CSMBD21-Big Data and Cloud Computing

Module Provider: School of Mathematical, Physical and Computational Sciences
Number of credits: 20 [10 ECTS credits]
Level:7
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2021/2

Module Convenor: Prof Atta Badii
Email: atta.badii@reading.ac.uk

Type of module:

Summary module description:

This module covers the topic of big data and advanced computing.

Aims:

The massively increased uptake of computing, with devices at all scales of operation, has driven the development of large-scale distributed systems capable of meeting the demands for handling scalable parallel data analysis and processing and supporting execution of analytical algorithms on computer clusters such as Hadoop. This module aims to introduce the concepts and design principles for big data analytics and advanced network-centric computing platforms.

This module also encourages students to develop a set of professional skills, such as software development documentation, technical reporting writing, and project management.

Assessable learning outcomes:

It is expected that students will be able to:

• Identify and describe the challenges of analysing big data and appraise relevant algorithms, tools and techniques to tackle such challenges;


• Analyse complex data in structured, semi-structured and/or unstructured format and adopt/adapt analytics techniques to tackle the problems and evaluate the solutions;


• Validate and redefine solutions for analytics problems, so that they could be applied to new but similar problems;


• Understand the rationale for the design choices to be made in building live web-scale service-oriented architectures;


• Acquire an integrated perspective on data processing in cloud computing platforms;


• Address socio-legal, security, privacy and trust issues involved in operating and using cloud services;


• Apply the cloud computing skills for data management and distributed and parallel data process ing.

Additional outcomes:

It is expected that students will also be able to:

• Recognise real world applications of big data analytics and also demonstrate how to deploy and evaluate data mining applications for big data on computer clusters;


• Become familiar with the potential applications of cloud computing.

Outline content:

The contents are organised in two parts:

Part 1

• Introduction to big data analytics principles and challenges;


• Techniques and tools for large data set analysis, including unstructured data analysis;


• Algorithms and tools for the analysis of fast streaming real time data;


• Techniques for building recommender systems.

Part 2

• Introduction to distributed and parallel computing; Cloud Computing (IaaS, PaaS, SaaS, AI-as-a-S);


• Framework design of large-scale distributed systems to support web-scale service-oriented architectures;


• Security and privacy protection challenges in Cloud Computing;


• Cloud Computing middleware, Map/Reduce; RESTful systems;


• Cloud computing design features, such as consistency, availability and partition toler ance in distributed Information Systems, consistent Hashing, parallelism and computational efficiency;


• Trustless Distributed Ledger Technologies and applications.

51ºÚÁϳԹÏÍø List: selected essential text:

• Data Mining, Concepts and Techniques, (Second Edition) Jiawei Han, Micheline Kamber Morgan Kaufmann Publishers, March 2006. ISBN: 978-1-55860-901-3


• Mahout in Action Sean Owen, Robin A nil, Ted Dunning, and Ellen Friedman ISBN 9781935182689

Brief description of teaching and learning methods:

The module comprises lectures, practical sessions, and a project-based assignment.

Summative assessment- Examinations:

One 2 hour examine paper in May/June.

Summative assessment- Coursework and in-class tests:

One piece of project-based assignment.

Formative assessment methods:

Penalties for late submission:

The below information applies to students on taught programmes except those on Postgraduate Flexible programmes. Penalties for late submission, and the associated procedures, which apply to Postgraduate Flexible programmes are specified in the policy 􀀓Penalties for late submission for Postgraduate Flexible programmes􀀔, which can be found here: