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Dimensional Modelling-Data Warehouse & Data Mining

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Dimensional Modelling-Data Warehouse & Data Mining
Design Requirements
ER Modeling
Problems with ER Model
ER vs Dimensional Modeling
Dimensional Modeling: Salient Features
Dimensional Modeling: Vocabulary
Star Schema

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Dimensional Modelling-Data Warehouse & Data Mining

  1. 1. TRINITY INSTITUTE OF PROFESSIONAL STUDIES Sector – 9, Dwarka Institutional Area, New Delhi-75 Affiliated Institution of G.G.S.IP.U, Delhi BCA Data Warehouse & Data Mining 20302 Dimensional Modelling
  2. 2. TRINITY INSTITUTE OF PROFESSIONAL STUDIES Sector – 9, Dwarka Institutional Area, New Delhi-75 Design Requirements Design of the DW must directly reflect the way the managers look at the business 2 Should capture the measurements of importance along with parameters by which these parameters are viewed It must facilitate data analysis, i.e., answering business questions
  3. 3. TRINITY INSTITUTE OF PROFESSIONAL STUDIES Sector – 9, Dwarka Institutional Area, New Delhi-75 ER Modeling • A logical design technique that seeks to eliminate data redundancy • Illuminates the microscopic relationships among data elements • Perfect for OLTP systems • Responsible for success of transaction processing in Relational Databases 3
  4. 4. TRINITY INSTITUTE OF PROFESSIONAL STUDIES Sector – 9, Dwarka Institutional Area, New Delhi-75 Problems with ER Model ER models are NOT suitable for DW? • End user cannot understand or remember an ER Model • Many DWs have failed because of overly complex ER designs • Not optimized for complex, ad-hoc queries • Data retrieval becomes difficult due to normalization • Browsing becomes difficult 4
  5. 5. TRINITY INSTITUTE OF PROFESSIONAL STUDIES Sector – 9, Dwarka Institutional Area, New Delhi-75 ER vs Dimensional Modeling • ER models are constituted to –Remove redundant data (normalization) –Facilitate retrieval of individual records having certain critical identifiers –Thereby optimizing OLTP performance • Dimensional model supports the reporting and analytical needs of a data warehouse system. 5
  6. 6. TRINITY INSTITUTE OF PROFESSIONAL STUDIES Sector – 9, Dwarka Institutional Area, New Delhi-75 Dimensional Modeling: Salient Features • Represents data in a standard framework • Framework is easily understandable by end users • Contains same information as ER model • Packages data in symmetric format • Resilient to change • Facilitates data retrieval/analysis 6
  7. 7. TRINITY INSTITUTE OF PROFESSIONAL STUDIES Sector – 9, Dwarka Institutional Area, New Delhi-75 Dimensional Modeling: Vocabulary • Measures or facts • Facts are “numeric” & “additive” • For example; Sale Amount, Sale Units • Factors or dimensions • Star Schemas • Snowflake & Starflake Schemas 7 Sales Amt = f (Product, Location, Fact Dimensions
  8. 8. TRINITY INSTITUTE OF PROFESSIONAL STUDIES Sector – 9, Dwarka Institutional Area, New Delhi-75 Star Schema 8 Sales Fact Table Location Dimension Promotion Dimension Product Dimension Time Dimension
  9. 9. TRINITY INSTITUTE OF PROFESSIONAL STUDIES Sector – 9, Dwarka Institutional Area, New Delhi-75 Dimensional Modeling • Facts are stored in FACT Tables • Dimensions are stored in DIMENSION tables • Dimension tables contains textual descriptors of business • Fact and dimension tables form a Star Schema • “BIG” fact table in center surrounded by “SMALL” dimension tables 9
  10. 10. TRINITY INSTITUTE OF PROFESSIONAL STUDIES Sector – 9, Dwarka Institutional Area, New Delhi-75 Fact Tables • Contains numerical measurements of the business • Each measurement is taken at the intersection of all dimensions • Intersection is the composite key • Represents Many-to-many relationships between dimensions • Examples of facts Sale_amt, Units_sold, Cost, Customer_count 10
  11. 11. TRINITY INSTITUTE OF PROFESSIONAL STUDIES Sector – 9, Dwarka Institutional Area, New Delhi-75 Dimension Tables • Contains attributes for dimensions • 50 to 100 attributes common • Best attributes are textual and descriptive • DW is only as good as the dimension attributes • Contains hierarchal information albeit redundantly • Entry points into the fact table 11
  12. 12. TRINITY INSTITUTE OF PROFESSIONAL STUDIES Sector – 9, Dwarka Institutional Area, New Delhi-75 12Star Schema (in RDBMS)
  13. 13. TRINITY INSTITUTE OF PROFESSIONAL STUDIES Sector – 9, Dwarka Institutional Area, New Delhi-75 13 Star Schema Example
  14. 14. TRINITY INSTITUTE OF PROFESSIONAL STUDIES Sector – 9, Dwarka Institutional Area, New Delhi-75 – The time independent, textual and descriptive attributes by which users describe objects. – Combining all the attributes including hierarchies, rollups and sub-references into a single dimension is denormalization. – Often the “by” word in a query or report – Not time dependent Dimensions
  15. 15. TRINITY INSTITUTE OF PROFESSIONAL STUDIES Sector – 9, Dwarka Institutional Area, New Delhi-75 Facts – Business Measurements – Most Facts are Numeric – Additive, Semi-Additive, Non-Additive – Built from the lowest level of detail (grain) – Very Efficient – Time dependent
  16. 16. TRINITY INSTITUTE OF PROFESSIONAL STUDIES Sector – 9, Dwarka Institutional Area, New Delhi-75 THANK YOU

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