1.
MODELING AVENGERS
OSS TECHNOLOGY MIX FOR SAVING
THE WORLD
, OBEO ( )
, INRIA and Univ. Rennes 1 ( )
Cédric Brun @bruncedric
Benoit Combemale @bcombemale
Slides available at http://cedric.brun.io/talks/ModelingAvengers/

2.
GEMOC
The GEMOC ANR project ( ):
A Language Workbench for concurrent execution and
simulation of heterogeneous models
The GEMOC Initiative ( ):
GEMOC is an open international initiative that aims to
coordinate and disseminate the research results regarding
the support of the coordinated use of various modeling
languages that will lead to the concept of globalization of
modeling languages, that is, the use of multiple modeling
languages to support coordinated development of diverse
aspects of a system.
http://gemoc.org/ins
http://gemoc.org/

3.
French National Institute for Agricultural Research

4.
WATER RESOURCE MANAGEMENT IN
AGRICULTURE

5.
Cultivator has to book for water one year in advance
Administration has to make sure there is twice the
quantity which has been booked for the whole region.
Domain expert (INRA) wants to defines and assess new
cultures activities
MULTIPLE STAKEHOLDERS, MULTIPLE CONCERNS AND
SCALES

6.
DECISIONS
Which field to use for growing what ?
How would the crops grows?
When would I have to add water ?
Machines ? Peoples ?
COMPLEX SYSTEM, OPTIMIZATION WITH MULTIPLE
FACTORS (WEATHER, COSTS, RESOURCES)

7.
WHAT WE*
DO
build domain specific tools for supporting design and
analysis of complex so ware or embedded systems from
multiple viewpoints.
*: modeling community

8.
HOW OUR TECHNOLOGIES AND
TECHNIQUES WOULD FARE IN SUCH
CONTEXT?

9.
Disclaimer: this experiment is not about the science itself
but about how to use the OSS modeling technologies.
This is a toy, but a toy complex enough that we can learn
from it.

10.
PROCESS
1. State your resources (Machines, peoples, fields)
2. Pick some climate model
3. Assign surfaces to cultures
4. Deduce a possible planning
5. See how the biomass would grow
6. See how much irrigation we would need
7. do it again !

11.
Language
Engineers Domain Viewpoint
(Graph Editor)
Grammar
(Textual Editor)
Constraints
and Goals
(Score function)
Behavio
Semant
(animator)
Language
Users Data Views and
static
checking
Text Optimization Executio
simulat

12.
DEMO TIME

13.
EMF

14.
SEVERAL ECORE MODELS

15.
ὄ API
p u b l i c s t a t i c v o i d m a i n ( S t r i n g [ ] a r g s ) {
/ / . . .
E x p l o i t a t i o n e x p l o i t a t i o n = l o a d F r o m F i l e ( a r g s [ 1 ] ) ;
f o r ( W o r k G r o u p g r o u p : e x p l o i t a t i o n . g e t G r o u p s ( ) ) {
S y s t e m . o u t . p r i n t l n ( g r o u p . g e t N a m e ( ) ) ;
f o r ( C u l t u r e c u l t u r e A s s i g n e d T o T h i s G r o u p : g r o u p . g e t C u l t u r e s ( ) ) {
f o r ( E x p l o i t a t i o n A c t i v i t y a c t i v i t y : c u l t u r e A s s i g n e d T o T h i s G r o u p
. g e t A c t i v i t i e s ( ) ) {
S y s t e m . o u t . p r i n t l n ( a c t i v i t y . g e t N a m e ( ) ) ;
S y s t e m . o u t . p r i n t l n ( a c t i v i t y . g e t F r e q u e n c y ( ) ) ;
S y s t e m . o u t . p r i n t l n ( a c t i v i t y . g e t S t a r t D a t e ( ) ) ;
S y s t e m . o u t . p r i n t l n ( a c t i v i t y . g e t E n d D a t e ( ) ) ;
}
/ / . . .

