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A vision on collaborative computation of things for personalized analyses
The document presents a vision for collaborative computation aimed at personalizing analyses through interdisciplinary science and technology. It emphasizes democratizing computation and utilizing big data to model individual behaviors and improve well-being. Specific examples illustrate how small variations in physical attributes can significantly impact performance, advocating for a collaborative technical engine to optimize simulations and predictions in user-centric applications.
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A vision on collaborative computation of things for personalized analyses
- 1. A Vision onCollaborative Computation of Things for Personalized Analyses Dr. Eng. Sc. Justyna Zander SIMULATEDWAY, Harvard University Copyrights reserved. © 2012
- 2. Agenda Science andInterdisciplinarity Democratizing Computation, Modeling and Simulation System Analysis Case Study Collaborative Technical Engine Simulation Engine Semantics
- 3.
- 4. One Scientific Discipline …still Stable and Quiet 4 Dr. Justyna Zander - MBD for CPS
- 5. What if ScienceDisciplines start interacting… 5 … toDr. Justyna Zander -Big Data create MBD for CPS
- 6. Science Disciplines Interact tocreate Emerging Behavior … 6 Dr. Justyna Zander - MBD for CPS
- 7. … and DynamicBehavior MovieSystem of the
- 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
- 9. Computation, Modeling andSimulation to the Rescue 9
- 10. Agenda Science andInterdisciplinarity Democratizing Computation, Modeling and Simulation System Analysis Case Study Collaborative Technical Engine Simulation Engine Semantics
- 11.
- 12.
- 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 PersonalizedVirtual 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 andInterdisciplinarity Democratizing Computation, Modeling and Simulation System Analysis Case Study Collaborative Technical Engine Simulation Engine Semantics
- 16.
- 17. What should Ido to attain in 2 years from now a cyclist performance of Armstrong’s performance from 2004?
- 18.
- 19.
- 26. Simulation findings Thecyclist 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 andInterdisciplinarity Democratizing Computation, Modeling and Simulation System Analysis Case Study Collaborative Technical Engine Simulation Engine Semantics
- 28.
- 29. Agenda https://www.brainshark.com/innocentive/vu?pi=zGtzus4Gsz4IX 8z0&dm=5&tb=0&bg=707070 http://www.youtube.com/watch?v=G8nlFN17D8E&feature=rel ated
- 30. Technical Engine Vision Engine Engine Infrastructure Prediction Query and Architecture Crowd-sourcing M&S Collecting Transformations Analyses Predictions
- 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 ofDifferent 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
- 33. Modeling and Simulation asa Collaboration and Technology Core
- 34.
- 35. Engine Architecture andUsage Process Problems Users Collection Collection Virtual instance on a User’s Device 1 Technology 1 Models Cloud Collection Virtual instance on a Network Technology 2 User’s Device 2 Big Data Technology 3 Collection Virtual instance on a Transfor- Transfor Prediction User’s Device 3 mations Queries Collection Collection
- 36. Engine Architecture andUsage Process
- 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 Ido 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 Ido 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 andInterdisciplinarity Democratizing Computation, Modeling and Simulation System Analysis Case Study Collaborative Technical Engine Simulation Engine Semantics
- 44.
- 45. State of thePast Computational Framework in the Past Execution System Model System Engine System Execution Model Engine Implemen- Implemen- tation tation
- 46. State of theArt Computational Framework Nowadays Simulation Model Solver Model System Model Implemen- Solver Implemen- Specification tation tation
- 47. State of theFuture 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
- 48. A Computational Framework Analysis & Synthesis Analysis, Synthesis, & Execution Computational Framework User MODELING MODELING MODELING SimCI DECLARATIVE DEFINITION SPECIFICATION SimRI OPERATIONAL DEFINITION IMPLEMENTATION ExPM TECHNOLOGY Platform Legend: SimCI – Simulation Control Interface SimRI – Simulation Runtime Interface ExPM– Execution Platform Mapping
- 49. Declarative Specification ofthe Solver
- 50. Non-Monotonous Time Notionin Solver time rejected time step accepted time step step size computational evaluation index
- 51. Agenda Science andInterdisciplinarity Democratizing Computation, Modeling and Simulation System Analysis Case Study Collaborative Technical Engine Simulation Engine Semantics
- 52.