of physical joins
Simplify the view of the data model
Allows rel. easy maintenance

9. Example Dimensions: Time, Product, Geography Attributes:
roll-up to region
Dimensions:
Time, Product, Geography
Attributes:
Product (upc, price, …)
Geography … …
Hierarchies:
Product  Brand  …
Day  Week  Quarter
City  Region  Country
Geography NY SF
roll-up to brand LA
Juice
Milk
Coke
Cream
Soap
Bread 10 34 56 32 12
Product
roll-up to week
M T W Th F S S
Time
56 units of bread sold in LA on M

10. Example Cont’d Geography Time Sales Product Account Geography Code
Region Code
Region Mgr
City Code
City Name
Time Code
Quarter Code
Quarter Name
Week Code
Day Code
Day name
Sales
Geography Code
Time Code
Account Code
Dollar Amount
Units
Product
Account
Product Code
Product Name
Brand Mgr
Brand Code
Prod. Line Code
Prod. Line Name
Prod. Name
...
Account Code
Key Account Code
Account Name
Account Type
Account Market