PRESENTATION ON

MULTIDIMENSIONAL DATA MODEL

Jagdish Suthar B. Tech. Final Year

Computer Science and Engineering

Jodhpur National university, Jodhpur

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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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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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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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TYPES OF MDDM

[A]. Data Cube Model.

[B]. Star Schema Model.

[C]. Snow Flake Schema Model.

[D]. Fact Constellations.

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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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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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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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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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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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CONT.….

Example of Star Schema:-

Time_key

Item_key

Branch_Key

Location_key

Unit_sold

Dollar_sold

Average_sales

Time_key

Day

Day of Week

Month

Quarter

Year

Time

Branch_Key

Branch_name

Branch type

Branch

Item_key

Item_name

Brand

Types

Suppiler_types

Item

Location_key

Street

City

State

Country

Location

Measure

Sales Fact

Table

Fig.:-Star Schema model 11

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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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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CONT.….

Example of Snow Flake Schema:-

Time_key

Item_key

Branch_Key

Location_key

Unit_sold

Dollar_sold

Average_sales

Time_key

Day

Day of

Week

Month

Quarter

Year

Time

Branch_Key

Branch_name

Branch type

Item_key

Item_name

Brand

Types

Suppiler_types

Branch

Item

Location_key

Street

City _key

Location

Measures

Sales Fact

Table

Supplier_key

Supplier_type

Supplier

City_key

City

State

Country

City

Fig.:-Snow Flake Schema model 14

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

De normalized with each

dimension being

represented in single table.

Snow flake Schema

dimension are normalized

into multiple related

tables.

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FACT CONSTELLATIONS

It is set of fact tables that share some dimensions

tables.

It limits the possible queries for the data warehouse.

Product

Quarter

Region

Revenue

Fact Table-

1

Product

Future

Quarter

Region

Projected

Revenue

Fact Table-

2

Product No.

Product Name

Product Design

Product Style

Product Line

Dimension Table

Business Result

Product Business

Forecast Fig.:-Fact Constellations

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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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THE END

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