1. Blue Economy
Global Performance Model design and implementation - Geoanalytics integration
Denis Pyriohos (i2s)
BlueBRIDGE 5th TCom
12-14 June 2017
Crete, Greece

2. Outline Models – Plain and Global Similar sites Global model cases
KPIs calculation
API

3. Model purpose Base to analysis (a.k.a. simulation) Biological analysis
Economical analysis
Forecasting

4. Model ingredients Species Temperature (via the associated site)
Daily needed
Simplified (e.g. fortnight) can be used
KPI tables (produced from sampling data)
FCR
SGR
SFR
Mortality rate

5. Model usage By a simulator. We should Provide the model
Declare startup set
Initial weight
Initial amount of fishes
Start date
Define the target
Target date OR
Target weight

6. Model users What-if analysis (simul-fish-growth portlet)
Project based on my experience
Compare to the competition (i.e. similar to me)
Technoeconomics
Forecast based on my experience
Geoanalytics
Compare based on many similar sources

7. Plain model Ingredients provided by the user
Directly (i.e. data entry)
Temperatures
Latitude/longitude
Via calculation (e.g. KPI tables from sample data spreadsheets using DataMiner)
Based on my data (i.e. my experience)

8. Global model - what Ingredients provided by other models
Based on similar data

9. Global model - similarity
Heavily based on similar sites
Calculate KPIs based on similar sites

10. Basis: site similarity
Sites that adhere to the privacy policy
Sites with similar environmental conditions
Temperature
Oxygen level
Similarity regarding temperature
Differences in average temperatures between the sites in the same month don't exceed ±1℃
Similar annual median thermal profiles (±1℃)
Similarity regarding oxygen:
Problem: companies do not have site data (there is a solution!)
calculate similarity: needs further consideration

11. Similar site: characteristics
One site that represents a particular similarity
Location ignorant
Each temperature slot is the average of respective distinct temps
Each similar site is an actual site in implementation
I.e. it has its own entry in the Site database table
Owner is the predefined, reserved “global” company
Not true for Geoanalytics :)
Similarity is symmetric and not transitive
“Forkys Rhodes” similar to “Psaria Crete” and “Aqua Surin”
“Forkys Rhodes” similar to “Sardin Madag”
But “Sardin Madag” not similar to “Psaria Crete” or “Aqua Surin”
The Similar site consists of all four

12. Global Model cases Two cases in order to collect the model ingredients
A user model is available
“I want to compare my performance to the competition”
Use: What-if analysis (fish growth portlet)
No model, just a geolocation available
“I want to evaluate an unknown area”
Use: Geoanalytics service

13. User model available The model has SiteXxx related to it
Site: the Similar site to SiteXxx
Global Model species is the model’ s species
Calculate KPIs
These will be stored in the database

14. Geolocation available
No model – no site
We have a geolocation (latitude/longitude)
Seek for environmental data (temperature, oxygen) based on geolocation
Using service
Make a fake (transient) site using these data
Get actual similar sites => produce a Similar site (transient) to be the model’s Site
Global Model species is declared by the API user
Calculate KPIs
These will be transient

15. KPIs calculation Initial approach Not fair
Model Samplings: Each model is associated to an amount of samplings
Site Samplings: Each site has a number of models associated to it (relationship: one site to zero or more models). Therefore each site is implicitly associated to the union of all related Model Samplings
Global Samplings: The “Similar site” is associated to many sites. Therefore it is implicitly associated to the union of all related Site Samplings.
Call the R algorithm that produce KPIs from sample data and pass it the Global Samplings
Not fair
Models with many sampling entries shadow the others

16. KPIs calculation schema
Model 111
sample 111
KPIs 111
Site 11
Model 112
sample 112
KPIs 112
Site 21
Model 113
sample 113
KPIs 113
Model 211
sample 211
Site AA
KPIs 211
Model x
Site 31
Model 311
sample 311
KPIs 311
sample 111
sample 112
sample 113
sample 211
sample 311
sample 312
Site AB
Model 312
sample 312
KPIs 312
Model y
_11_21_31_
Global KPIs

17. KPIs calculation Each model contributes the calculated KPI value
The KPI value is the average of the contributing KPI tables. Easy? Well, not so much :)
Adjust weight ranges (Weight Limits table)
For each slot in the weight ranges, calculate the KPI as the average of the contributing KPI values
Yes, there is an example!

18. KPI calculation hands on
gr\oC 13 14 15 16 2 1 11 21 31 5 12 22 32 10 3 23 33
99999 4 24 34
gr\oC 13 14 15 16 3 6 26 23 5 7 17 27 37 8 18 28 38
99999 9 19 29 39
gr\oC 13 14 15 16 2 3 5 8 10
99999
gr\oC 13 14 15 16 2
(1+6) /2 3
(2+6) /2 5
(2+7) /2 8
(3+8) /2 10
(3+9) /2
99999
(4+9) /2

19. KPIs calculation schema
Model 111
sample 111
KPIs 111
Site 11
Model 112
sample 112
KPIs 112
Site 21
Model 113
sample 113
KPIs 113
Model 211
sample 211
Site AA
KPIs 211
Model x
Site 31
Model 311
sample 311
KPIs 311
Site AB
Model 312
sample 312
KPIs 312
Model y
Global KPIs
_11_21_31_

20. API for geolocation Java library Used by Geoanalytics
Called repeatedly, run simultaneously