Overviews of ITCS 6161/8161: Advanced Topics on Database Systems

1. Overviews of ITCS 6161/8161: Advanced Topics on Database Systems Dr. Jianping Fan Department of Computer Science UNC-Charlotte www.cs.uncc.edu/~jfan Course web site: http://www.cs.uncc.edu/~jfan/itcs6161.html

2. Course Web Site • Most useful information (course schedule, • presentation slides, announcements, et al.) • can be found and downloaded at: http://www.cs.uncc.edu/~jfan/itcs6161.html 2. You may check course web site before you come to classroom because this website will be updated frequently! 3. 10 hours/week rule: 2 hours for preparing, 2 hours for reviewing, 3 hours for class, & 3 hours for homework and projects

3. Course Information • Class hour9:25AM - 12:15PM, Friday • Office hour Friday 14:00PM - 18:00PM • Instructor - Dr. Jianping Fan • email - jfan@uncc.edu • Office – Woodward 205B • Webpage http://www.cs.uncc.edu/~jfan • Textbook: we will use the slices and papers on the course web page, but some good books are suggested on web site • Classroom: Woodward Hall 135

4. What we have done in Database? • Data modeling: data is structural and it can be modeled by E-R model! • Data indexing: B-tree for one attribute! • Query are well defined by SQL!

5. What we have done in Database? Database Information Retrieval

6. What we have done in Database? Internet is changing everything! Information Retrieval Database Web Database

7. What are Advanced Topics? • Data Types are advanced rather than relational data! • Data Analysis Tools are advanced rather than traditional ones! • Applications are advanced rather than relational database!

8. Course Objectives Google, Yahoo! & MSN IE Big Data? How can I access web-scale data in database over Internet? Internet User Data Server

9. What are Advanced for such application? • Data Types: Multi-Modal Data without • structure! • Data Analysis Tools: E-R model could be to • simple! • Applications: It is part of our daily life!

10. Course Content Problems we should address in this class: 1. How to store web-scale data? 2. How to analyze web-scale data ? 3. How to index web-scale data ? 4. How to access web-scale data in database? 5. How to control user’s access ? Web-scale data are always in multi-modals

11. Why we should have this course? • Good job market: Google, Yahoo!.... • Have fun: solving real problem • Not so “hard” to learn (??) • Next generation search engines

12. Tools to be Introduced • a. Advanced Data Organization Tools; • b. Advanced Data Analysis Tools; • Machine Learning & Data Mining Tools for • Knowledge Discovery from web-scale data collections. Internet is changing our life but ………

13. Database System Tools • Data Representation Schema • Database Indexing • Database Storage • Query Management

14. Data Analysis Tools • Image & Video Analysis & Feature Extraction • Object Detection & Scene Understanding • Classifier Training for object and concept • detection • d. Scene Configuration and Structure

15. Knowledge Discovery Tools • GMM & Bayesian Network • Support Vector Machine (SVM) • Graphical Models & Structure Learning • Statistical Inference

16. Course Topics • Data Mining Tools • Machine Learning Tools • Image/Video analysis and feature extraction • Image/Video Database indexing • Image/Video transmission over networks • Query refinement for image/video retrieval • Open discussion & topic-based student presentation • What Yahoo!, Google are doing now

17. Grading • Composition • Project 25% • Show-up and understanding 10% • Midterm 30% • Final 35% • Scale • >93% = A • 75-93% = B • 55-74% = C • <55% or cheating = F

18. Class Policy • You have to attend the class and come to classroom on time (9:25am)! • You should be ready to learn from the class • You should respect your classmates: come to learn from their presentations!

19. Course Project • Develop video analysis system using Visual C++ and Java. • Each group consists 3-4 students • 3-4 hours workload each week is expected • Java or C++ assumed • Research Presentation Project • Video Analysis Project • More information • http://www.cs.uncc.edu/~jfan/itcs6161.html

20. Midterm & Final Tests • closed books and notes • One page notes is permitted • Cumulative • No makeup • Bonus is expected

21. Suggestions from Instructor • Do your best in the class • Show your problems to the instructor when you cannot make it • Show the evidence to us if you think you are right. • Open discussion is welcome

22. Google Search Engine Who cares? • Google Search Engine

23. Who cares?

24. Who cares? Google & Yahoo!

25. Who cares? You & Your Start-ups

26. The way to join them • Good grade from class • More training on programming skills, especially for multimedia analysis, indexing and retrieval • Get recommendation from professor

27. Recommendation • Good grade is very important, but it is not everything! • Learning something and solving one problem you like may be more important! • Learning from someone who may make you better! Especially your classmates

28. Research areas we will touch • Computer Vision • Database & Data Mining • Information Retrieval • Machine Learning & AI • Visualization • Networks • Statistics & Security

29. Q & A

30. You have chance! If these are too hard for you, you still have chance to withdraw now!