![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.
2](https://image.slidesharecdn.com/multidimentionaldatamodel-121012060705-phpapp02/85/Multidimentional-data-model-2-320.jpg)
![TYPES OF MDDM
[A]. Data Cube Model.
[B]. Star Schema Model.
[C]. Snow Flake Schema Model.
[D]. Fact Constellations.
5](https://image.slidesharecdn.com/multidimentionaldatamodel-121012060705-phpapp02/85/Multidimentional-data-model-5-320.jpg)
Multidimentional data model
- 1. PRESENTATION ON MULTIDIMENSIONAL DATA MODEL 1 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. 2
- 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. 3
- 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. 4
- 5. TYPES OF MDDM [A]. Data Cube Model. [B]. Star Schema Model. [C]. Snow Flake Schema Model. [D]. Fact Constellations. 5
- 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. 6
- 7. CONT.……. Changing from one dimensional hierarchy to another is early accomplished in data cube by a technique called piroting (also known rotation). 7
- 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. 8
- 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. 9
- 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. 10
- 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 11 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. 12
- 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. 13
- 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 17
- 18. THE END 18