1 GISolve – TeraGrid GIScience Gateway Shaowen Wang Department of Geography and Grid Research & educatiOn ioWa (GROW) The University of Iowa May 24, 2007

2 Purpose Present how cyberinfrastructure-based Geographic Information Systems (GIS) function Present how cyberinfrastructure-based Geographic Information Systems (GIS) function Illustrate how GISolve is developed to help advance GIScience using cyberinfrastructure Illustrate how GISolve is developed to help advance GIScience using cyberinfrastructure Demonstrate science impact of GISolve Demonstrate science impact of GISolve Background Design Science Implementation E&O Conclusions Purpose

3 Background Geographic information quantity Geographic information quantity –Ever increasing Application driven Application driven –GPS, LBS, RS Computationally intensive geographic analysis Computationally intensive geographic analysis –Heuristic search –Simulation –Optimization –Statistical methods Cyberinfrastructure Cyberinfrastructure –Grid computing and virtual organization Open Grid Service Architecture (OGSA) Open Grid Service Architecture (OGSA) –Problem solving environments Grid portals Grid portals –TeraGrid Science Gateway Background Design Science Implementation E&O Conclusions Purpose

4 GISolve Architecture Computational domain modeling Domain decomposition Task scheduling Information broker and resource discovery Data access module Problem solving environments implemented using Grid portal technologies Monitoring servicesProtocols and services for data access on the Grid, such as the Globus GridFTP Grid Middleware such as Globus and Condor Resource management

5 Spatial Computational Domain Theory Wang, S., and Armstrong, M. P “A Theory of the Spatial Computational Domain.” In: Proceedings of GeoComputation 2005 (CDROM), Ann Arbor, MI, August 1-3, 2005 Wang, S., and Armstrong, M. P “A Theory of the Spatial Computational Domain.” In: Proceedings of GeoComputation 2005 (CDROM), Ann Arbor, MI, August 1-3, 2005 Background Design Science Implementation E&O Conclusions Purpose

6 Information Broker and Resource Discovery Self-Organized Grouping method for Grid resource discovery Self-Organized Grouping method for Grid resource discovery –Padmanabhan, A., Wang, S., Ghosh, S., and Briggs, R “A Self-Organized Grouping (SOG) Method for Efficient Grid Resource Discovery.” In: Proceedings of the Grid 2005 Workshop, Seattle, WA, November 13-14, 2005, IEEE Press, pp Modular Information Provider to support interoperable information brokering Modular Information Provider to support interoperable information brokering –Wang, S., Shook, E., Padmanabhan, A., Briggs, R., Pearlman, L “Developing a Modular Information Provider to Support Interoperable Grid Information Services.” In: Proceedings of Grid and Cooperative Computing - GCC 2006: The Fifth International Conference, IEEE Computer Society, pp Background Design Science Implementation E&O Conclusions Purpose

7 GISolve Workflow

8 A Diagrammatic Example of Task Scheduling 1, 1 0, 02, 4 4, 25, 3 11, 13 14, 1415, 15 3, 5 6, 67, 7 8, 89, 910, 12 12, 1013, 11 1, 1 0, 02, 4 4, 25, 3 11, 13 14, 1415, 15 3, 5 6, 67, 7 8, 89, 910, 12 12, 1013, 11 1, 1 0, 02, 4 4, 25, 3 11, 13 14, 1415, 15 3, 5 6, 67, 7 8, 89, 910, 12 12, 1013, 11 1, 1 0, 02, 4 4, 25, 3 11, 13 14, 1415, 15 3, 5 6, 67, 7 8, 89, 910, 12 12, 1013, 11 Small Capacity Large Capacity Medium Capacity Background Design Science Implementation E&O Conclusions Purpose

9 GISolve Portal Background Design Science Implementation E&O Conclusions Purpose

10 Domain Decomposition Portlet Background Design Science Implementation E&O Conclusions Purpose

11 Scientific Analyses Supported by GISolve Bayesian geostatistical modeling Bayesian geostatistical modeling –Yan, J., Cowles, M. K., Wang, S., and Armstrong, M. P. (2007). Parallelizing MCMC for Bayesian spatiotemporal geostatistical models. Statistics and Computing, in press Detection of local spatial clustering Detection of local spatial clustering –Wang, S., Cowles, M. K., and Armstrong, M. P. (2006) Grid computing of spatial statistics: using the TeraGrid for G*(d) analysis. Concurrency and Computation: Practice and Experience, under revision –Wang, S., Cowles, M. K., and Armstrong, M. P. (2006) Grid computing of spatial statistics: using the TeraGrid for G i *(d) analysis. Concurrency and Computation: Practice and Experience, under revision Inverse distance weighted interpolation Inverse distance weighted interpolation –Wang, S., and Armstrong, M. P. (2003) A quadtree approach to domain decomposition for spatial interpolation in Grid computing environments. Parallel Computing, 29 (10): Background Design Science Implementation E&O Conclusions Purpose

