Aims and Fit of Module

This module provides students with a comprehensive understanding of the advanced topics in database systems, i.e. the fundamental algorithms and techniques in both relational and non-relational databases systems, as well as the principles of data warehouse and data mining. It equips them with the skills to use, design and analyse traditional and modern database systems. This module prepares students on programmes relating to information science, computing and data analytics with a solid foundation for modules including data mining, machine learning and data analytics.

Learning outcomes

A. Critically analyse and compare advanced database techniques, such as indexing, query processing and optimisation;
B. Explain and apply principles underpinning transaction management within relational DBMS;
C. Effectively use and compare recent Web technologies and data representation formalisms, including both semi-structured and structured data, in developing database solutions;
D. Distinguish and analyse principles underlying object-relational databases, distributed databases, big data storage systems and blockchain-based storage systems;
E. Explain main concepts in data warehousing and data mining, and demonstrate their uses in real-world applications.

Method of teaching and learning

This module makes use of a diverse range of teaching and learning methods to support students in mastering topics on advanced databases. These include lectures to introduce fundamental theories, algorithms and techniques, tutorials to discuss the applicability of theoretical knowledge and solve different types of problems, and supervised labs to practice database techniques. Independent study is emphasised, with students tasked to review textbook and academic literature, prepare for practical sessions and complete coursework assignments.