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2019/20 Taught Postgraduate Module Catalogue

COMP5111M Big Data Systems

15 creditsClass Size: 200

Module manager: To be confirmed

Taught: Semester 2 View Timetable

Year running 2019/20

This module is not approved as an Elective

Module summary

The aim of the module is for students to develop a practical understanding of methods, techniques and architectures needed to build big data systems required, so that knowledge may be extracted from large heterogeneous data sets.


On completion of this module, students should be able to:
- Understand the history, characteristics and future of big data
- Understand the contexts in which big data systems are applied
- Design systems architectures that could be used to implement big data solutions for given scenarios
- Implement key components of a big data system

Learning outcomes
On completion of the year/programme students should have provided evidence of being able to:
-to demonstrate in-depth, specialist knowledge and mastery of techniques relevant to the discipline and/or to demonstrate a sophisticated understanding of concepts, information and techniques at the forefront of the discipline;
-to exhibit mastery in the exercise of generic and subject-specific intellectual abilities;
-to demonstrate a comprehensive understanding of techniques applicable to their own research or advanced scholarship;
-proactively to formulate ideas and hypotheses and to develop, implement and execute plans by which to evaluate these;
-critically and creatively to evaluate current issues, research and advanced scholarship in the discipline.


Overview: history and definitions of big data, the five 'Vs' (Volume, Velocity, Variety, Veracity & Value), technology landscape, and future predictions (data, analysis capacity, business opportunities & employment). Application contexts: Structure & properties of data, analysis scenarios and case studies (e.g., nuclear physics (volume), social media (velocity), and medical bioinformatics or consumer retail (variety)). Systems architectures, encompassing data acquisition, storage, linkage, computation, security/confidentiality, and end-users. Key system components, e.g., Hapoop, MapReduce, parallel databases, SQL vs. NoSQL, algorithm scalability, and exploiting existing infrastructure (e.g., Cloud).

Teaching methods

Delivery typeNumberLength hoursStudent hours
Private study hours117.00
Total Contact hours33.00
Total hours (100hr per 10 credits)150.00

Opportunities for Formative Feedback

Coursework and labs.

Methods of assessment

Assessment typeNotes% of formal assessment
Total percentage (Assessment Coursework)30.00

This module is re-assessed by exam only.

Exam typeExam duration% of formal assessment
Open Book exam2 hr 00 mins70.00
Total percentage (Assessment Exams)70.00

This module is re-assessed by exam only.

Reading list

There is no reading list for this module

Last updated: 05/11/2019 08:50:06


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