2. A multi-dimensional data model
Location
Time
Item
The word cube brings to mind a 3-D object, and we can think of a 3-D data cube as being a set of similarly structured 2-D tables stacked on top of one another. We can think of a 4-D data cube as consisting of a series of 3-D cubes.
If we continue in this way, we may display any n-D data as a series of (n - 1)-D “cubes". The data cube is a metaphor for multidimensional data storage. The actual physical storage of such data may differ from its logical representation.

3. Multi-dimensional Data Model
A multidimensional data model views data in the form of a data cube.
A data cube allows data to be modeled
and viewed in multiple dimensions. It is defined by dimensions and facts.
Dimensions are the perspectives or entities with respect to which an organization wants to keep records.
It represents data by means of a multidimensional N grid Q which is Cartesian product of certain discrete sets Di called dimension.
Elements of a dimensions are called members.
The set of dimension & members forms metadata.

4. Example Loc= Chicago Loc= New York Loc=Toronto Loc= Vancouver Item
home
ent. Comp. phone Sec.
Time Q1 Q2 Q3 Q4

5. Multi-dimensional Data Model
Dimensions: Product, Region, Time
Hierarchical summarization paths
Product Region Time
Industry Country Year
Category Region Quarter
Product City Month week
Office Day
Region W S N
Product
Toothpaste
Juice
Cola
Milk
Cream
Soap
Month 1 2 3 4 7 6 5

6. Conceptual Modeling of Data Warehouses
The most popular data model for data warehouses is a multidimensional model. This model can exist in the form of a star schema, a snowflake schema, or a fact constellation schema.
Star schema: The most common modeling paradigm is the star schema in which the data warehouse contains
A large central table (fact table) containing the bulk of the data, with no redundancy, and
A set of smaller attendant tables (dimension tables), one for each dimension. The schema graph resembles a starburst, with the dimension tables displayed in a radial pattern around the central fact table.

8. Example of Star Schema item branch time Sales Fact Table time_key
day
day_of_the_week
month
quarter
year
time
item_key
item_name
brand
type
supplier_type
item
Sales Fact Table
time_key
item_key
branch_key
branch_key
branch_name
branch_type
branch
location_key
street
city
state_or_province
country
location
location_key
units_sold
dollars_sold
avg_sales
Measures

9. The Snowflake schema: The snowflake schema is a variant of the star schema model, where some dimension tables are normalized, thereby further splitting the data into additional tables. The resulting schema graph forms a shape similar to a snowflake.
The major difference between the snowflake and star schema models is that the dimension tables of the snowflake model may be kept in normalized form. Such a table is easy to maintain and saves storage space.
The snowflake structure can reduce the effectiveness of browsing since more joins will be needed to execute a query. Consequently, the system performance may be adversely impacted.

10. Example of Snowflake Schema
time_key
day
day_of_the_week
month
quarter
year
time
item_key
item_name
brand
type
supplier_key
item
supplier_key
supplier_type
supplier
Sales Fact Table
time_key
item_key
branch_key
location_key
street
city_key
location
branch_key
branch_name
branch_type
branch
location_key
units_sold
city_key
city
state_or_province
country
dollars_sold
avg_sales
Measures

11. Fact schema: Sophisticated applications may require multiple fact tables to share dimension tables. This kind of schema can be viewed as a collection of stars, and hence is called a galaxy schema or a fact constellation.

12. Example of Fact Constellation
time_key
day
day_of_the_week
month
quarter
year
time
item_key
item_name
brand
type
supplier_type
item
Shipping Fact Table
Sales Fact Table
time_key
item_key
time_key
shipper_key
item_key
from_location
branch_key
branch_key
branch_name
branch_type
branch
location_key
to_location
location_key
street
city
province_or_state
country
location
dollars_cost
units_sold
units_shipped
dollars_sold
avg_sales
shipper_key
shipper_name
location_key
shipper_type
shipper
Measures