Discovery Systems Program

1. Discovery Systems Program Barney Pell, Ph.D. RIACS / NASA Ames Research Center pell@email.arc.nasa.gov Presentation to IJCAI-2003 Workshop on Information Integration Using the Web

2. Discovery Systems Program Context NASA’s Computing Information and Communications Technology Program NASA Program Funding Philosophy Discovery Systems Project Project Overview Exploratory Environments and Collaboration Distributed Data Search, Access, and Analysis Machine-Assisted Model Discovery and Refinement Demonstrations, Applications, and Infusions Schedule and participation Outline of Talk

3. FY02-FY08 CICTOverall Project Structure Phasing Intelligent Systems Computing, Networking, and Info. Systems Space Communications Information Technology Strategic Research Collaborative Decision Systems Discovery Systems Advanced Networking & Communications Advanced Computing Reliable Software Adaptive Embedded Information Systems FY02 FY03 FY04 FY05 FY06 FY07 FY08

4. CICT Project Definition- Existing Projects - • Intelligent Systems • Smarter, more adaptive systems and tools that work collaboratively with humans in a goal-directed manner to achieve the mission/science goals • Computing, Networking and Information Systems • Seamless access to ground-, air-, and space-based distributed information technology resources • Space Communications • Innovative technology products for space data delivery enabling high data rates, broad coverage, internet-like data access • Information Technology Strategic Research • Fundamental information, biologically-inspired, and nanoscale technologies for infusion into NASA missions

5. CICT Project Definition- Proposed FY05-FY07 New-Start Projects - • Collaborative Decision Systems(FY05) • Information technologies enabling improved decision making for science and exploration missions • Discovery Systems (FY05) • Knowledge management and discovery technologies accelerating the scientific process and engineering analysis • Advanced Networking and Communications (FY05) • Integrated, intelligent, deeply networked ground and in-space system technologies to enable the next generation of NASA Enterprise communication architectures • Advanced Computing (FY05) • Advanced ground and space-based computing technologies to enable NASA’s science and engineering activities • Reliable Software (FY07) • Software development, verification, and validation technologies to maintain and increase the reliability of increasingly complex NASA operational and analysis software systems • Adaptive Embedded Information Systems (FY07) • Embedded information systems capable of adapting to evolving mission science requirements, system health, and environmental factors in support of improved science return with reduced mission risk.

6. Funding Philosophy • Cross-cutting Information Technologies • “As Only NASA Can” • NASA Relevance • Future needs of NASA Enterprises • Would not be filled without funding by NASA • Research Excellence • Competitive Evaluation • Technology Maturity Spectrum • Breakthrough research • Demonstrations of capability • Selective infusions for NASA-relevant efforts • Milestones and Metrics • Failable • “So-what”-able

7. Discovery SystemsProject Overview • Objective • Create and demonstrate new discovery and analysis technologies • Make them easier to use • Extend them to complex problems in massive, distributed, diverse data • Enabling scientists and engineers to solve increasingly complex interdisciplinary problems in future data-rich environments. • Subprojects • Exploratory Environments and Collaboration • Distributed Data Search, Access, and Analysis • Machine-Assisted Model Discovery and Refinement • Demonstrations, Applications, and Infusions

8. Discovery Systems Project- WBS Technology Elements - • Distributed data search, access and analysis • Grid based computing and services • Information retrieval • Databases • Planning, execution, agent architecture, multi-agent systems • Knowledge representation and ontologies • Machine-assisted model discovery and refinement • Information and data fusion • Data mining and Machine learning • Modeling and simulation languages • Exploratory environments and Collaboration • Visualization • Human-computer interaction • Computer-supported collaborative work • Cognitive models of science

9. Discovery Systems Before/After

10. Distributed Search, Access and Analysis • Objective • Develop and demonstrate technologies to enable investigating interdisciplinary science questions by finding, integrating, and composing models and data from distributed archives, pipelines; running simulations, and running instruments. • Support interactive and complex query-formulation with constraints and goals in the queries; and resource-efficient intelligent execution of these tasks in a resource-constrained environment. • Milestone: Enable novel what-if and predictive question answering • Across NASA’s complex and heterogeneous data and simulations • By non data-specialists • Use world-knowledge and meta-data • Support query formulation and resource discovery • Example query: “Within 20%, what will be the water runoff in the creeks of the Comanche National Grassland if we seed the clouds over southern Colorado in July and August next year?”

