The document presents on multidimensional data models. It discusses the key components of multidimensional data models including dimensions and facts. It describes different types of multidimensional data models such as data cube model, star schema model, snowflake schema model, and fact constellations. The star schema model and snowflake schema model are explained in more detail through examples and their benefits are highlighted.

1. PRESENTATION ON
MULTIDIMENSIONAL DATA
MODEL
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Jagdish Suthar
B. Tech. Final Year
Computer Science and Engineering
Jodhpur National university, Jodhpur

2. MULTIDIMENSIONAL DATA MODEL(MDDM)
Content:-
1. Introduction of MDDM.
2. Component of MDDM.
3. Types of MDM.
[A]. Data Cube Model.
[B]. Star Schema Model.
[C]. Snow Flake Schema Model.
[D]. Fact Constellations.
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3. INTRODUCTION MDDM
 The Dimensional Model was Developed for
Implementing data warehouse and data marts.
 MDDM provide both a mechanism to store data
and a way for business analysis.
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4. COMPONENT OF MDDM
 The
two primary component of dimensional
model are Dimensions and Facts.
Dimensions:- Texture Attributes to analyses
data.
Facts:- Numeric volume to analyze business.
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5. TYPES OF MDDM
[A]. Data Cube Model.
[B]. Star Schema Model.
[C]. Snow Flake Schema Model.
[D]. Fact Constellations.
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6. DATA CUBE DIMENSIONAL
MODEL
 When data is grouped or combined together in
multidimensional matrices called Data Cubes.
 In Two Dimension :- row & Column or Products &fiscal
quarters.
 In Three Dimension:- one regions, products and fiscal
quarters.
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7. CONT.…….
 Changing from one dimensional hierarchy to another
is early accomplished in data cube by a technique called
piroting (also known rotation).
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8. CONT.…
 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 of capability uses can zoom out to see a
summarized level of data.
 The navigation path is determined by hierarchy with in
dimension.
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.
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9. CONT..
 The MDDM involve two types of tables:-
1. Dimension Table: -
 Consists of tupple of attributes of dimension.
 It is Simple Primary Key.
2. Fact Table:-
 A Fact table has tuples, one per a recorded fact.
 It is Compound primary key.
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10. STAR SCHEMA MODEL
 It is also known as Star Join Schema.
 It is the simplest style of data warehouse schema.
 It is called a 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 join but dimension table are
not joined to each other.
 A typical fact table contain key and measure.
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11. CONT.….
 Example of Star Schema:-
Time Item
Sales Fact
Time_key Table Item_key
Day Time_key Item_name
Day of Week Item_key Brand
Month Types
Branch_Key
Quarter Suppiler_types
Location_key
Year
Unit_sold Location
Branch Location_key
Dollar_sold
Branch_Key
Street
Branch_name Average_sales
City
Branch type
State
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Fig.:-Star Schema model Country
Measure

12. CONT..
Advantage of Star Schema Model:-
 Provide highly optimized performance for typical star
queries.
 Provide a direct and intuitive mapping b/w the
business entities being analyzed by end uses and the
schema design.
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13. 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 Flaking effecting only affecting the
dimension tables and not the fact tables.
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14. CONT.….
 Example of Snow Flake Schema:-
Time Sales Fact Item
Time_key Table Item_key
Day Time_key Item_name Supplier
Day of Item_key Brand Supplier_key
Week
Types Supplier_type
Month Branch_Key
Quarter Suppiler_types
Location_key
Year
Unit_sold Location
Branch Location_key
Dollar_sold
Branch_Key Street City
Branch_name Average_sales City _key City_key
Branch type
City
State 14
Fig.:-Snow Flake Schema model
Measures Country

15. CONT..
Benefits of Snow flaking:-
 It is Easier to implement a snow flak Schema when a
multidimensional is added to the typically normalized
tables.
 A Snow flake schema can reflect the same data to the
database.
Difference b/w Star schema and Snow Flake:-
Star Schema Snow Flake
Star Schema dimension are Snow flake Schema
De normalized with each dimension are normalized
dimension being into multiple related 15
represented in single table. tables.

16. FACT CONSTELLATIONS
 It is set of fact tables that share some dimensions
tables.
 It limits the possible queries for the data warehouse.
Fact Table- Fact Table-
1 Dimension Table 2
Product Product No. Product
Quarter Product Name Future
Quarter
Region Product Design
Region
Revenue Product Style
Projected
Business Result Product Line
Revenue
Product
Business 16
Fig.:-Fact Constellations Forecast

17. REFERENCES:-
 Data Mining & Warehousing-Saumya Bajpai.
(Ashirwad Publication ,Jaipur)
 https://www.google.com
 http://en.wikipedia.org/wiki/Dimensional_modeling
 http://www.cs.man.ac.uk/~franconi/teaching/2001/CS636/CS6
36-olap.ppt
 Data Warehouse Models and OLAP Operations, by Enrico
Franconi
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18. THE END
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