Jan Cuny U of Oregon

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

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

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

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

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!

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

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

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

2 - Tool Compositionfor “Computational Steering” Experimental Data Processing Ocean Data MatLab Adjust constraints Seismic Velocity Model Parameters Geodynamic Application Parameters Seismic Velocity Model Published result Viz Visualize model space Add physics MatLab

Tool CompositionBuilding a Computational Experiment

Tool Composition with VineDescribing Data for an Experiment

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

Model CouplingCreating 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 »

Model CouplingLaunch Computational Steering across Platforms

Data Models and DatabasesPhysical Access to Ridge Data MATLAB Computational Steering & Model Coupling Seismic Anistrophy Model Web Browser JDBC Driver Le Select Le Select Le Select view wrapper Communication Modules program wrapper JDBC Ridge Global Schema SQL Engine Job Mgr Flow Model data wrapper data wrapper data wrapper EPR Endeavor Vents

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

VRV - ET (Educational Tool)

Expected OutcomesIntegrating datawith 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.

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.

Jan Cuny U of Oregon