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  1. 1. Project Topic Discussion Sept. 21, 2007 ChengXiang Zhai
  2. 2. Outline <ul><li>How to choose a project topic? </li></ul><ul><li>Broad topic areas </li></ul><ul><li>Sample topics </li></ul>
  3. 3. There are many research problems to work on. It’s more beneficial to the society if we work on problems that reflect real world challenges…
  4. 4. What is a Good Research Problem? <ul><li>A good research problem is a solvable challenge that is well connected to a real world need/problem </li></ul><ul><li>Real word challenges vs. imaginary challenges </li></ul><ul><ul><li>Not all challenges are interesting (to the society) </li></ul></ul><ul><ul><li>Real world challenges are always interesting to work on </li></ul></ul><ul><ul><li>Imaginary challenges may (happen to) be interesting </li></ul></ul><ul><ul><li>Spend your effort to solve interesting challenges so that you’ll make more contributions to the society </li></ul></ul><ul><li>However, not all real world problems are challenges; some are straightforward to solve </li></ul><ul><li>Not all challenges/problems are solvable (with limited resources, time, money, tools, etc) </li></ul>
  5. 5. Real Word vs. Imaginary Challenges Real World Needs/Problems Challenges Imaginary Needs/Problems Real world challenges
  6. 6. Identify a Good Research Problem Level of Challenges Impact/Usefulness Known Unknown Good applications Not interesting for research High impact Low risk (easy) Good short-term research problems High impact High risk (hard) Good long-term research problems Low impact Difficult Often publishable, but not good research problems Low impact Low risk Bad research problems Generally not publishable Course project
  7. 7. How to Choose a Topic? <ul><li>Application-driven (Find a nail, then make a hammer) </li></ul><ul><ul><li>Identify a need by people/users that cannot be satisfied well currently (“complaints” about current data/information management systems?) </li></ul></ul><ul><ul><li>How difficult is it to solve the problem? </li></ul></ul><ul><ul><ul><li>No big technical challenges: do a startup </li></ul></ul></ul><ul><ul><ul><li>Lots of big challenges: write an NSF proposal </li></ul></ul></ul><ul><ul><li>Identify one technical challenge as your project topic </li></ul></ul><ul><ul><li>Formulate/frame the problem appropriately so that you can solve it </li></ul></ul><ul><li>Aim at a completely new application/function (find a high-stake nail) </li></ul>
  8. 8. How to Choose a Topic? (cont.) <ul><li>Tool-driven (Hold a hammer, and look for a nail) </li></ul><ul><ul><li>Choose your favorite state-of-the-art tools </li></ul></ul><ul><ul><ul><li>Ideally, you have a “secret weapon” </li></ul></ul></ul><ul><ul><ul><li>Otherwise, bring tools from area X to area Y </li></ul></ul></ul><ul><ul><li>Look around for possible applications </li></ul></ul><ul><ul><li>Find a novel application that seems to match your tools </li></ul></ul><ul><ul><li>How difficult is it to use your tools to solve the problem? </li></ul></ul><ul><ul><ul><li>No big technical challenges: do a startup </li></ul></ul></ul><ul><ul><ul><li>Lots of big challenges: write an NSF proposal </li></ul></ul></ul><ul><ul><li>Identify one technical challenge as your project topic </li></ul></ul><ul><ul><li>Formulate/frame the problem appropriately so that you can solve it </li></ul></ul><ul><li>Aim at important extension of the tool (find an unexpected application and use the best hammer) </li></ul>
  9. 9. How to Choose a Topic? (cont.) <ul><li>In reality, you do both in various kinds of ways </li></ul><ul><ul><li>You talk to people in application domains and identify new “nails” </li></ul></ul><ul><ul><li>You take courses and read books to acquire new “hammers” </li></ul></ul><ul><ul><li>You check out related areas for both new “nails” and new “hammers” </li></ul></ul><ul><ul><li>You read visionary papers and the “future work” sections of research papers, and then take a problem from there </li></ul></ul><ul><ul><li>… </li></ul></ul>
  10. 10. Landscape of Data Management Query capability Scale Data complexity Structured Data Exact Matching Inexact Matching Inferences/Mining Unstructured Data Multimedia Data RDMS
  11. 11. DB and Related Areas Databases Text Information Management (Inforamation Retrieval) Multimedia Information Management Web/Bio Information Management Data Mining/Machine Learning
  12. 12. Map of General Topic Areas Databases IR Multimedia Web/Bio Data Mining Core/Traditional DB (Getting mature) Web/Bio DB Applications DB+IR Data Mining, Decision Support Multimedia DB
  13. 13. The Next Database Revolution [Gray 04] <ul><li>Object Relational </li></ul><ul><li>Web Services </li></ul><ul><li>Queues, Transactions, Workflows </li></ul><ul><li>Cubes and Online Analytic Processing </li></ul><ul><li>Data Mining </li></ul><ul><li>Column Stores </li></ul><ul><li>Text, Temporal, and Spatial Data Access </li></ul><ul><li>Semi-Structured Data </li></ul><ul><li>Stream Processing </li></ul><ul><li>Publish-Subscribe and Replication </li></ul><ul><li>Late Binding in Query Plans </li></ul><ul><li>Massive Memory, Massive Latency </li></ul><ul><li>Smart Objects: Databases Everywhere </li></ul><ul><li>Self-Managing and Always Up </li></ul>
  14. 14. “ ... Our biggest challenge is a unification of approximate and exact reasoning. Most of uscome from the exact-reasoning world – but most of our clients are asking questions with approximate or probabilistic answers….” - Jim Gray [SIGMOD 2004]
  15. 15. Sample Project Topics <ul><li>Touring the project wiki…. https://agora.cs.uiuc.edu/display/cs511/Home </li></ul><ul><li>Fall 06 class projects: </li></ul><ul><li>http://www.cs.uiuc.edu/class/fa06/cs511/present.html </li></ul>
  16. 16. What You Should Do <ul><li>Visit project wiki </li></ul><ul><li>Start thinking about possible topics for your project </li></ul><ul><li>Upload your project ideas </li></ul><ul><li>Discuss your ideas with your classmates </li></ul><ul><li>Form teams </li></ul>