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DWH-Ahsan Abdullah DWH-Ahsan Abdullah 1 Data Warehousing Data Warehousing Lecture-38 Lecture-38 Case Study: Agri-Data Warehouse Case Study: Agri-Data Warehouse Virtual University of Pakistan Virtual University of Pakistan Ahsan Abdullah Assoc. Prof. & Head Center for Agro-Informatics Research www.nu.edu.pk/cairindex.asp FAST National University of Computers & Emerging Sciences, Islamabad FAST National University of Computers & Emerging Sciences, Islamabad

Lecture 38

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DWH-Ahsan AbdullahDWH-Ahsan Abdullah

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Data Warehousing Data Warehousing Lecture-38Lecture-38

Case Study: Agri-Data WarehouseCase Study: Agri-Data Warehouse

Virtual University of PakistanVirtual University of Pakistan

Ahsan AbdullahAssoc. Prof. & Head

Center for Agro-Informatics Researchwww.nu.edu.pk/cairindex.asp

FAST National University of Computers & Emerging Sciences, IslamabadFAST National University of Computers & Emerging Sciences, Islamabad

DWH-Ahsan Abdullah

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Step-5: Surprise case Step-5: Surprise case

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Ball Worm ComplexSucking pests

SBW: Spotted Ball WormABW: Army Ball WormPBW: 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.

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Step-6: Data Acquisition & Cleansing Step-6: Data Acquisition & Cleansing

Hand filled pest scouting sheet

Typed pest scouting sheet

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Step-6: Issues Step-6: Issues

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Step-6: Why the issues?Step-6: Why the issues?

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Step-7: Transform, Transport & PopulateStep-7: Transform, Transport & Populate

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Motivation For TransformationMotivation For Transformation

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Step-7: Resolving the issueStep-7: Resolving the issue

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Step-8: Middleware ConnectivityStep-8: Middleware Connectivity

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Step-9-11: Prototyping, Querying & ReportingStep-9-11: Prototyping, Querying & Reporting SELECT Date_of_Visit, AVG(Predators), SELECT Date_of_Visit, AVG(Predators),

…………………………AVG(Dose1+Dose2+Dose3+Dose4)AVG(Dose1+Dose2+Dose3+Dose4)

FROM Scouting_DataFROM Scouting_DataWHERE Date_of_Visit < #12/31/2001# WHERE Date_of_Visit < #12/31/2001#

and predators > 0and predators > 0GROUP BY Date_of_Visit;GROUP BY Date_of_Visit;

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Step-12: Deployment & SystStep-12: Deployment & System Managementem Management

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Agri-DSS usage: Data ValidationAgri-DSS usage: Data Validation

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Agri-DSS usage: Data Validation GraphAgri-DSS usage: Data Validation Graph

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Predator Spray

ALL goes to graphicsALL goes to graphics

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Agri-DSS usage: FAO reportAgri-DSS usage: FAO report

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GraphGraph

Using pesticides to increase yield.

Why negative correlation between yield and pesticides?

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Agri-DSS usage: Spray DatesAgri-DSS usage: Spray Dates

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Agri-DSS usage: Spray Dates Graph Agri-DSS usage: Spray Dates Graph

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Spray dates (mm_dd) for 2001 & 2002

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Agri-DSS usage: Explaining FindingsAgri-DSS usage: Explaining Findings

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Agri-DSS usage: Sowing DatesAgri-DSS usage: Sowing Dates

2001: Sowing week_day

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Conclusions & LessonsConclusions & Lessons ETL is a big issue.ETL is a big issue.

Each farmer is repeatedly visitedEach farmer is repeatedly visited

There is a skewness in the scouting data.There is a skewness in the scouting data.

Decision-making goes all the way “down” to Decision-making goes all the way “down” to the extension levelthe extension level. .

All goes to graphicsAll goes to graphics