access keys text only skip to navigation skip to content

Module Description

Module Name, Abbreviated Name, Code

Data Warehousing and Data Mining, DWDM, COIY026H7

Credits, Level

15 credits, level 7

Lecturer

Nigel Martin

Online Material

Module web pages

Aims

To study advanced aspects of data warehousing and data mining, encompassing the principles, research results and commercial application of the technologies.

Syllabus

  • Review of database technology underpinning data warehousing and data mining.
  • Data warehouse logical design: star schemas, fact tables, dimensions, snowflake schemas, dimension hierarchies, data marts.
  • Data warehouse physical design: partitioning, parallelism, compression, indexes, materialized views, column stores.
  • Data warehouse construction: data extraction, transformation, loading and refreshing. Data warehouse support in Oracle. Warehouse metadata. Specialised warehouse architectures.
  • From data warehousing to data mining: OLAP architectures, OLAP operations. SQL extensions for OLAP.
  • Data mining approaches and applications. Data mining technologies and implementations. Techniques for mining large databases.
  • Data mining support in commercial systems. Data mining standards.
  • Research trends in data warehousing and data mining.

Prerequisites

A first module in Database Systems (e.g. as taught in a typical UK undergraduate degree in computer science)

Timetable

All dates and timetables are now listed in the programme booklets of the individual programmes.

Coursework

Practical exercise involving programming and design aspects of a data warehouse.

Assessment

By 2-hour written examination and practical coursework. The final module mark will be the exam mark attained. Passing the practical coursework component will be compulsory in order to pass the module overall.

Recommended Reading

  • 1. R. Ramakrishnan, J. Gehrke, Database Management Systems (3rd ed.), McGraw Hill, 2003, ISBN 0-07-246563-8.
  • 2. M. Golfarelli, S. Rizzi, Data Warehouse Design: Modern Principles and Methodologies, McGraw Hill, 2009, ISBN 978-0-07-161039-1.
  • 3. J. Han, M. Kamber, Data Mining Concepts and Techniques (3rd ed.), Morgan Kaufmann, 2011, ISBN 978-0-12-381479-1.