The document discusses different types of multidimensional data models (MDDM) used for data warehousing. It describes MDDM as providing both a mechanism for storing data and enabling business analysis. The main types discussed are star schema, snowflake schema, and fact constellation. Star schema has one central fact table connected to multiple dimension tables, resembling a star. Snowflake schema is similar but dimensional tables are normalized into hierarchies. Fact constellation has multiple fact tables sharing some dimensional tables.
2. INDEX:-
1. Definition of Multidimensional Data Models?
2. Types of MDDM:-
a. Star Schema
b. Data Cube Schema
c. Snowflake Schema
d. Fact Constellation schema
(Global Schema)
3. MULTIDIMENSIONAL DATA MODEL:-
The MDDM was developed for implementing
data warehouse and data marts.
MDDM provide both a mechanism to store data
and a way for business analysis.
4. TYPES OF MDDM:-
A. Data Cube Model.
B. Star Schema Model
C. Snow Flake Schema Model
D. Fact Constellations Schema Model
(Global Schema)
5. DATA CUBE MODEL:-
When data is grouped or combined together in
multidimensional matrices called Data Cubes.
In 2 Dimension:- row & column or products.
In 3 Dimension:- one regions, products and fiscal
quarters. Regions
product
Reg 1 Reg 2 Reg 3
P1
P2
P3
p4
Fig:-Data Cube
6. CONT……….
Changing from one dimensional hierarchy to
another is early accomplished in data cube by a
technique called rotation.
7. STAR SCHEMA:-
It is the simplest form of data warehousing
schema.
It consists one large central table (fact) containing
the bulk of data and a set of smaller dimension
tables one for each dimension .
Its entity relationship diagram between
dimensions and fact table resembles a star where
one fact table is connected to multiple dimensions
or table.
9. SNOW FLAKE SCHEMA:-
It is difficult from a star schema .
In this dimensional table are organized into
hierarchy by normalization them.
The Snow Flake Schema is represented by
centralized fact table which are connected to
multiple dimensions.