Download Slide 1

GEON: GEOsciences Network
Data
Physical model
Portal (login, myGEON)
Registration
GEONsearch
Data
Registration
Integration
Services
Services
Indexing
Services
GEONworkbench
Community Modeling
Environment
Workflow Visualization
& Mapping
Services
Services
Modeling
Environment
Core Grid Services
Authentication, monitoring, scheduling, catalog, data transfer,
Replication, collection management, databases
Physical Grid
RedHat Linux, ROCKS, Internet, I2, OptIPuter (planned)
Model results
HPCC
Agenda
• Scientific Framework: Integration
Scenarios
• IT Advances
• Data and Modeling - Scientific Advances
• Educational Leadership
• Social Aspects of Large Projects
• Summary and Plans
Snapshot of the Day
• GEON research and education activities:
– Highlights given in talks
– Some details provided in posters
– Presentations available at the end of the day
• GEON infrastructure and applications:
mostly prototypes
GEON Principal Investigators
•
•
Ramon Arrowsmith
Chaitan Baru
Arizona State University
San Diego Supercomputer Center /
University of California, San Diego
•
•
•
•
Maria Luisa Crawford
Karl Flessa
Randy Keller
Mian Liu
Bryn Mawr College
University of Arizona
University of Texas at El Paso
University of Missouri, Columbia
•
•
•
Chuck Meertens
John Oldow
Dogan Seber
•
•
Paul Sikora
Krishna Sinha
UNAVCO, Inc.
University of Idaho
San Diego Supercomputer Center /
University of California, San Diego
University of Utah
Virginia Tech
GEON Mission and Goals
“Enabling
scientific discoveries and improving
education in Earth Sciences through
information technology research.”
• Develop cyberinfrastructure for Geoscience research
– Integrate, analyze and model 4-D data
– Research and development in data integration systems, computing
environments, and ontologic frameworks
– Facilitate knowledge discovery for the geosciences
• Promote leadership within geoscience education reform
• Revolutionize how earth scientists do their science
– democratize access to services and data
– allow on-line replication of results
– increase awareness of scientific knowledge “pathways”
• Facilitate a cultural change
Challenges
Many databases and models:


Interpretations limited by existing knowledge
Capture of concepts and relationships needed for
computational tractability
Creation of community knowledge base:
 Required to support knowledge discovery
 Assists in hypothesis generation
The Pathway…Partnership with
Information Technology






Access /share data and products
Access /develop smart tools
Access computational resources
Access/apply knowledge management
Preserve data
Become educational leaders
GEON supports such activities
Access to data representing scales of phenomenon and processes
will be available within the infrastructure for discovering new
knowledge (remember EarthScope)
Surface geology
Deep mantle
Distribution of
faults and
earthquakes in
mid-Atlantic
region
Cratonal
lithosphere
Lithosphere thickness (schematic) based on Zoback
and Mooney (2003), Geologic Map ( USGS), fault
distribution from Sinha (unpubl.)
GEON
TestbedS
cience
Themes
CRUSTAL EVOLUTION:
ANATOMY OF AN
OROGEN
The Appalachian Orogen is a
continental scale mountain belt
that provides a geologic
template to examine the
growth and breakup of
continents through plate
tectonic processes .
First Order Science Question:
What is the geologic history of accretionary
orogens ?
Role of accretionary orogens in the growth of continents
1. Major site of juvenile continental crust production at
convergent plate margins
2. Addition of crust through accretion (terranes)
3. Recycling of continental and oceanic crust
The Appalachian orogen provides a natural laboratory to
develop methods for integration of data, tools and models with
an emphasis on 4-D management of data and knowledge
Appalachian Mountains: Recording 1000 Ma Of
Earth History
Geologic phenomena
• Assembly and dispersal of
super-continents: Rodinia ,
the Grenville record
• Neo-Proterozoic failed rift :
testing multiple hypotheses
• Successful rifting of Rodinia:
rift to drift transition
• Collisional events:
representing an orogenic
cycle
• Successful rifting : present
configuration
Research tasks to represent and
interpret phenomenon
Representing paleo-geography
of plates
Developing process ontology for
hypothesis evaluation
Integration of disciplinary
databases through developing
schemas and object ontology
research
Present day properties
Diversity Of Geologic Information Required
To Analyze Crustal Evolution
GEOLOGIC MAPS
METAMORPHISM
IGNEOUS ACTIVITY
GEODYNAMIC
MODELING
TIME
STRATIGRAPHY/
SEDIMENTOLOGY
PLATE
CONFIGURATION
GEOPHYSICS
STRUCTURE
From schemas to ontologies to integration
Virginia Tech research activities
•
Spatial distribution of igneous rocks: provide access to geologic
maps at multiple scales
•
Capture igneous rock properties data in a digital format
(database schema)
•
Provide web based tools
•
Develop discipline ontologies
•
Implement integration scenarios through ontologies
•
Shared educational opportunities (cs & geo graduate research)
The rock record preserves processes associated with crustal
evolution of continents
Access, analysis and modeling of the igneous rock record is a prerequisite for understanding crustal evolution through time-space
Scales of georeferenced observations contained in
Virginia Tech database: facilitating analysis of orogens
Conceptual Model for Igneous Rock
Properties (static) and Genesis (dynamic)
Design/Information Flow for Analysis of Igneous Rocks
Schema
Development
Components of the Virginia Tech field based schema: deploying data
across multiple scales of observation and analysis
Design/Information Flow for Analysis of Igneous Rocks
Ontology
Development
Igneous Rock Database Schema and Linked Ontology
Prototype web based access and application of tools
Results of query
displayed
geographically and
used in spatial analyses
of terranes
Based on SDSC (KR research group)
Query results displayed in tables and in
classification diagrams
Point-in-polygon routine classifies
sample as Chrysolite. Sample can
now participate in additional
ontologically-driven comparative,
statistical and data mining
analyses.
Based on SDSC (KR research group)
Design and Information Flow for Analysis of Igneous Rocks
Tool
Development
Ontology Based Data
Mining
Ontology Driven Data Mining
GEOROC : UNIQUE DATABASE FOR DATA MINING RESEARCH
Create reusable “Knowledge Base”
Iterate over experiment to refine the knowledge base
Minimize data handling/Maximize research
Allow different levels of knowledge discovery: Hidden, Deep
Adapted from Ramachandran, (2003)
Ontology Driven Data Mining
•
•
•
•
•
Ontology assists in structuring the
data
Data sets associated with
concepts in ontology
User navigates ontology to choose
data sets
Helps to apply data mining at
different levels of abstraction
Spatial and temporal variables are
represented in the data
Plates
Rock
TIme
Composition
Age
Thickness
Density
Velocity
Thermal Prpoerties
Upper Plate
Units
Subducted Plate
angle
Continental Margin
Upper plate : continental
Subducted plate: continental or oceanic
Oceanic ARC
Upper plate : oceanic
Subducted plate: oceanic
Web screen
Problem: Scientific Data Integration...
from Questions to Queries ...
What is the distribution and U/ Pb zircon ages of A-type plutons in VA?
How about their 3-D geometry ?
domain
How does it relate to host rock structures?
knowledge
Knowledge Representation:
ontologies, concept spaces
Database mediation
Data modeling
?
Information
Integration
“Complex
Multiple-Worlds”
Mediation
raw data
Geologic Map
(Virginia)
GeoChemical
(From Ludaescher, SDSC)
GeoPhysical GeoChronologic
(gravity contours) (Concordia)
Foliation Map
(structure DB)