Computer Networks

Aims

This course will examine computer networks within the context of the Internet. We will study the fundamental principles, elements, and protocols of computer networks. We will investigate how the different protocols work, why they work that way, and their performance trade-offs. Using this knowledge, we will try to examine the way applications are deployed on the Internet and their performance trade-offs. In particular, we will try to examine some strategies that are commonly used to accelerate application level performance in the context of the operation of the Internet.

Learning Outcomes

On completing the course students will be able to:

• Explain the operation of a range of computer networking applications such as email, web, and peer-to-peer file-sharing
• Relate the architecture of the Internet to the underlying design principles
• Illustrate the operation of common routing protocols, queuing mechanisms, and congestion control mechanisms
• Develop elements of a network such as gateways and routers that conform to IETF standards with acceptable levels of simplification
• Explain the performance of a given set of routing protocols, queuing mechanisms, and congestion control mechanisms on an example network.

Content

• Introduction to Computer Networks
• Sockets Programming
• Protocol Stacks and Layering: Application Layer, Physical Layer, Link Layer Basics.
• Switching & Flow Control
• Ethernet and Bridging
• IP forwarding & addressing
• IP Packets & Routers
• Routing: RIP & OSPF, Routing: BGP, Multicast, DNS, IPv6, tunnelling, NAT, VPN, Virtual circuits, ATM, MPLS, Transport Intro.
• TCP & Congestion Control.
• TCP Performance
• Multimedia/QoS, QoS & Mobile (IP & TCP)
• Ad-hoc networks
• Web + CDNs + Caching, P2P
• Security - SSL, Security - firewalls, DoS
• Broadband access networks (xDSL,UWB, DOCSIS)

Reading

Kurose J. F., Ross K. W. (2007) Computer Networking: A Top-Down Approach, Addison Wesley, 6th edition.

Peterson L. L., Davie B. S. (2007) Computer Networks ISE: A Systems Approach, Morgan Kaufmann; 4th edition.

Stallings W. (2008) Data and Computer Communications, Pearson Education, 8th edition.

Web Programming

Aims

The students will get acquainted with all modern tools and principles of modern Web Information Systems through this course. An introduction will be given to basic internet protocols and applications and the course will guide the students in more advanced web architectures and implementation using modern programming language tools and security implementations.

Learning Outcomes

On completion of the course students will be able to:

• Understand the principal protocols, architectures and standards for Internet and Web applications
• Develop simple Web applications, using modern tools of Java, XML and PHP
• Incorporate commonly used security protocols (SSL, HTTPS) in their information system design
• Adapt their web design to enhance reliability, efficiency and internationalisation
• Understand the basic principles and future directions of Web 2.0

Content

• Internet and the Web protocols and standards.
• Architecture and Components of Web-Based Applications (3-tier and multi-tier Client/Server systems, Web servers, Database servers).
• Design and implementation of applications on the Internet environment with the use of modern tools (Java technology, XML, PHP etc).
• Advanced Design Issues (Efficiency, Reliability, and Internationalisation).
• Security / encryption protocols (SSL, HTTPS). Web 2.0.

Reading

Taniar D., Rahayu J. W. (2004) Web information systems Hershey, PA: Idea Group Publishing.

Vidgen R., Avison D., Wood B., Wood-Harper T. (2002) Developing Web Information Systems: From Strategy to Implementation, Butterworth-Heinemann Information Systems Series, Elsevier.

M. Stepp, J. Miller, V. Kirst (2012) Web Programming Step-by-Step, Step-By-Step Publishing

Information Systems Security

Aims

This course provides an introduction to the foundational aspects of cybersecurity and computer security. Most modern organisations face security and privacy risks that threaten their valuable assets. It is imperative to design secure and privacy-aware information systems that protect against these threats. This course provides a wide range of skills and knowledge of existing technologies, security and privacy principles to develop the professional skills and experience needed for information systems security.

Learning Outcomes

On completing the course students will be able to:

• Develop the knowledge, understanding and skills to work as a computing security professional
• Learn the concepts, principles, techniques and methodologies you need to design and assess complex networks, systems and applications
• Develop the practical experience you need to plan, perform and direct security audits of information systems to the level required by standard security frameworks
• Develop the appropriate legal and ethical skills you need to be a security professional.

Content

• Information security –Security Policy
• Identification -Authentication
• Authorization –Access Control –Auditing -Accountability
• Malicious Attacks-Malware
• Hash Functions -Digital Signatures Public Key Infrastructure (PKI) -Digital Certificates
• Firewalls
• ISO 27001
• Application Security

Reading

• Computer Security, D. Gollmann, J. Wiley & Sons, third edition, 2011
• Security Engineering, R. Anderson, J. Wiley, second edition, 2008
• Cryptography and Network Security: Principles and Practice, W. Stallings, Prentice Hall, fifth edition 2010
• Practical Unix and Internet Security, S. Garfinkel, G. Spafford, O'Reilly & Associates, Inc., third edition, 2003
• Privacy-What Developers and IT Professionals Should Know, J.C.Cannon, Addison Wesley, 2005

Advanced Database Systems

Aims

The course aims to familiarise students with contemporary database systems, as well as emerging database technologies. It discusses basic aspects of advanced database techniques and exposes tools and technologies that can be used along with “core” database systems. Students are expected to engage in practical database system design through a series of assignments and coursework. The emphasis in the lectures will be on general concepts and theoretical foundations. In addition to the theoretical concepts, the course will require students to use commercial database systems and develop a class project.