12 Bayesian Geostatistical Modeling Bayesian geostatistical models based on Markov chain Monte Carlo (MCMC) Bayesian geostatistical models based on Markov chain Monte Carlo (MCMC) – –Large-scale spatial-temporal data mining and inference – –Using geostatistical models to characterize the spatial distributions of environmental processes or disease-related outcomes Challenges – –Computationally intensive Background Design Science Implementation E&O Conclusions Purpose

13 Science Impact – A Lung Cancer Risk Study Based on Residential Radon Concentrations Iowa Radon MeasurementsPredicted Radon Concentrations Brian J. Smith From Dr. Brian J. Smith Background Design Science Implementation E&O Conclusions Purpose

14 Science Impact – Social Complexity and the Management of Ecosystems A Study on Yellowstone’s Northern Elk Herd From Dr. David A. Bennett

15 Big Picture – GISolve as an Integrated GIScience Workbench Background Design Science Implementation E&O Conclusions Purpose

16 Education and Outreach Modules Modules –SpatialGen A tool to generate simulated datasets with configurable geographic distributions A tool to generate simulated datasets with configurable geographic distributions –Kriging Spatial prediction Spatial prediction In classrooms In classrooms –The University of Iowa, 2007 Foundations of Geographic Information Systems (undergraduate) Foundations of Geographic Information Systems (undergraduate) Principles of Geographic Information Systems (undergraduate and graduate) Principles of Geographic Information Systems (undergraduate and graduate) Bayesian Statistics (undergraduate and graduate) Bayesian Statistics (undergraduate and graduate) Computing in Statistics (undergraduate and graduate) Computing in Statistics (undergraduate and graduate) –The University of Illinois at Urbana-Champaign, 2007 Geographic information Science (undergraduate and graduate) Geographic information Science (undergraduate and graduate) Upcoming TeraGrid07 student competition Upcoming TeraGrid07 student competition Background Design Science Implementation E&O Conclusions Purpose

17 Conclusions GISolve demonstrates how cyberinfrastructure can benefit research on computationally intensive geographic information analyses GISolve demonstrates how cyberinfrastructure can benefit research on computationally intensive geographic information analyses GISolve integrates OGSA-based Web services to support the computational aspects of GIServices GISolve integrates OGSA-based Web services to support the computational aspects of GIServices Background Design Science Implementation E&O Conclusions Purpose

18 Ongoing Work Extension of the types of geographic information analyses GISolve supports Extension of the types of geographic information analyses GISolve supports –Science impact Interoperability of GISolve services Interoperability of GISolve services Adaptive domain decomposition services Adaptive domain decomposition services Visualization services Visualization services Evaluation of GISolve performance Evaluation of GISolve performance Education and outreach Education and outreach

19 Acknowledgments The University of Iowa The University of Iowa –The Office of Vice President for Research –The Office of Provost The NSF TeraGrid The NSF TeraGrid The NSF ITR: iVDGL project The NSF ITR: iVDGL project The Open Science Grid The Open Science Grid My colleagues My colleagues Dr. Marc P. Armstrong (UIowa, GIScience) Dr. David A. Bennett (UIowa, GIScience) Mary Kathryn Cowles Dr. Mary Kathryn Cowles (UIowa, Statistics) Mr. Thomas Hansen (UIowa, CS) Ms. Wenli He (UIowa, CS) Mr. Peter Likarish (UIowa, CS) Mr. Yan Liu (UIowa, CS & GIScience) Dr. Brian J. Smith (UIowa, Biostatistics) Edward Walker Mr. Eric Shook (UIowa, GIScience) Dr. Edward Walker (UT-Austin/TACC, CS) Dr. Jun Yan (UIowa, Statistics) Mr. Junfeng Zheng (UIowa, CS)