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COMP3420: Advanced Databases and Data Mining

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(6 units) Group C

Thirty lectures and six two-hour tutorial/laboratory sessions

Lecturers: Dr Weifa Liang and Dr Peter Christen

Prerequisites

COMP1100 or COMP2720; COMP2400; 6 units of 2000-series IT; and 6 units of 1000-series MATH/STAT courses

Syllabus

This course examines the design of data warehouses and their use for data mining. Topics may include: star and snowflake data models for data warehouses, architectures of data warehouse, fact tables and datacube computation, materialized view computation and maintenance, OLAP (On-Line Analytical Processing), data preprocessing (data extraction, data cleaning, data reduction, data transformation, and data integration), data mining concepts and techniques including associated rules, clustering, etc.

Description

The course deals with data analysis on very large volume of data to help business making strategic decisions. Specifically, it considers the issues related to the design and implementation of data warehouses, multidimensional data analysis, and fundamental data mining techniques and applications built on data warehouses.

Rationale

This course is about the deployment of advanced database techniques in modern enterprises and business. It aims to provide the students with the state-of-art conceptual and practical knowledge on recent development in data warehousing and data mining. On completion of this course, the students should have gained a good understanding of basic concepts, principles and techniques in data warehousing and data mining. Specifically, the students are able to perform the following tasks.

• perform data modeling
• apply OLAP techniques for mulit-dimensional data analysis
• apply datacubing techniques
• develop general skill of data warehousing project management
• obtain the general knowledge on the design and implementation of data war\ ehouses
• be able to apply data mining techniques for knowledge discovery
• develop in-depth understanding of fundamental data mining algorithms
• perform data mining in data warehouses.

Skills

Will acquire a good knowledge of design and implementation of data warehouses; will master the data analysis tools like OLAP and OLAM for multidimensional data analysis; will get exposure to fundamental data mining concepts and techniques; will develop skills by applying data warehousing and data ming technique to help large enterprises and business making strategic decisions.

Assessment

There will be two assignments and one final exam. The overall assessment will be based on a 40:60 weighting for the assignments (A/100) and the examination (E/100). It will be calculated using the following formula:

Total = 0.4A+0.6E

Note that final marks are moderated in departmental examiners' meetings and may be scaled as a result of this moderation. The departmental policy on plagiarism will be enforced.

Recommended Reading

• Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2nd Edition, March 2006, ISBN 1-55860-901-6. TEXTBOOK
  
• Elmasri and Navathe Fundamentals of Database Systems. Addison-Wesley, 5th Edition, 2007.