VASA: Visual Analytics for Simulation-based Action
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Slides from our IEEE VAST 2014 talk at IEEE VIS on VASA, a visual analytics system for interactive computational steering of pipelines of asynchronous simulation models.
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VASA: Visual Analytics for Simulation-based Action
- 1. VASA Interactive Computational Steering of Large Asynchronous Simulation Pipelines for Societal Infrastructure SUNGAHN KO JIEQIONG ZHAO JING XIA SHEHZAD AFZAL XIAOYU WANG GREG ABRAM NIKLAS ELMQVIST LEN KNE DAVID VAN RIPER KELLY GAITHER SHAUN KENNEDY WILLIAM TOLONE WILLIAM RIBARSKY DAVID S. EBERT
- 2. Society is under threat from many sources…
- 3. Power grid
- 4. Power plants
- 5. Motivating scenario slide: Picture of critical infrastructure Highways
- 7. DISASTER strikes… Simulation slide: Simulation is the answer When HOW CAN WE PREPARE?
- 8. WHEN REAL-WORLD EXERCISES ARE COSTLY AND DANGEROUS simulation I S T H E A N SWE R
- 9. IDEA: pipeline of asynchronous simulations Weather simulation Power grid model Road networks Critical infrastructure
- 10. System-of-systems: multiple heterogeneous systems combined into a unified system greater than its individual parts
- 11. CHALLENGES C1 Monolithic simulation C2 Complex relations C3 Non-standard data C4 Long exec. times C5 Certainty + fidelity
- 12. VASA VISUAL ANALYTICS FOR SIMULATION-BASED ACTION Distributed component-based framework for computational steering of systems-of-systems simulations for societal infrastructure based on a visual analytics approach
- 14. CASUAL EXPERTS: Deep expertise in domain No expertise in simulation + data science RESILIENCE + RESPONSE: Understand and trace events Identify vulnerabilities “What if?” scenarios COMPLEX SYSTEMS: Supply chain logistics Public safety Cybersecurity USERS TASKS DOMAIN
- 15. DESIGN GUIDELINES • Avoids integration of a monolithic design with another • Provides a data exchange format (C1, C3) • Enables parallel execution of distributed models (C4) G1 Simulation services • Provides approximated results for interactive response • Enables real-time response hiding long execution times G2 Simulation proxies • Help to simplify configurations for non-experts • Provides a data exchange format (C1, C3) G3 Visual Relations
- 16. DESIGN GUIDELINES (2) • Partial and interruptible computational steering G4 Computational steering • Uncertainty visualization (C5) • Propagation of errors G5 Visual representations • Main focus of VASA is maps G6 Spatiotemporal focus
- 18. VASA WORKBENCH Interactive desktop tool for a distributed system Visual analytics dashboard w/ multiple coordinated views Configure + steer + explore (simulation models) Control distributed simulations using REST API Simulation proxy provides real-time response
- 20. VASA COMPONENT: WEATHER Historic data (Irene, Sandy, etc) ADCIRC: Advanced Circulation NOAA: National Oceanic and Athmospheric Administration Prepares event datasets from server Visualizes hurricane cone over time using slider Generates inputs to downstream components MODEL PROXY
- 22. VASA COMPONENT: CRITICAL INFRASTRUCTURE Vu environment with submodels E.g. Electric grids, telecom networks, gas distribution Simplified connectivity graph of important structures Example: show impact of hurricanes on restaurants MODEL PROXY
- 24. VASA COMPONENT: ROUTING Maintains road network for critical infrastructure Simulation engine: ArcGIS Server Inputs: barriers and closures Outputs: new transport routes Approximates disabled routes and facilities Visualizes disabled routes and facilities MODEL PROXY
- 26. VASA COMPONENT: SUPPLY CHAIN Supply chains depend on business and goods Our models: poultry firm + fast food = farm to restaurant Discrete event simulation on chain in geolocated facilities Accepts external inputs (weather and roads) Supports road closures, powerless stores and flooding (polygons) Optimizes distribution and even redistributes products Food contamination also modeled and visualized MODEL PROXY
- 27. EXAMPLE: U.S. HURRICANE SEASON Hurricane Irene hits North Carolina on August 27, 2011 34-knot winds batter the coast; critical infrastructure proxy estimates impacted restaurants
- 28. EXAMPLE: U.S. HURRICANE SEASON (2) Complete power grid simulation is run; a shaded polygon shows actual power outage Supply chain simulation run shows that some routes are no longer completing deliveries
- 29. CASE STUDIES + FEEDBACK
- 30. Regional FEMA • Unprecedented work • Visual investigation • Instant approximations • “Whole Community” • Meets missions needs • Enable informed decisions •Suggestion: real-time weather data
- 31. U.S. Coast Guard • Dire need with no current solution •VASA could drastically change their operations • Potential interface for emergency response • Great potential
- 32. CONCLUSION •VASA: Visual Analytics for Simulation-based Action • Systems-of-systems approach •Multiple heterogeneous systems into a unified system • Case studies • Hurricane impact on societal critical infrastructures • Feedback by FEMA and U.S. Coast Guard
- 33. FUTURE WORK Advanced simulation: Energy infrastructures, transportation networks, societal infrastructure Visual representations: Configurations, proxies, intermediate, and final results from simulations
- 34. QUESTIONS Niklas Elmqvist elm@umd.edu David Ebert ebertd@purdue.edu Work supported by the U.S Department of Homeland Security’s VACCINE Center 2009-ST-061-CI0001-06. We thank our analysts and partners for feedback and advice during the project. Iconography created by designers from the Noun Project.
Editor's Notes
- #6: Highways, water (sewer) mains, power grids Vulnerable to both natural and man-made threats Storms, hurricanes, flash floods How to protect these? How to design for resilience and preparedness?
- #9: Disaster can strike
- #10: Increasingly turning to simulation Need a VA system for a large asynchronous simulations
- #11: A large asynchronous simulation pipeline Output of simulation models becomes the input for other simulations arranged in a sequence with feedback
- #22: NOAA: Wind-speed probabilities along the tracks as contours at 34, 50, and 64-knot levels. ADCIRC: surge, flooding, tides, wind-drive circulation (run every 4 hours)
- #24: NOAA: Wind-speed probabilities along the tracks as contours at 34, 50, and 64-knot levels. ADCIRC: surge, flooding, tides, wind-drive circulation (run every 4 hours)
- #28: Data from poultry, distribution, food restaurant firms Transportation routes and schedule, foods quantities E.g., 120K records/week for nearly 500 restaurants (NC) Food-borne illness generation: Synthetic spatiotemporal illness data based on population density distribution