1. This file includes speaker notes that are in the Notes module of PPT (go to View--->Notes Page)

2. Jan Cuny U of Oregon Doug Toomey U of Oregon Dawn Wright Oregon State Judy Cushing Evergreen State Developing a Computational Environment for Coupling MOR Data, Maps, and Models: The Virtual Research Vessel (VRV) Prototype

3. Best studied fast-spreading ridge segment Wealth of data, results, models under-utilized...formats, standards, tools incomplete/incompatible

4. physical structure of axial magma chambers (seismologists) hydrothermal activity/convection (geologists & geochemists)

5. Vision for VRV: A Computational Infrastructure MORE than just archiving…. data sharing, tool composition, and model coupling –physical observations (traditional data) –text attributes, video and graphics –programs, models, tools, and scripts for computational processing New data and metadata, format conversion Web interface for distributed computing

6. Good Fit to NSF ITR Computer science clearly needed – Improvements to current technologies Interdisciplinary, multi-institutional team, history of collaboration EPR yes, but other sites (e.g., Galapagos) and types of environmental data as well Human resource development (undergrads, VRV-ET, Saturday Academy) research plan "compelling" but obviously too ambitious!

7. Three Components (Solutions) 1 - Data Sharing GIS, RDBMS, computational experiment management system (ViNE) are all needed Non-spatial data and text metadata Computational experimentation More than physical access to files –More than flat files and simple tables

8. ArcIMS Zoom in Query, simple analyses, add your own data

9. So far.... Dawn –Our vision & NSF s ITR –The data sharing problem –GIS data visualization Judy –Tool Composition & Model Coupling –Educational outreach –Expected outcomes

10. 2 - Tool Composition for Computational Steering Visualize model space Add physics Adjust constraints Experimental Data Processing Ocean Data MatLab Geodynamic Application Parameters Seismic Velocity Model Viz MatLab Seismic Velocity Model Parameters Published result

11. Tool Composition Building a Computational Experiment

12. Tool Composition with Vine Describing Data for an Experiment

13. 3 - Model Coupling -- SuperModels flow models seismic anisotrophy models image mantle structure melt generation regions mantle streamlines start image mantle structure image mantle structure image mantle structure melt generation regions melt generation regions mantle streamlines mantle streamlines

14. Model Coupling Creating a Super Model Steer a single model (Vine), Launch that steering (Vine) across platforms, Transfer data seemlessly across platforms Describe the models « declaratively » –input, parameters, process, output Describe « Process Interactions »

15. Model Coupling Launch Computational Steering across Platforms

16. Data Models and Databases Physical Access to Ridge Data Le Select view wrapper Ridge Global Schema Web Browser Computational Steering & Model Coupling Seismic Anistrophy Model MATLAB JDBC Driver Le Select program wrapper Flow Model data wrapper data wrapper data wrapper EPREndeavorVents Le Select Communication Modules JDBC SQL EngineJob Mgr

17. Data Models and Databases (prelim) Common Semantics (EPR & Endeavor)? Location TimeStamp Event Observation

18. VRV - ET (Educational Tool)

19. Expected Outcomes Integrating data with metadata, tools and models - A (possibly virtual) database - Tools to visualize data (GIS and MatLab) - Tools for Steering & Coupling - Publish models - Compose tools - Support migration paths for model coupling Apply all to VRV for EPR Educational Outreach -- VRV ET – UOregon, Portland Sat. Academy, Evergreen, etc.

20. Methods for Model Coupling Express model couplings so they can be implemented as coupling between simulations. Use simulation code analysis and theoretical tools such as Petri Nets to express these couplings. Describe models so that the coupling can be automated and model descriptions can be reused.