16.
ὄ SEAMLESS DATA REUSE
p u b l i c c l a s s C l i m a t e D a t a R e s o u r c e I m p l e x t e n d s R e s o u r c e I m p l {
p u b l i c C l i m a t e D a t a R e s o u r c e I m p l ( U R I u r i ) {
s u p e r ( u r i ) ;
}
@ O v e r r i d e
p r o t e c t e d v o i d d o L o a d ( I n p u t S t r e a m i n p u t S t r e a m , M a p < ! - - ? , ? - - > o p t i o n s )
t h r o w s I O E x c e p t i o n {
g e t C o n t e n t s ( ) . c l e a r ( ) ;
C l i m a t e D a t a c l i m a t e = S i m u l a t i o n F a c t o r y . e I N S T A N C E . c r e a t e C l i m a t e D a t a ( ) ;
S t r i n g c o n t e n t = C h a r S t r e a m s . t o S t r i n g ( n e w I n p u t S t r e a m R e a d e r ( i n p u t S t r e a m ,
C h a r s e t s . U S _ A S C I I ) ) ;
L i s t < s t r i n g > l i n e s = L i s t s . n e w A r r a y L i s t ( S p l i t t e r . o n ( ' n ' )
. o m i t E m p t y S t r i n g s ( ) . s p l i t ( c o n t e n t ) ) ;

17.
TEXTUAL SYNTAX : XTEXT

18.
ὄ PREDICATES, EXPRESSIONS,
CONDITIONS

19.
GRAPHICAL SYNTAX: SIRIUS

20.
ὄ DYNAMIC EDITING

21.
ὄ TABLE EDITORS

22.
ὄ ANIMATION*
* with Sirius animator

23.
BARELY SCRATCHED THE SURFACE
Eclipse ICE and Science WG
Generating reports using Acceleo
Comparing alternatives using EMF Compare
...

24.
ANALYSIS

25.
PLANNING PROBLEMS
If you need to optimize goals under constraints while having
limited resources.
When will I seed the crop based on the culture constraints, the
climate and the peoples, machine, fields I have ?
NP class of problems, huge number of solutions, many being
useless

26.
ὄ EASY TO MIX WITH EMF
@ P l a n n i n g E n t i t y (
d i f f i c u l t y C o m p a r a t o r C l a s s = A c t i v i t y W o r k D i f f i c u l t y C o m p a r a t o r . c l a s s
)
p u b l i c i n t e r f a c e A c t i v i t y W o r k e x t e n d s E O b j e c t {
/ / . . .
@ P l a n n i n g V a r i a b l e ( v a l u e R a n g e P r o v i d e r R e f s = { " d a y s " } )
D a y g e t S c h e d u l e d O n ( ) ;
/ / . . .
}
@ P l a n n i n g E n t i t y (
d i f f i c u l t y C o m p a r a t o r C l a s s = R e s o u r c e A l l o c a t i o n D i f f i c u l t y C o m p a r a t o r . c l a s s
)
p u b l i c i n t e r f a c e R e s o u r c e A l l o c a t i o n e x t e n d s E O b j e c t {
/ / . . .
@ P l a n n i n g V a r i a b l e ( v a l u e R a n g e P r o v i d e r R e f s = { " r e s o u r c e s " } )
R e s o u r c e g e t R e s o u r c e ( ) ;
/ / . . .
}

27.
ὄ EASY TO MIX WITH EMF
@ P l a n n i n g E n t i t y (
d i f f i c u l t y C o m p a r a t o r C l a s s = A c t i v i t y W o r k D i f f i c u l t y C o m p a r a t o r . c l a s s
)
p u b l i c i n t e r f a c e A c t i v i t y W o r k e x t e n d s E O b j e c t {
/ / . . .
@ P l a n n i n g V a r i a b l e ( v a l u e R a n g e P r o v i d e r R e f s = { " d a y s " } )
D a y g e t S c h e d u l e d O n ( ) ;
/ / . . .
}
@ P l a n n i n g E n t i t y (
d i f f i c u l t y C o m p a r a t o r C l a s s = R e s o u r c e A l l o c a t i o n D i f f i c u l t y C o m p a r a t o r . c l a s s
)
p u b l i c i n t e r f a c e R e s o u r c e A l l o c a t i o n e x t e n d s E O b j e c t {
/ / . . .
@ P l a n n i n g V a r i a b l e ( v a l u e R a n g e P r o v i d e r R e f s = { " r e s o u r c e s " } )
R e s o u r c e g e t R e s o u r c e ( ) ;
/ / . . .
}

