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10._DWH_Case_Study_II.ppt

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10._DWH_Case_Study_I.ppt
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10._DWH_Case_Study_II.ppt

  1. 1. Data Warehousing CASE STUDY: AGRI-DATA WAREHOUSE 1
  2. 2. Step-5: Surprise case -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 0.3 Jassid WhiteFly_Nymph WhiteFly_Adult Thrip Mite SBW ABW_White_Eggs ABW_Brown_Eggs ABW_Larvae_Small ABW_Larvae_Large PBW_RF PBW_Bolls Correlation 2 Ball Worm Complex Sucking pests SBW: Spotted Ball Worm ABW: Army Ball Worm PBW: Pink Ball Worm If pest population is low, predator population will also be low, because there will be less “food” for predators to live on i.e. pests. Graphics
  3. 3. Step-6: Data Acquisition & Cleansing 3 Hand filled pest scouting sheet Typed pest scouting sheet Graphics
  4. 4. Step-6: Issues 4
  5. 5. Step-6: Why the issues? 5
  6. 6. Step-7: Transform, Transport & Populate 6
  7. 7. Motivation For Transformation 7 Graphics
  8. 8. Step-7: Resolving the issue 8 Graphics
  9. 9. Step-8: Middleware Connectivity 9
  10. 10. Step-9-11: Prototyping, Querying & Reporting SELECT Date_of_Visit, AVG(Predators), …………………………AVG(Dose1+Dose2+ Dose3+Dose4) FROM Scouting_Data WHERE Date_of_Visit < #12/31/2001# and predators > 0 GROUP BY Date_of_Visit; 10 Graphics
  11. 11. Step-12: Deployment & System Management 11
  12. 12. Agri-DSS usage: Data Validation 12
  13. 13. Agri-DSS usage: Data Validation Graph 0 2 4 6 8 10 07/06/01 07/09/01 07/12/01 07/15/01 07/18/01 07/21/01 07/24/01 07/27/01 07/30/01 08/02/01 08/05/01 08/08/01 08/11/01 08/14/01 08/17/01 08/20/01 08/23/01 08/26/01 08/29/01 09/01/01 09/04/01 09/07/01 Predator Spray 13 ALL goes to graphics
  14. 14. Agri-DSS usage: FAO report 14
  15. 15. Graph 15 Using pesticides to increase yield. Why negative correlation between yield and pesticides? Graphics
  16. 16. Agri-DSS usage: Spray Dates 16
  17. 17. Agri-DSS usage: Spray Dates Graph -0.50 -0.30 -0.10 0.10 0.30 0.50 0.70 0.90 7_23 7_28 8_2 8_7 8_12 8_17 8_22 8_27 9_1 9_6 9_11 9_16 9_21 9_26 10_1 Spray dates (mm_dd) for 2001 & 2002 Relative values Moving Avg Correlation 17
  18. 18. Agri-DSS usage: Explaining Findings 18
  19. 19. Agri-DSS usage: Sowing Dates 2001: Sowing week_day 303 405 179 174 431 429 398 0 100 200 300 400 500 Mon Tue Wed Thu Fri Sat Sun 2002: Sowing week_day 367 278 409 124 223 387 357 0 100 200 300 400 500 Mon Tue Wed Thu Fri Sat Sun 19 Graphics
  20. 20. Conclusions & Lessons  ETL is a big issue.  Each farmer is repeatedly visited  There is a skewness in the scouting data.  Decision-making goes all the way “down” to the extension level. 20 All goes to graphics

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