11. Terrestrial Biogeoscience Involves Many Complex Processes and Data Chemistry CO2, CH4, N2O ozone, aerosols Climate Temperature, Precipitation, Radiation, Humidity, Wind Heat Moisture Momentum CO2 CH4 N2O VOCs Dust Minutes-To-Hours Biogeophysics Biogeochemistry Carbon Assimilation Aero- dynamics Decomposition Water Energy Mineralization Microclimate Canopy Physiology Phenology Hydrology Inter- cepted Water Bud Break Soil Water Days-To-Weeks Snow Leaf Senescence Evaporation Transpiration Snow Melt Infiltration Runoff Gross Primary Production Plant Respiration Microbial Respiration Nutrient Availability Species Composition Ecosystem Structure Nutrient Availability Water Years-To-Centuries Ecosystems Species Composition Ecosystem Structure WatershedsSurface Water Subsurface Water Geomorphology Disturbance Fires Hurricanes Ice Storms Windthrows Vegetation Dynamics Hydrologic Cycle (Courtesy Tim Killeen and Gordon Bonan, NCAR)

12. evaporationmodel runoff model data preparation evaporati evaporati runoff mo runoff mo data preper data preper snow melt metadata snow melt metadata surface watercommunity surface watercommunity snow coverage snow and iceDAAC (NASA) topography Solution Construction via Composing Models modeledphenomenon service interface: required inputs,provided outputs, data descriptions,events climate model binary data streams snow melt metadata Each model typically has acommunity of experts thatdeal with the complexity of themodel and its environment surface watercommunity parameterizedphenomenon rainfall Nat. WeatherService modeledphenomenon modeledphenomenon USGS

13. Virtual Data Grid Example Notify that exists LFN for  Need  PERSrequires   data and LFN Application: Three data types of interest:  is derived from ,  is derived from , which is primary data(interaction and and operations proceed left to right) Need  Have  Request  Need  Proceed? How to generate ( is at LFN) Estimate for generating   is known. Contact Materialized Data Catalogue. Need  Abstract Planner(for materializing data) Concrete Planner(generates workflow) MetadataCatalogue Need  Exact steps to generate  ResolveLFN Materialize with PERS Grid workflow engine PFN  ismaterializedat LFN Need tomaterialize  Virtual Data Catalogue(how to generate  and ) Grid compute resources Materialized Data Catalogue Data Grid replica services Inform that is materialized LFN = logical file name PFN = physical file name PERS = prescription for generating unmaterialized data Store an archival copy, if so requested. Record existence of cached copies. Grid storage resources As illustrated, easy to deadlock w/o QoS and SLAs.

14. Machine assisted model discovery and refinement • Develop and demonstrate methods to • assist discovery of and fit physically descriptive models with quantifiable uncertainty for estimation and prediction • improve the use of observational or experimental data for simulation and assimilation applied to distributed instrument systems (e.g. sensor web) • integrate instrument models with physical domain modeling and with other instruments (fusion) to quantify error, correct for noise, improve estimates and instrument performance. • Eg. Metrics • 50% reduction in scientist time forming models • 10% reduction in uncertainty in parameter estimates or a 10% reduction in effort to achieve current accuracies • 10% reduction in computational costs associated with a forward model • ability to process data on the order of 1000s of dimensions • ability to estimate parameters from tera-scale data.

15. Prediction of the 97/98 El Nino JFM 1998 Predicted Precipitation 1997 1999 A reasonable 15 month prediction of the 97/98 El Nino is achieved when ocean height, temperature and surface wind data are combined to initialize the model.

16. Observing System of the Future • Partners • NASA • DoD • Other Govt • Commercial • International • Advanced Sensors • Information Synthesis • Access to Knowledge • Sensor Web User Community Information

17. Exploratory Environments and Collaboration • Objective • Develop exploratory environments in which interdisciplinary and/or distributed teams visualize and interact with intelligently combined and presented data from such sources as distributed archives, pipelines, simulations, and instruments in networked environments. • Demonstrate that these environments measurably improve scientists’ capability to answer questions, evaluate models, and formulate follow-on questions and predictions.

18. Multi-parameter Explorations

20. Discovery Systems Program Exciting NASA funding program Follow-on to CNIS and IS/IDU ~$250M total over 5 years Information Integration is highly relevant Focus on NASA needs, but these are challenging Program Funding starts FY 2005 Targeting funding external community FY05 So likely a broad call sometime in FY04 We’d like your help Technical workshops in FY04 Advisors wanted for planning teams Submissions to funding calls Reviewers Conclusion