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Jan Cuny U of Oregon

Doug Toomey U of Oregon

Dawn WrightOregon State

Judy CushingEvergreen State

Developing a Computational Environment for Coupling MOR Data, Maps, and Models:

The Virtual Research Vessel (VRV) Prototype

Best studied fast-spreadingridge segment

Wealth of data, results,models under-utilized

...formats, standards, tools incomplete/incompatible

physical structure of axial magma chambers (seismologists)

hydrothermal activity/convection(geologists & geochemists)

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, simpleanalyses, addyour 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 Composition for “Computational Steering”

Visualize model spaceAdd physics

Adjust constraints

Experimental Data ProcessingOceanData

MatLab

GeodynamicApplication

Parameters SeismicVelocityModel

VizMatLab

SeismicVelocityModel

Parameters

Published result

Tool CompositionBuilding a Computational

Experiment

Tool Composition with Vine

Describing Data for an Experiment

3 - Model Coupling -- “SuperModels”

flow models

seismic anisotrophymodels

image mantle

structure

melt generation

regions

mantle streamlines

startimage mantle

structure

image mantle

structure

image mantle

structure

melt generation

regions

melt generation

regions

mantle streamlines

mantle streamlines

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 Databases

Physical Access to Ridge Data

Le Select

viewwrapper

RidgeGlobal Schema

WebBrowser

Computational Steering &Model Coupling

Seismic Anistrophy Model

MATLAB

JDBC Driver

Le Select

programwrapper

FlowModel

datawrapper

datawrapper

datawrapper

EPR Endeavor Vents

Le SelectCommunication Modules

JDBC

SQL Engine Job Mgr

Data Models and Databases (prelim)

Common Semantics (EPR & Endeavor)?

Adventure 91 Observations

DiveCodeComment

FK2 event_id

Argo 35mm Observations

Line#ClassCodeComment

FK2 event_id

Argo Deposits List

DepositFK2 location_id

Argo Vents List

VentFK2 location_id

Argo Video Fissures

Line#Width (m)

FK2 event_id

Argo Video Observations

Line#CodeCommentVehicle_Depth

FK2 event_id

ASC Coordinates

ab

Biomarker Locations

Marker#FK2 location_id

Biomarker Transect Trackline

FK3 timeFK2 location_id

Adventure Sample Curation

DiveStationTypeBottleTypeTempDescriptionMg

FK1 timeDepthLong. WestLat. NorthXYCuratorDiversOther Inves

Adventure Markers Dawn

Dive DeployedMarkerCommentsVent

FK2 event_id

Adventure Samples

Dive #Sample #Sample Type

FK2 event_id

Dive Track

FK3 timeFK2 location_id

Data - East Pacific Risewith abstractions

ALVIN Dive GIS Summary

dive id #FK1 date

dive #investigatorsboundscoveragesplot files

Moment

PK moment_id

FK1 dateFK2 time

Location

PK location_id

latitudelongitude

Event

PK event_id

FK1 moment_idFK2 location_id

Date

PK date

Time

PK time

This is an Abstraction from the East Pacific

Rise Data, which I can apply to Endeavor Data

and, hopefully, most other geological data.

• Location • TimeStamp• Event • Observation

VRV - ET (Educational Tool)

Expected OutcomesIntegrating 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.

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.