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Project Topic Discussion

Project Topic Discussion. Sept. 21, 2007 ChengXiang Zhai. Outline. How to choose a project topic? Broad topic areas Sample topics . 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….

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Project Topic Discussion

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  1. Project Topic Discussion Sept. 21, 2007 ChengXiang Zhai

  2. Outline • How to choose a project topic? • Broad topic areas • Sample topics

  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. What is a Good Research Problem? • A good research problem is a solvable challenge that is well connected to a real world need/problem • Real word challenges vs. imaginary challenges • Not all challenges are interesting (to the society) • Real world challenges are always interesting to work on • Imaginary challenges may (happen to) be interesting • Spend your effort to solve interesting challenges so that you’ll make more contributions to the society • However, not all real world problems are challenges; some are straightforward to solve • Not all challenges/problems are solvable (with limited resources, time, money, tools, etc)

  5. Real Word vs. Imaginary Challenges Real world challenges Challenges Real World Needs/Problems Imaginary Needs/Problems

  6. Identify a Good Research Problem High impact High risk (hard) Good long-term research problems Low impact Difficult Often publishable, but not good research problems High impact Low risk (easy) Good short-term research problems Low impact Low risk Bad research problems Generally not publishable Good applications Not interesting for research Course project Level of Challenges Unknown Known Impact/Usefulness

  7. How to Choose a Topic? • Application-driven (Find a nail, then make a hammer) • Identify a need by people/users that cannot be satisfied well currently (“complaints” about current data/information management systems?) • How difficult is it to solve the problem? • No big technical challenges: do a startup • Lots of big challenges: write an NSF proposal • Identify one technical challenge as your project topic • Formulate/frame the problem appropriately so that you can solve it • Aim at a completely new application/function (find a high-stake nail)

  8. How to Choose a Topic? (cont.) • Tool-driven (Hold a hammer, and look for a nail) • Choose your favorite state-of-the-art tools • Ideally, you have a “secret weapon” • Otherwise, bring tools from area X to area Y • Look around for possible applications • Find a novel application that seems to match your tools • How difficult is it to use your tools to solve the problem? • No big technical challenges: do a startup • Lots of big challenges: write an NSF proposal • Identify one technical challenge as your project topic • Formulate/frame the problem appropriately so that you can solve it • Aim at important extension of the tool (find an unexpected application and use the best hammer)

  9. How to Choose a Topic? (cont.) • In reality, you do both in various kinds of ways • You talk to people in application domains and identify new “nails” • You take courses and read books to acquire new “hammers” • You check out related areas for both new “nails” and new “hammers” • You read visionary papers and the “future work” sections of research papers, and then take a problem from there • …

  10. Landscape of Data Management RDMS Query capability Inferences/Mining Inexact Matching Exact Matching Structured Data Scale Unstructured Data Multimedia Data Data complexity

  11. DB and Related Areas Web/Bio Information Management Multimedia Information Management Text Information Management (Inforamation Retrieval) Databases Data Mining/Machine Learning

  12. Map of General Topic Areas Web/Bio DB Applications Multimedia DB DB+IR Data Mining, Decision Support Core/Traditional DB (Getting mature) Web/Bio Databases Multimedia IR Data Mining

  13. The Next Database Revolution [Gray 04] • Object Relational • Web Services • Queues, Transactions, Workflows • Cubes and Online Analytic Processing • Data Mining • Column Stores • Text, Temporal, and Spatial Data Access • Semi-Structured Data • Stream Processing • Publish-Subscribe and Replication • Late Binding in Query Plans • Massive Memory, Massive Latency • Smart Objects: Databases Everywhere • Self-Managing and Always Up

  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. Sample Project Topics • Touring the project wiki…. https://agora.cs.uiuc.edu/display/cs511/Home • Fall 06 class projects: http://www.cs.uiuc.edu/class/fa06/cs511/present.html

  16. What You Should Do • Visit project wiki • Start thinking about possible topics for your project • Upload your project ideas • Discuss your ideas with your classmates • Form teams

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