jan cuny u of oregon
DESCRIPTION
This file includes speaker notes that are in the “Notes” module of PPT (go to View--->Notes Page). 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 - PowerPoint PPT PresentationTRANSCRIPT
This file includes speaker notes that are in the “Notes” module of PPT (go to View--->Notes Page)
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.