multi dimensional data model

Data mining - multi dimensional data model

1. Multi Dimensional Data Model By, S. Moni Sindhu

2. What is Data Model ?

3. C ollection of conceptual tools for describing data, data relationships, data semantics and consistency constraint . C onceptual representation of data structures required for database

4. What is Multi Dimensional Data Model ?

5. M odel 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

6. 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.

7. Why Multi Dimensional Database

8. 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. P rovides “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

9. Components of MDDM

10. Types of MDDM

11. Data Cube Model Star Schema Model Snow Flake Schema Model Fact Constellations Schema Model (Global Schema)

12. Data Cube Model

13. 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.

14. 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

15. 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

16. Slicing Dicing Rotation

17. Star schema Model

18. 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.

19. Example of star schema:-

20. Snow Flake schema

21. 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.

22. Example of Snow flake schema:-

23. Fact constellations

24. It is a set of fact tables that shares some dimensional tables. It limits the possible queries for the data warehouse.