Multi Dimensional Data Model

By, S. Moni Sindhu

What is Data Model ?

Collection of conceptual tools for describing data, data relationships, data semantics and consistency constraint.

Conceptual representation of data structures required for database

What is Multi Dimensional Data Model ?

Model for data management where the databases are developed according to user's preferences, in order to be used for specific types of retrievals.

Multidimensional database (MDB) is mainly optimized for data warehouse and online analytical processing (OLAP) applications

Multidimensional data-base technology is a key factor in the interactive analysis of large amounts of data for decision-making purposes

MDB mainly useful for sales and marketing applications that involve time series.

Why Multi Dimensional 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

View data as multidimensional cubes , which have been particularly well suited for data analyses

Enforces simplicity

Components of MDDM

Types of MDDM

Data Cube Model Star Schema Model Snow Flake Schema ModelFact Constellations Schema Model (Global Schema)

Data Cube Model

Data is grouped or combined together in multidimensional matrices called Data Cubes.

In two Dimension :- row & column or products. In three Dimension :- one regions, products and fiscal quarters.

data cubes have categories of data called dimensions and measures.

measure ◦ represents some fact (or number) such as cost or

units of service. dimension

◦ represents descriptive categories of data such as time or location.

Dimensions and measures

Slicing : Refers to two- dimensional page selected

from the cube. Dicing : Dicing provides you the smallest available

slice. Define a sub-cube of the original space. Rotation : Rotating changes the dimensional

orientation of the report from the cube data.

Slicing , Dicing and Rotation

Slicing Dicing

Rotation

Star schema Model

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.

Example of star schema:-

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.

Example of Snow flake schema:-

Fact constellations

It is a set of fact tables that shares some dimensional tables.

It limits the possible queries for the data warehouse.