- To study the methodology of engineering legacy databases for data warehousing and data mining to derive business rules for decision support systems.
Lecture and Tutorial:
Lecture 1 Data Warehousing I - XML Database Lecture 2 Data Warehousing II - Schema Translation Lecture 3 Data Warehousing III - Schemas Integration Lecture 4 Data Warehousing IV - Star Schema & Data Cube Lecture 5 Data Warehousing V - Online Analytical Processing Lecture 6 Data Warehousing VI-Data conversion & Integration Lecture 7 Data Mining I - Association Rules Lecture 8 Data Mining II - Web Mining Lecture 9 Data Mining III - Decision Tree Lecture 10 Data Mining IV - Clustering Lecture 11 Data Mining V - Genetic Algorithm Lecture 12 Data Mining VI - Neural Network Lecture 13 Review
- “Data Mining: Concepts and Techniques” by Jiawei Han and Micheline Kamber, published by Morgan Kanfmann Publishers, Second Edition.
- “Information Systems Reengineering and Integration” by Joseph Fong, published by Springer Verlag, 2006, ISBN 978-1-84628-382-6, Second edition.
Make-up Tutorial Session:
- A computer Laboratory session will be available in room 2450 for students to work together for their project every Saturday from 4:30pm to 5:30pm.
- In class review question will be asked after each lecture. Students are encouraged to work with each other, but not copying other's answer.
- Homework tutorial questions to be worked at home and submitted in each tutorial session. Students are encouraged to ask questions in course open forum.
- Team project assignment on developing a data warehouse system with prototype implementation to be submitted in week 14 with a report document and voiced movie demonstration.
Outcome based Teaching and Learning
- This course emphasizes in practicing the learnt material in the class.
- The examination will be open book to assess students’ understanding of the course materials to pass the course.
- To get high marks in the course, students must know how to apply the learnt materials in the class.
- The hands-on exercises are to train students to implement their team projects technically.
- Students are encouraged to work together to do their coursework for the questions and answers and the project.