1. SHIFALI CHOUBEY GISE LAB IITB Decision Support System For Farmers

2. Agenda Factors affecting farming activity Data collected from farmers Motivation Our Approach  Normalization  Report generation  Dimensional Analysis

3. Factors affecting farming activity Location. Type of soil. Time of sowing. Type of Fertilizer/Insecticide used. Frequency of irrigation. Frequency of hoeing.

4. Data collected from farmers

5. Motivation Best Farming Practices  Crop Analysis  Usage pattern of insecticides/fertilizer  Frequency of irrigation  Location wise analysis

6. Our Approach Normalization.  Data cleansing.  Huge database divided into 17 tables.  Entity relationship diagram for normalized tables.

7. Our Approach Generated Reports.  Tool Used: Jasper Soft.  Reports in from of tables, charts and crosstabs.

8. Our Approach Dimensional Analysis  Data Warehouse  Star Schema  Cube Operations

9. What is a Data Warehouse ? A data warehouse is a subject-oriented, integrated, nonvolatile, time-variant collection of data in support of management's decisions. - WH Inmon Data stored for historical period. Data is populated in the data warehouse on daily/weekly basis depending upon the requirement. Can I see how the application of fertilizer on particular crop affected the yield? Data from multiple sources is integrated for a subject Identical queries will give same results at different times. Supports analysis requiring historical data

10. Star Schema A technique for modeling data that is optimized for end-user access that utilizes Fact and Dimension tables Fact table: It consists of the measurements, metrics or facts. Dimension table: Dimensions are particular angle or perspective that you see the facts.

11. Star Schema FARMING DATA Farming ID Date Key (FK) Crop Key (FK) Farmer Key (FK) Location Key (FK) Cost of cultivation Cost of input Yield Net Profit Gross Profit Crop Crop Key (PK) Crop Name Location Location Key (PK) District Village Time Date Key Date Farmer Farmer Key Farmer Name

12. Data Cube Data Cubes allow data to be modeled and viewed from multiple perspectives Perspectives are modeled as dimensions (axes) Each cell in the cube represents some aggregation of the data (avg, sum, etc.)

13. Cube Operations Roll-up (drill-up)  Summarize data by climbing up concept hierarchy or dimension reduction Drill-down (Roll-down)  From summary level to detail level by introducing new dimensions Slice  Selection on one dimension of the cube Dice  Selection on two or more dimensions Pivot  Rotation of the data axes for different visualizations