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从信息化基础设施角度展望 下一代地理信息系统

从信息化基础设施角度展望 下一代地理信息系统. 2008.10. 王少文. It was six men of Indostan To learning much inclined, Who went to see the elephant (Though all of them were blind), That each by observation Might satisfy his mind. What is Cyberinfrastructure?. It’s Grid!. It’s Network!. It’s middleware .

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从信息化基础设施角度展望 下一代地理信息系统

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  1. 从信息化基础设施角度展望下一代地理信息系统从信息化基础设施角度展望下一代地理信息系统 2008.10 王少文

  2. It was six men of Indostan To learning much inclined, Who went to see the elephant (Though all of them were blind), That each by observation Might satisfy his mind What is Cyberinfrastructure? It’s Grid! It’s Network! It’s middleware It’s HPC! • And more!: • Applications • Data • E-community • Instruments • Virtual Organization • Etc. It’s Sharing It’s Storage After Charlie Catlett

  3. Cyberinfrastructure Evolution Cyberinfrastructure Terascale PACI NSF Networking Supercomputer Centers | | | | | | 1985 1990 1995 2000 2005 2010 After Deborah L. Crawford

  4. Integration – Holism • "The whole is more than the sum of its parts.“ • By Aristotle in the Metaphysics Borromean rings, after Daniel E. Atkins Image source: http://www.phy.ornl.gov/theory/dean/RIATG/web_pages/structure_one_pager.html

  5. Motivation – What’s Beyond/Next? Google Earth: http://earth.google.com/ Microsoft Virtual Earth: http://maps.live.com/ ESRI ArcGIS: http://www.esri.com/

  6. Challenges • Problems • User interface – not intuitive • Based on window, icon, menu, pointing device • Single user • Desktop-based • Hard to collaborate • Low performance • How much data can we analyze?

  7. Purpose • Illustrate how GISolve – a cyberinfrastructure-based GIS is developed to help advance research and education of GIScience and cyberinfrastructure • Demonstrate science impact of GISolve and the use of GISolve as an education tool Purpose Background Design Implementation Demo Education Conclusions

  8. Background • Geographic information quantity • Ever increasing • Application driven • GPS, location based services, remote sensing • Computationally intensive geographic information analysis • Heuristics and optimization • Simulation • Spatial statistical methods • Cyberinfrastructure (CI) • High-performance computing • Virtual organization • Grid computing, middleware • Data, visualization, and knowledge • Education and workforce development Purpose Background Design Implementation Demo Education Conclusions

  9. Review • CI-based geographic analysis • Wang et al. 2008, Wang and Armstrong 2008, Wang and Zhu, 2008, Wang and Armstrong 2003 • Domain-specific CI activities • GEON (Geosciences Network) • LEAD (Linked Environments for Atmospheric Discovery) • NEON (National Ecological Observatory Network) • WATERS (WATer and Environmental Research Systems) Network • Internet/Web-based GIS • Tsou 2004, Wang et al. 2005, Yang et al. 2005 • Ontology-driven GIS • Fonseca et al. 2002 Purpose Background Design Implementation Demo Education Conclusions

  10. CI Complexity • Cyberinfrastructure • Is evolving • Has many sophisticated components • Has NOT been developed to directly focus on the requirements of domain-specific problem solving Purpose Background Design Implementation Demo Education Conclusions

  11. Managing CI Complexity • Science and engineering gateway • Rooted in CI • Problem solving environments • Rooted in domain science and engineering Purpose Background Design Implementation Demo Education Conclusions

  12. GISolve – Integrating CI Capabilities and GIS Wang and Zhu (2008)

  13. Spatial computational domain Information broker and resource discovery Domain decomposition Data access module Task scheduling Middleware such as the Globus Toolkit and Condor Protocols and services for data access on the Grid, such as the Globus GridFTP Resource management Monitoring services Problem solving environments implemented using Web 2.0 technologies GISolve Middleware

  14. Computational Intensity = Wattage?! For CI-based geographic problem solving, computational intensity metricsare critically important! Purpose Background Design Implementation Demo Education Conclusions

  15. Spatial Computational Domain • Wang, S., and Armstrong, M. P. 2008. “A Theoretical Approach to the Use of Cyberinfrastructure in Geographical Analysis.”International Journal of Geographical Information Science, DOI: 10.1080/1365881080191850 Purpose Background Design Implementation Demo Education Conclusions

  16. Information Broker and Resource Discovery • Self-Organized Grouping method for Grid resource discovery • Padmanabhan, A., Wang, S., Ghosh, S., and Briggs, R. 2005. “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. 312-317 • Modular Information Provider to support interoperable information brokering • Wang, S., Shook, E., Padmanabhan, A., Briggs, R., Pearlman, L. 2006. “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. 448-453 Purpose Background Design Implementation Demo Education Conclusions

  17. 12, 10 12, 10 12, 10 12, 10 13, 11 13, 11 13, 11 13, 11 14, 14 14, 14 14, 14 14, 14 15, 15 15, 15 15, 15 15, 15 8, 8 8, 8 8, 8 8, 8 9, 9 9, 9 9, 9 9, 9 10, 12 10, 12 10, 12 10, 12 11, 13 11, 13 11, 13 11, 13 4, 2 4, 2 4, 2 4, 2 5, 3 5, 3 5, 3 5, 3 6, 6 6, 6 6, 6 6, 6 7, 7 7, 7 7, 7 7, 7 0, 0 0, 0 0, 0 0, 0 1, 1 1, 1 1, 1 1, 1 2, 4 2, 4 2, 4 2, 4 3, 5 3, 5 3, 5 3, 5 Domain Decomposition and Task Scheduling Purpose Background Design Implementation Demo Education Conclusions Medium Capacity Large Capacity Small Capacity

