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Multidimentional data model


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Multidimentional data model

  1. 1. PRESENTATION ON MULTIDIMENSIONAL DATA MODEL1 Jagdish Suthar B. Tech. Final Year Computer Science and Engineering Jodhpur National university, Jodhpur
  2. 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. 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. 4. COMPONENT OF MDDM The two primary component of dimensional model are Dimensions and Facts. Dimensions:- Texture Attributes to analysesdata. Facts:- Numeric volume to analyze business. 4
  5. 5. TYPES OF MDDM [A]. Data Cube Model. [B]. Star Schema Model. [C]. Snow Flake Schema Model. [D]. Fact Constellations. 5
  6. 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. 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. 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. 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. 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. 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_soldBranch_Key StreetBranch_name Average_sales CityBranch type State 11 Fig.:-Star Schema model Country Measure
  12. 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. 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. 14. CONT.….  Example of Snow Flake Schema:- Time Sales Fact ItemTime_key Table Item_keyDay Time_key Item_name SupplierDay of Item_key Brand Supplier_keyWeek Types Supplier_typeMonth Branch_KeyQuarter Suppiler_types Location_keyYear Unit_sold Location Branch Location_key Dollar_soldBranch_Key Street CityBranch_name Average_sales City _key City_keyBranch type City State 14 Fig.:-Snow Flake Schema model Measures Country
  15. 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 FlakeStar Schema dimension are Snow flake SchemaDe normalized with each dimension are normalizeddimension being into multiple related 15represented in single table. tables.
  16. 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 2Product Product No. ProductQuarter Product Name Future QuarterRegion Product Design RegionRevenue Product Style ProjectedBusiness Result Product Line Revenue Product Business 16 Fig.:-Fact Constellations Forecast
  17. 17. REFERENCES:- Data Mining & Warehousing-Saumya Bajpai. (Ashirwad Publication ,Jaipur) 36-olap.ppt  Data Warehouse Models and OLAP Operations, by Enrico Franconi 17
  18. 18. THE END 18