- Multidimensional data modeling (MDDM) was developed for data warehousing and marts. It allows for interactive analysis of large amounts of data to aid decision making. - Dimensions and measures make up data cubes, with dimensions being categories like time and location, and measures being numeric facts. Slicing, dicing, and rotating allow analyzing the data from different angles. - Star schemas and snowflake schemas are common MDDM structures. Star schemas have a central fact table linked to dimension tables, while snowflake schemas further normalize the dimension tables. Fact constellations link multiple fact tables through shared dimensions.

1. MULTIDIMENSIONAL DATA
MODEL
SUBMITTED BY-
JASBEER CHAUHAN,15071257
HARJINDER KHAN,15071258
SUBMITTED TO-
NEHA DOGRA

2. INTRODUCTION MDDM:-
• MDDM was Developed for implementing data
warehouse and data mart.
• It provide Both a mechanism to store Data and
a way for business analysis.
• It provide us interactive analysis of large
amount of data which help’s in decision
making process.

3. Why Multidimensional Database ?
Enables interactive analyses of large amounts
of data for decision-making purposes.
Rapidly process the data in the database so
that answers can be generated quickly.
Provides “just in time” information for
effective decision-making in a successful OLAP
application.
Enforces simplicity.

4. COMOPONENT OF MDDM:-

5. TYPES OF MDDM
• Data Cube Model
• Star Schema Model
• Snow flake Schema Model
• Fact constellations

6. Data Cube Dimensional
Model
• When data is combined together in
multidimensional matrices called Data Cubes.
• 2D-It consists of row and column or products
and fiscal quarters.

7. Conti..
• 3D- one regions , products and fiscal quarters.

8. Dimensions and measures
 Data cubes have categories of data called
dimensions and measures
 Measure- It represents some fact (number)
such as cost or units of service.
 Dimension- It represents descriptive
categories of data such as time or location.

10. Slicing, Dicing and Rotation
Slicing:
Refers to two – dimensional page selected
from the cube
Dicing :
Dicing provides you a smallest available slice.
It define a sub-cube of the original space.

11. Conti..
Slicing Dicing

12. Conti..
• Changing from one dimensional hierarchy to
another is early accomplished in data cube by
a technique called Rotation.

13. Conti..
• These types of models are applied to
hierarchical view such as Role up Display and
Drill Down Display.
Role up Display:-
 When role up operation is performed by dimension reduction
one or more dimension are remove from dimension cube.
 With role up capability user can zoom out to see a summarized level of
data.
 The navigation path is determined by hierarchy with in dimension,

14. Conti..
Drill-Down Display:-
 It is reverse of role up.
 it navigate from less detailed data to more detailed data.
 It can also be performs by adding new dimension to a cube.

15. STAR SCHEMA MODEL
• It is also known as star join schema
• It is simplest style of data warehouse schema.
• It is called Star Schema because the entity relationship
diagram of this schema resembles a star , with points
radiating from central table.
• A star query is a join between a fact table and a no of
dimension table.
• Each dimension table is joined to the fact table using
primary key to foreign key but dimension table not
joined to each other.
• A typical fact table consist of key and measure.

16. Conti..

17. Conti..
Advantage of Star Schema Model :-
Provide highly optimized performance for
typical star queries.
Provide a direct and intuitive mapping
between the business entities being analyzed
by end users.

18. SNOW FLAKE SCHEMA
It is slightly different from a star schema in
which the dimensional tables from a star
schema are organized into a hierarchy by
normalizing them.
The snow flake schema is represented by
centralized fact table which are connected to
multiple dimensions.
The snow flake effecting only affecting the
dimension table not the fact tables.

19. Conti..

20. Conti..
Benefits of Snow Flaking:-
I. It is easier to implement a snow flak schema
when a multidimensional is added to the
typically normalized tables.
II. A snow flake schema can reflect the same
data to the database.

21. Difference between snow flak schema and
star schema
Snow flak schema Star schema
No redundancy redundancy
More complex queries Less complex queries
Lots of foreign keys so it needed more
execution time.
Quick executions.
More no of dimensions for a single
dimension.
Only one dimension.
Normalized De-normalized

22. FACT CONSTELLATIONS:-
For each schema it is possible to construct fact
constellation table.
It limits the possible Queries for the data
warehouse.
The fact constellation architecture contains
multiple fact tables that share many
dimensional tables.

23. Conti..
 The main shortcoming of fact constellation schema is a more
complicated design because many variants of particular kinds
of aggregation must be considered and selected. Moreover,
dimensions tables are still large.

24. THANK
YOU