1. Physical model Model results HPCC Data Modeling Environment Core Grid Services Authentication, monitoring, scheduling, catalog, data transfer, Replication, collection management, databases Portal (login, myGEON) Physical Grid RedHat Linux, ROCKS, Internet, I2, OptIPuter (planned) Registration Services Data Integration Services Indexing Services Workflow Services Visualization & Mapping Services RegistrationGEONsearch GEONworkbench Community Modeling Environment GEON: GEOsciences Network

2. Agenda Scientific Framework: Integration Scenarios IT Advances Data and Modeling - Scientific Advances Educational Leadership Social Aspects of Large Projects Summary and Plans

3. 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

4. GEON Principal Investigators Ramon ArrowsmithArizona State University Chaitan Baru San Diego Supercomputer Center / University of California, San Diego Maria Luisa CrawfordBryn Mawr College Karl FlessaUniversity of Arizona Randy KellerUniversity of Texas at El Paso Mian LiuUniversity of Missouri, Columbia Chuck MeertensUNAVCO, Inc. John OldowUniversity of Idaho Dogan Seber San Diego Supercomputer Center / University of California, San Diego Paul SikoraUniversity of Utah Krishna SinhaVirginia Tech

5. 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

6. 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

7. 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

8. Access to data representing scales of phenomenon and processes will be available within the infrastructure for discovering new knowledge (remember EarthScope ) Lithosphere thickness (schematic) based on Zoback and Mooney (2003), Geologic Map ( USGS), fault distribution from Sinha (unpubl.) Distribution of faults and earthquakes in mid-Atlantic region Surface geology Cratonal lithosphere Deep mantle

9. GEON TestbedS cience Themes

10. 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.

11. 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 First Order Science Question: What is the geologic history of accretionary orogens ?

12. 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

13. Diversity Of Geologic Information Required To Analyze Crustal Evolution METAMORPHISM IGNEOUS ACTIVITY GEOLOGIC MAPS STRATIGRAPHY/SEDIMENTOLOGY PLATECONFIGURATION STRUCTURE GEOPHYSICS TIME GEODYNAMIC MODELING

14. 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)

15. The rock record preserves processes associated with crustal evolution of continents

16. Access, analysis and modeling of the igneous rock record is a pre- requisite for understanding crustal evolution through time-space

17. Scales of georeferenced observations contained in Virginia Tech database: facilitating analysis of orogens

18. Conceptual Model for Igneous Rock Properties (static) and Genesis (dynamic)

19. Design/Information Flow for Analysis of Igneous Rocks Schema Development

20. Components of the Virginia Tech field based schema: deploying data across multiple scales of observation and analysis

21. Design/Information Flow for Analysis of Igneous Rocks Ontology Development

22. Igneous Rock Database Schema and Linked Ontology

23. Results of query displayed geographically and used in spatial analyses of terranes Based on SDSC (KR research group) Prototype web based access and application of tools

24. 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)

25. Design and Information Flow for Analysis of Igneous Rocks Tool Development

27. Ontology Based Data Mining

28. 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)

29. 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

30. Web screen

31. 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 ? How does it relate to host rock structures? ? Information Integration Geologic Map (Virginia) GeoChemical GeoPhysical (gravity contours) GeoChronologic (Concordia) Foliation Map (structure DB) “Complex Multiple-Worlds” Mediation domain knowledge Database mediation Data modeling Knowledge Representation: ontologies, concept spaces raw data (From Ludaescher, SDSC)