Presentation delivered at the 3rd IEEE Track on
Collaborative Modeling & Simulation - CoMetS'12.
Please see http://www.sel.uniroma2.it/comets12/ for further details.
2. Agenda
Science and Interdisciplinarity
Democratizing Computation, Modeling and Simulation
System Analysis Case Study
Collaborative Technical Engine
Simulation Engine Semantics
8. Collaborative Analysis
Lifestyle
Geolocation Interests
Travels Social
Pollution Network
Nutrition
Body
Habits
Building
Information for the
Benefit of an
Individual
Health Emotional
Record Intelligence
Brain Education
Capacity Knowledge
Genetics Wisdom
DNA
The Personalized Mirror
of Human Life
10. Agenda
Science and Interdisciplinarity
Democratizing Computation, Modeling and Simulation
System Analysis Case Study
Collaborative Technical Engine
Simulation Engine Semantics
13. Computation of Things
Mission: increase sustainable wellbeing and happiness
Vision: increase personal awareness in any possible aspect
of life based on:
14. A Future Personalized Virtual Advisor
? Participatory Sensing
Assessment
criteria,
CoTh
Intelligent Modeling
Geolocation, Physical Systems,
and Simulation
Infrastructure, etc
Patterns, for Sustainability
User
ANALYSIS Interface
! SYNTHESIS: Forecast and Prediction
15. Agenda
Science and Interdisciplinarity
Democratizing Computation, Modeling and Simulation
System Analysis Case Study
Collaborative Technical Engine
Simulation Engine Semantics
26. Simulation findings
The cyclist who finished second in 2004 was reported to
be 5 cm taller than Lance Armstrong.
If the body height of the virtual cyclist is increased
from 179 cm to 184 cm, the model simulation
predicts that the time needed for the time trial becomes
about 3 s longer.
This illustrates that small differences in body size can
have significant impact on athletic performance.
27. Agenda
Science and Interdisciplinarity
Democratizing Computation, Modeling and Simulation
System Analysis Case Study
Collaborative Technical Engine
Simulation Engine Semantics
31. Simulation Analyst Expert
Domain Expert
Process Analyst Expert Mass-Scale User
Business Analyst Expert Citizen Analyst
Simulation Tool Expert
Infrastructure Expert
32. A Merge of Different Approaches
Modeling and Simulation Participatory Sensing
Ubiquitous Computing Computational Thinking
Internet of Things Computation Engineering
Wisdom of Crowds Reciprocatory Sensing (AI)
Human in the Loop
Engineering Sustainable Development and Human Awareness
37. Technology Management System
Platform Design
Views
Technology Management Process
IT Resources
Cloud-based Tools
Technical Execution
Guidelines for Users
38. Technology Management System Collaborative Knowledge Management System
User’s Prediction Query
Platform Design
Views
Technology Management Process
Collaborative Platform Objectives
IT Resources
Simulation
Problem Solution
Cloud-based Tools Expertise
Management Process
M&S Project
Definition Cross-sections
Technical Execution
Big Data Existing Big Data
Models Available Models
Guidelines for Users People People
Transformations
Guidelines for Users
Predictions
39. Technology Management System Collaborative Knowledge Management System
User’s Prediction Query
Platform Design
Views
Technology Management Process
Collaborative Platform Objectives
IT Resources
Simulation
Problem Solution
Cloud-based Tools Expertise
Management Process
M&S Project
Definition Cross-sections
Technical Execution
Big Data Existing Big Data
Models Available Models
Guidelines for Users People People
Transformations
Guidelines for Users
Predictions
40. Technology Management System Collaborative Knowledge Management System
User’s Prediction Query
Platform Design
Views
Technology Management Process
Collaborative Platform Objectives
IT Resources
What should I do to attain Simulation
Problem Solution
Cloud-based Tools Expertise
Management Process
in 2 years from now
M&S Project
Definition Cross-sections
Technical Execution a cyclist performance
of Armstrong’s performance from 2004? Data
Big Data Existing Big
Models Available Models
Guidelines for Users People People
Transformations
Guidelines for Users
Predictions
41. What should I do to attain
in 2 years from now
User’scyclist performance
a
Prediction Query
Platform Design of Armstrong’s performance from 2004?
Views
Technology Management Process
Collaborative Platform Objectives
IT Resources
Simulation
Problem
Cyclist performance Solution
Cloud-based Tools Expertise
Management Process
J. Ullrich
M&S Project
Definition Cross-sections
Technical Execution Myself – statistics
Statistics in the
Big
L. Armstrong Data Existing Big Data
Web
Brain capacity
Models
Body and muscle of the
Biomechanics,
Biochemistry
Performance
Group dynamics Available the Web
Models in Models
Disease track Geography
race
Simulation
Guidelines for Users People
community People
Transformations
Guidelines for Users
Predictions
42. What should I do to attain
in 2 years from now
User’scyclist performance
a
Prediction Query
Platform Design of Armstrong’s performance from 2004?
Views
Technology Management Process
Collaborative Platform Objectives
IT Resources
Simulation
Problem
Cyclist performance Solution
Cloud-based Tools Expertise
Management Process
M&S Project
Definition Cross-sections
Technical Execution
L.Big–Data Statistics in the
Armstrong
MyselfUllrich
J. statistics Existing Big Data
Web
Geography of the
Models
Body and muscle
Biomechanics,
Biochemistry
Performance
Group dynamics
Brain capacity
Disease track
race Available the Web
Models in Models
Simulation
Guidelines for Users People
community People
Transformations
Guidelines for Users
Predictions
43. Agenda
Science and Interdisciplinarity
Democratizing Computation, Modeling and Simulation
System Analysis Case Study
Collaborative Technical Engine
Simulation Engine Semantics
45. State of the Past
Computational Framework in the Past
Execution
System Model System
Engine
System Execution
Model Engine
Implemen- Implemen-
tation tation
46. State of the Art
Computational Framework Nowadays
Simulation
Model Solver
Model
System Model
Implemen- Solver
Implemen-
Specification
tation tation
47. State of the Future
Future
Computational Framework
Solver
Model
Model
Specification
Specification
Verification and Validation
Verification and Validation
Simulation Runtime Interface
Model Solver
Model
Implemen- Solver
Implemen-
Model
Implemen- Solver
Implemen-
tation tation
Implemen-
tation Implemen-
tation
tation tation
Simulation
50. Non-Monotonous Time Notion in Solver
time
rejected time step
accepted time step
step size
computational evaluation index
51. Agenda
Science and Interdisciplinarity
Democratizing Computation, Modeling and Simulation
System Analysis Case Study
Collaborative Technical Engine
Simulation Engine Semantics