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Data Warehousing
CASE STUDY: AGRI-DATA WAREHOUSE
1
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
Step-6: Data Acquisition & Cleansing
3
Hand filled pest scouting sheet
Typed pest scouting sheet
Graphics
Step-6: Issues
4
Step-6: Why the issues?
5
Step-7: Transform, Transport & Populate
6
Motivation For Transformation
7
Graphics
Step-7: Resolving the issue
8
Graphics
Step-8: Middleware Connectivity
9
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
Step-12: Deployment & System Management
11
Agri-DSS usage: Data Validation
12
Agri-DSS usage: Data Validation Graph
0
2
4
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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
Agri-DSS usage: FAO report
14
Graph 15
Using pesticides to increase yield.
Why negative correlation between yield and pesticides?
Graphics
Agri-DSS usage: Spray Dates
16
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
Agri-DSS usage: Explaining Findings
18
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
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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