28.
ὄ EASY TO MIX WITH EMF
@ P l a n n i n g E n t i t y (
d i f f i c u l t y C o m p a r a t o r C l a s s = A c t i v i t y W o r k D i f f i c u l t y C o m p a r a t o r . c l a s s
)
p u b l i c i n t e r f a c e A c t i v i t y W o r k e x t e n d s E O b j e c t {
/ / . . .
@ P l a n n i n g V a r i a b l e ( v a l u e R a n g e P r o v i d e r R e f s = { " d a y s " } )
D a y g e t S c h e d u l e d O n ( ) ;
/ / . . .
}
@ P l a n n i n g E n t i t y (
d i f f i c u l t y C o m p a r a t o r C l a s s = R e s o u r c e A l l o c a t i o n D i f f i c u l t y C o m p a r a t o r . c l a s s
)
p u b l i c i n t e r f a c e R e s o u r c e A l l o c a t i o n e x t e n d s E O b j e c t {
/ / . . .
@ P l a n n i n g V a r i a b l e ( v a l u e R a n g e P r o v i d e r R e f s = { " r e s o u r c e s " } )
R e s o u r c e g e t R e s o u r c e ( ) ;
/ / . . .
}

29.
SCORE FUNCTION
p u b l i c S c o r e c a l c u l a t e S c o r e ( S i m u l a t i o n S o l u t i o n s o l u t i o n ) {
i n t h a r d S c o r e = 0 ;
i n t s o f t S c o r e = 0 ;
/ / . . .
/ *
* C o n s t r a i n t : a l l t h e r e s o u r c e s w h i c h a r e r e q u i r e d s h o u l d b e a l l o c a t e d .
* /
f o r ( R e s o u r c e A l l o c a t i o n a l l o c : s o l u t i o n . g e t S i m u l a t i o n ( ) . g e t A l l o c a t i o n s ( ) )
i f ( a l l o c . g e t R e s o u r c e ( ) = = n u l l ) {
h a r d S c o r e + = m e d i u m P e n a l t y ( 1 ) ;
a d d F e e d b a c k (
a l l o c . g e t W o r k ( ) ,
( c r e a t e F e e d b a c k ( F e e d b a c k L e v e l . E R R O R , " A r e q u i r e d r e s o u r c e o f k i n d "
+ a l l o c . g e t K i n d ( ) . g e t N a m e ( ) + " i s m i s s i n g . " ) ) ) ;
}
}

30.
WHAT HAVE WE LEARNED SO FAR?

31.
“Do not compromise on your domain model.”

32.
EFFORT TO BUILD THIS TOOLING ?
3 hours meeting + mail exchanges with INRA experts
10 days of Eclipse Modeling experts
Proof of concept code is on github

33.
TECHNOLOGIES MEANT TO DO THIS

34.
ARE RELEVANT TO SAVE THIS

35.
TEAMING UP TECHNOLOGIES
INSTEAD OF STACKING IT
Eclipse: platform and User interface integration
EMF: data, resources, deeplinkink, reflective manipulation

36.
OPPORTUNITIES
Time modeling and management
Probabilistic models
Graph/Charts representation in Sirius
Alternatives comparisons

37.
...
FURTHER MATERIALS
[slides] (Benoit Combemale and Jean-Michel Bruel), CPS Seminar, 2016
[slides] (Benoit Combemale), INRA Seminar, 2015
[paper] (Jean-Michel Bruel, Benoit Combemale, Ileana
Ober, Hélène Raynal), In International Conference on Computational Science (ICCS), 2015.
[video, french]
(Benoit Combemale, DEVLOG-IDM2014, Oct. 2014)
[video, french]
(Benoit Combemale, DEVLOG-IDM2013, Oct. 2013)
Modeling for Smart CPS
Modeling for Sustainability
MDE in Practice for Computational Science
L'IDM par la pratique dans le contexte des modèles agronomiques autour d'une
étude de cas
Composition and concurrent execution of heterogeneous domain-specific models