CS 3571: Multimedia Databases & Applications
首頁 向上 Fundamentals of Computer Science Linear Algebra Java Language Probability & Statistics CS 3571: Multimedia Databases & Applications CS 5730: Data Mining Multimedia Computing Systems Special Topics in Database Management Systems (II) S.T. on Multimedia Databases

 

多媒體資料庫及應用

Dr. Fenn-Huei Simon Sheu (許奮輝)

Phone: (03) 574-2959, Office: 資電館 638

Email: sheu@cs.nthu.edu.tw

Spring 2005

 Objective:

      This course aims to introduce multimedia databases and develop the theoretical foundations for applicable deployments. Based on XML databases, exemplar web applications will be used for usability demonstration. Techniques for knowledge extraction from web data will be discussed to solidify the intelligences therein. Students will learn the way to apply these disciplines for their subsequent research work and the corresponding application development.

Prerequisite: Calculus, Linear Algebra, Engineering Mathematics, Self Motivation.

Class Time & Room: T5F5F6, 資電館 128

Office Hours: T7T8, R7R8, or by appointment.

Lecture notes & Homework assignments:

Textbook:

  •  Soumen Chakrabarti, "Ming the Web, Discovering Knowledge from Hypertext Data," 1th edition, (ISBN: 1-55860-754-4), Morgan Kaufmann Publishers, 2003.

Reference:   

  • Jiawei Han and Micheline Kamber, "Data Mining: Concepts and Techniques," (ISBN: 1-55860-489-8), Morgan Kaufmann Publishers, 2001.

  • Ian H. Witten, Alistair Moffat, and Timothy C. Bell, "Managing Gigabytes, Compressing and Indexing Documents and Images," 2nd edition, (ISBN: 1-55860-570-3", Morgan Kaufmann Publishers, 1999.

  • Ricardo Baeza-Yates and Berthier Ribeiro-Neto, "Modern Information Retrieval," (ISBN: 0-201-39829-X), Addison Wesley, 1999.

  • Matthew Langham and Carsten Ziegeler, "Cocoon: Building XML Applications," 1th edition, (ISBN: 0-7357-1235-2), New Riders Publishing, 2002.

  • Neal Ford, "Art of Java Web Development," 1th edition, (ISBN: 1-932394-06-0), Manning Publications Co., 2004.

  • Lecture notes.

Topics:

 

Introduction (Web Mining)

Crawling the Web

 

Web Search & Information Retrieval

Web Applications (Cocoon)

 

Similarity & Clustering

Supervised Learning

 

Semi-supervised Learning

Social Network Analysis

  Resource Discovery The Future of Web Mining

Grading:

  • Critical Review (30%),

  • Midterm (30%),

  • Project (Presentation, Demo, & Reports) (40%).

Tentative Schedule:

  • Midterm (April 22).