1. Vision + Focus + Execution Meiliu Lu, RVR 5016, mlu@csus.edu mlu@csus.edu http://gaia.ecs.csus.edu/~mei/ For CSc 209 Spring 2003, 5/6/03

2. 2 Outline Vision: know what is going to happen Focus: Interests – key elements to keep you going, select project topic Execution: methodology Take a look at how others have done Prepare, start, and finish yours

3. 3 R. W. Hamming on R & D What can happen  Science What will happen  engineering & economics What should happen  morals, ethics and including what society is prepared to accept and reject

4. 4 Richard M. Karp’s 3 Principles on Making Career Decisions Understand what you are good at and what you like to do, and choose accordingly. In the words of Socrates, “Know thyself.” Disregard the fashions of the day and search for new areas of research that are about to become important. In the words of the great hockey player and philosopher Wayne Gretzky, “Skate to where the puck is gonna be.” To find exciting problems, look at the interfaces between disciplines.

5. 5 Research Interests and Projects Machine learning and data mining DMV Data mining (clustering and data warehousing, OLAP) Data management and integration Web portal development (Searchware) Data integration and visualization (multiple data sources and single analytical view) Courseware Development XML course module (136) Data Warehousing course module (196K) SVM, support vector machine (196K, 219)

6. 6 Select Project Topic Select a topic according to your interest and factors such as following: Utility vs. innovation Learning Timing

7. 7 What is Data Mining? Data mining, simulation, and modeling are part of a large process – Knowledge Discovery in Data (KDD) Finding hidden information in a database Fit data to a model Web related data Business, health, and financial data Scientific and Engineering

8. 8 Data Mining Algorithms Objective: fit data to a model Descriptive Predictive Preference – technique to choose the best model Search – technique to search the data “Query”

9. 9 Query Examples Database Find all credit applications with last name of smith Identify customers who have purchased more than $10,000 in the last month. Find all customers who have purchased milk Data mining Find all credit applicants who are poor credit risks. (classification) Identify customers with similar buying habits. (clustering) Find all item which are frequently purchased with milk. (association rules)

10. 10 Extracting Knowledge from Gene Expression Data: A Case Study of Batten Disease – S. M. Lin Duke University Medical Center proposed a prototype KDD system to enable scientists to analyze the massive microarray data, form hypotheses, and draw insights directly into underlying mechanisms of diseases. Data  Microarray database  data mining  patterns  human experts  Genomics knowledge base  discoveries

11. 11 Your MS Project Try to make someone happy yourself people who helped you people who need help Start early, keep working, finish on time

12. 12 Sample MS Project 1 Jeffery Lewis, Spring 2001, “Audio Compression for Music Synthesis Using Gaussion Functions and Genetic Algorithms” Paper published in the proceedings of 3rd International Conference on Information Reuse and Integration. November 2001 Self-selected topic

13. 13 Sample MS Project 2 Yan Xiong, Summer 2002, “A Web- Based Microarray Management System” Paper published in the 15th International Conference on Computer Applications in Industry and Engineering. November 2002. Topic introduced at CSC 209 Fall 2001 Improvement: engineering and function

14. 14 Sample MS Project 3 Wen Xia, Fall 2002, “Online Course Management System” Developed for C Sc 138 and can be adopted for other courses Topic selected by self (continue development of an existing tool) More system enhancement can be a new MS project

15. 15 Writing Report “MS Thesis and Project Writing Guide” at: http://gaia.ecs.csus.edu/~mei/http://gaia.ecs.csus.edu/~mei/ Format, References and Acknowledgement A useful reference book: “Writing for Computer Science” by Justin Zobel, Springer, 1998. Game of time and stepping stone

16. 16 Leaders are Visionaries Dr. Ernie Bodai They have a poorly developed sense of fear They have no concept of the odds against them They make the impossible happen