Teaching Geoinformatics: Computer Science Perspective

1. Teaching Geoinformatics: Computer Science Perspective Ann Quiroz Gates Professor and Chair Department of Computer Science The University of Texas at El Paso

2. Organization • Computer Science Perspective • Geoscience Perspective • Open Discussion

3. Overview • Approaches • Software Engineering: A Project-Driven Approach • Example Projects • Challenges

4. Session Goals • Acquire ideas on how to integrate geoinformatics into Geoscience and Computing curricula • Learn about an approach to develop cross-disciplinary software projects • Have fun!

5. Question • What approaches can be used to introduce geoinformatics into the classroom?

6. Suggested Approaches-1 • Integrate geoinformatics components into courses • TeraGrid Education, Outreach, and Training • Discover Data: Incorporate scientific data into curriculum • Data Bridge: Organize data in common file formats and visualize http://www.teragrid.org/eot/resources_edu.html • Digital Library for Earth Systems Education • Earth Exploration Toolkit • Discover our Earth http://www.dlese.org/educators/usingdata.html • NCSA: Cybereducation http://education.ncsa.uiuc.edu/

7. Suggested Approaches-2 • Integrate tutorials into course, e.g., Kepler • Develop exercises around GEON portal and others • Collaborate with faculty from Computer Science, Information Technology/Systems, or Software Engineering programs • Offer courses that are cross listed across departments • Include guest speakers in course • Become involved in project-driven courses

8. Questions How do you develop complex software systems?

9. Waterfall Model

10. Incremental Model

11. Where does the client fit in?

12. UTEP’s Software Engineering Course A Project-Driven Approach

13. Course Overview-1 • Teach and apply software engineering methods, tools, and techniques • Work with an actual customer to develop a product • Prepare documentationin adherence to IEEE standards

14. Course Overview-2 • Two-semester (32-week) required course • Approximately 30 seniors enrolled • Instructor-selected teams consisting of 5 students • Students: limited background in SE principles, methods, and process

15. Cross-Disciplinary Features • High interaction with clients (e.g., geoscientist and graduate students) to define requirements • Guest speakers used to provide background • Experts used to critique prototypes and validate deliverables • Experts provide feedback to students at the end-of-semester presentations

16. Course Benefits • Students learn how to interact with people from different disciplines • Students develop cross-disciplinary knowledge • The course results in a working prototype that can be adopted and deployed • GEON Perspective • Prepare students who can work on cross-disciplinary projects in the geo-science domain • Contribute to the geo-science toolkit • Promote the field of geoinformatics

17. Master-Apprenticeship Learning Cycle

18. Master-Apprenticeship Learning Cycle

19. Master-Apprenticeship Learning Cycle

20. Deliverables: SE1 • Interview report • Feasibility report • Software Requirements Specification • Interface prototype • Analysis diagrams and models • Use Case diagrams • Scenarios • Class diagrams • State transition diagrams • Dataflow diagrams

21. Deliverables: SE2 • Configuration management plan • Design document • Test plans • “Contracted” software • Funded students complete software development after the course completes

22. Effective Teams • Students must leave course with demonstrated abilities to work in teams. • Teams must be monitored. • Basis: cooperative paradigm • Positive interdependence • Promotive interaction • Individual accountability • Teach team skills • Group processing

23. Creating Teams • Goal: heterogeneous teams • Process • Submit application letter and resumé • Present in-class workshop on personality types • Create set of equally capable teams of 5 • Consider position preferences and expertise • Consider grades, work, and extracurricular experience • Balance teams wrt diversity considering culture, gender, and personality

24. Team Skills • Develop basic leadership skills • Setting agendas • Assigning roles in meetings • Clarifying assignment • Defining tasks and timelines • Ensuring progress is made and deadlines are met • Maintain meeting minutes and task assignment sheets • Model and teach team skills

25. Individual Accountability • Observe student behavior when project teams are working. • Create and maintain team notebooks • Meeting records • E-mail trail • Rough drafts • Submit statement of work • Document individual, subgroup, and team work • Signed by all members • Question each member during presentations

26. Group Processing • Request after each deliverable • Ask questions such as: • Did you complete your task on time? • How did you encourage participation from another team member? • What is working well in your team? • What needs to be improved in your team? • Consolidate and share anonymous responses • Identify problems and skills that resolve them

27. Projects: Gravity Data Repository System (GDRP)-1 • Need for GDRP • Accumulated gravity measurements stored at numerous research centers around world • Effort seeks to establish a combined database • Relevance of gravity data • Important source of geophysical and geological information • Geophysical models are designed to fit gravity measurement and used to generate models of lithospheric structure

28. Projects: Gravity Data Repository System (GDRP)-2 • Relevance con’t • Measurements used by geologists, scientists, oil and mineral exploration companies, environmental consultants • Features of GDRP • Gravity data warehouse • Upload and download validated data • Collection of tools • Access data • Visualize data • Manipulate data

29. Projects: Seismic Waves Rock Correlation System (SWRoCS)-1 • Need for SWRoCS • Earthscope and Geoinformatics initiatives call for better access of data on physical properties of rocks and minerals • Understanding of how properties vary with temperature and pressure • Relevance • Required to interpret geophysical data • Facilitates integrated analysis of different types of data

30. Projects: Seismic Waves Rock Correlation System (SWRoCS)-2 • Features of SWRoCS • Identify rock based on mineral composition or seismic properties • Determine physical properties of named rock • Experiment with related seismic properties of rocks • Extend knowledge base • Navigate ontologies • View general information • Limitations • Restricted to intrusive igneous rocks and component minerals • Properties: S-wave velocity, density, % anisotropy

31. Projects: Seismic Tomography • Overview: • Facilitates construction and experimentation of 3-D structure of Earth • Calculates 2-D or 3-D models using travel-time tomography algorithm of Hole and first- arrival travel times and rays using Vidale’s finite difference solution

32. Challenges • Establishing collaboration • Time investment • Communication gap

33. Contact • E-mail: agates@utep.edu • SE Websites: http://www.courses.utep.edu/CS4310FS

34. Breakout Session

35. Reusable Course Components • What would be useful for you? • Presentation material • Pre-requisite knowledge • Learning objectives • Exercises/projects • Handouts • Exam questions • Resources for background

36. Exercise • Brainstorm on ideas for a software project related to geoinformatics • Rules • Each group member, in turn, states an idea. NO idea is criticized. • As ideas are generated, write each one on a flipchart. Don’t abbreviate or interpret. • Ideas are generated in turn until each person passes.

37. Uncertainty and Knowledge Representation in Geoinfomatics-1 • Offered in spring 2004 by Vladik Kreinovich • Attended by 13 graduate CS students • Course objectives • to learn general techniques of representing and processing uncertainty and • to learn how to use these techniques in geoinformatics (using geospatial applications)

38. Uncertainty and Knowledge Representation in Geoinfomatics-2 • Topics • Motivation for estimating and processing uncertainty • Techniques for estimating uncertainty of the results of data processing • Techniques for representing and processing expert uncertainty • Geospatial applications in uncertainty: methods for detecting outliers and duplicates • Determination of geospatial characteristics based on measurement results