  18. GISolve Workflow Purpose Background Design Implementation Demo Education Conclusions

  19. TeraGrid GIScience Gateway Based on GISolve (www.gisolve.org) Purpose Background Design Implementation Demo Education Conclusions

  20. TeraGrid Image source: www.teragrid.org

  21. Open Science Grid Image source: www.opensciencegrid.org

  22. GISolve Services • Security • Decomposition and task scheduling • Geographic data access • Information broker and resource discovery • Workflow Purpose Background Design Implementation Demo Education Conclusions

  23. Service-oriented approach Purpose Background Design Implementation Demo Education Conclusions

  24. Spatio-Temporal Data Handling and Visualization Purpose Background Design Implementation Demo Education Conclusions Bioenergy data portal

  25. Bayesian Geostatistical Modeling –Markov chain Monte Carlo • Communication topology management • Help split processors into groups • The processors of each group belong to the same computer • Each group runs a single chain • Cross-cluster communication cost is minimal Purpose Background Design Implementation Chain 1 Chain 2 Node 1 Node 2 Node 5 Node 6 Chain 3 Demo Node 9 Node 10 Node 3 Node 4 Node 7 Node 8 Education Node 11 Node 12 Conclusions Supercomputer A Supercomputer B

  26. Analyses Supported by the Gateway • 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, 17 (4): 323-335 • Detection of local spatial clustering • Wang, S., Cowles, M. K., and Armstrong, M. P. (2008)Grid computing of spatial statistics: using the TeraGrid for Gi*(d) analysis.Concurrency and Computation: Practice and Experience, forthcoming • Spatial 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): 1481-1504 • Under development • ABM (Agent-Based Modeling) • Spatial Genetic Algorithms Purpose Background Design Implementation Demo Education Conclusions

  27. Integrated CI-based Workbench for Geospatial Scientists Purpose Background Design Implementation Demo Education Conclusions

  28. Education and Outreach • In classrooms • The University of Iowa, 2006, 2007 • Foundations of Geographic Information Systems (undergraduate) • Principles of Geographic Information Systems (undergraduate and graduate) • Bayesian Statistics (undergraduate and graduate) • Computing in Statistics (undergraduate and graduate) • The University of Illinois at Urbana-Champaign, 2007, 2008 • Advanced Geographic Information Systems (undergraduate and graduate) • Introduction to Geographic Information Systems (undergraduate) • TeraGrid07 student competition • High-school students • Supercomputing 2007 education program • High-school and college teachers Purpose Background Design Implementation Demo Education Conclusions

  29. Conclusions • GISolve principles • Integrated • Collaborative • Distributed • High-performance • Service-oriented • GISolve is effective to teach • CI • GIScience • CI-based GIS Purpose Background Design Implementation Demo Education Conclusions

  30. Application Thinking Computational Driven CIGI – CyberInfrastructure and Geospatial Information Laboratory / Virtual-Organization Energy, Environment, Public Health Applications Geospatial Analysis and Modeling Computational Intensity Multidisciplinary Interactions High-Performance, Distributed and Collaborative GIS GISolve Open Science Grid, TeraGrid Base Cyberinfrastructure

  31. Disciplines Involved in the CIGI VO • Biology • Computer Science • Geography • GIScience • Environmental engineering • History • Hydrology • Statistics

  32. Global Malaria Risk

  33. From James D. Myers

  34. Ongoing R&D • Interoperability of GISolve services • Spatiotemporal computational domain • Adaptive domain decomposition services • Visualization services • Evaluation of GISolve performance • Extension of the types of geographic information analysis • Provenance management

  35. Acknowledgments • CyberInfrastructure and Geospatial Information Laboratory (CIGI) • National Center for Supercomputing Applications (NCSA) • Faculty Fellowship • Department of Energy • Open Science Grid • National Science Foundation • ITR: iVDGL (International Virtual Data Grid Laboratory) • OCI-0503697 • Open Science Grid • TeraGrid SES060004N • TeraGrid SES070004N • Colleagues Dr. Marc P. Armstrong (Geography, UIowa) Dr. David A. Bennett (Geography, UIowa) Mr. Tim Cockerill (NCSA, UIUC) Dr. Mary Kathryn Cowles (Statistics, UIowa) Mr. Yan Liu (CIGI/NCSA, UIUC) Mr. Doru Marcusiu (NCSA, UIUC) Dr. James D. Myers (NCSA, UIUC) Dr. Anand Padmanabhan(CIGI/NCSA, UIUC) Ms. Ruth Pordes (Open Science Grid) Dr. Brian J. Smith (Biostatistics, UIowa) Mr. Eric Shook (Geography, UIUC) Dr. Wenwu Tang (CIGI/NCSA, UIUC) Mr. John W. Towns (NCSA, UIUC) Dr. Edward Walker (TACC, UT-Austin) Ms. Nancy Wilkins-Diehr (SDSC/TeraGrid) Dr. Jun Yan (Statistics, UConn) Dr. Xin-Guang Zhu (Biology, UIUC)

  36. 谢谢! • Comments and/or questions?

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