Learning Outcomes

Upon successful completion of this course students will be able to:

• Develop the logical model of a relational database
• Use essential SQL tools to program commercial database systems
• Understand advanced concepts of database management and architecture
• Organize, store and process data efficiently, using contemporary methods.
• Understand and apply emerging technologies, including Data Mining, Information Retrieval and XML.
• Undertake a practical database management project.

Content

• ER model, relational model, mapping ER to relational model and basic SQL
• Indexing, query processing and optimization
• Parallel, Distributed and Spatial databases and spatial query processing
• Hadoop ecosystem and mapreduce
• Data Warehousing and OLAP
• Data Mining and Business Intelligence
• Information Retrieval, Web Search and XML

Reading

Elmasri R., Navathe S. B., (2010), Fundamentals of Database Systems: Global Edition, 6th Edition, Pearson.

Garcia-Molina H., Ullman J., and Widom J., (2009), Database Systems: The Complete Book, 2nd edition, Pearson.

Silberschatz A., Korth H., and Sudarshan S., (2010), Database System Concepts, 6th Edition, McGraw-Hill.

Ramakrishnan R, Gehrke J. (2002), Database Management Systems, 3rd edition, McGraw-Hill Science/Engineering/Math.

Foundations of Computing

Aims

This course will deal with the fundamentals of object-oriented software development. We will investigate how an object-oriented systems is designed, and how it can be decomposed to data structures and algorithms. Using this knowledge, we will develop a medium-scale software application, based on well-established principles of object-orientation. Finally, we will place emphasis on software development processes and specification.

Learning Outcomes

On completing the course students will be able to:

• Understand and apply the basics of object-oriented analysis, design, and programming
• Draft software specification documents
• Develop basic algorithms & data structures
• Understand the fundamentals of algorithm complexity

Content

• Basic Concepts of Programming Paradigms.
• Introduction to Object-Oriented Programming
• Basic data structures (Lists, Stacks, Queues, and Heaps)
• General Responsibility Assignment Software Patterns (GRASP)
• SOLID principles for structuring the system
• Sorting (Selection, Insertion, Bubble, Merge, and Quick) and Searching algorithms
• Complexity Theory
• RUP-based Software Specification
• IEEE-based Software Specification
• Unified Modeling Language
• Introduction to testing
• Black- and white-box testing

Reading

1. R. C. Martin (2002), “Agile Software Development, Principles, Patterns, and Practices”, Pearson; 1st edition.
2. C. Larman (2004), “Applying UML and Patterns: An Introduction to Object-Oriented Analysis and Design and Iterative Development”, Prentice Hall; 3rd edition.

Human Computer Interaction, Design and User Experience

Computers have been a part of every aspect of human life for quite a while. A vast number of computing technologies, paradigms, architectures, solutions, applications etc. were born, evolved, matured and died, to give their place in new ones that can harness the ever growing potential of a continuously evolving landscape. Human creativity and ingenuity has played a crucial role in these developments. However, the software market has gone beyond discovering new technologies or improving the existing ones. Except from being operational, software has to be not just easy to use but also intuitive, engaging and pleasant. Human-Computer Interaction (HCI) is a field that addresses the need to create user interfaces that can improve user experience, increase productivity while at the same time providing an environment that is safe and comfortable. HCI involves a confluence of many different disciplines, such as graphic design, cognitive science and psychology, education etc. Therefore, a familiarization of basic concepts of non-computing fields is necessary.

Mobile Applications Development

Aims

Mobile computing has recently emerged with the spread of smartphones and it has soon become the fastest growing ICT field. A significant percentage of businesses and organizations are already marketing their products and services through mobile sites and applications and those who haven’t already, they will have to do so in the near future. This course introduces the students to the basic concepts of mobile computing technologies as well as business principles and practices in order to exploit the full potential of the mobile application market. The students will learn how to imbue a business strategy with capabilities and functionalities offered by the new technological platform.

Learning Outcomes

Students will be able to:

• Learn basic principles of marketing and B2B of mobile computing
• Harness the potential that mobile computing offers to businesses
• Learn about the technologies involved (wireless and mobile communications, web application development basics, security protocols involved, etc.)
• Identify strengths, weaknesses, risks and opportunities and build a successful strategy

Content

• Wireless technologies (Wi-Fi, 3G, 4G etc.)
• Cross-platform mobile web applications
• Native mobile applications that exploit the device’s hardware
• Designing a mobile computing business strategy and evaluating risks and opportunities
• Social networks for collaboration and marketing
• Security and privacy aspects
• Case studies

Reading

Mobile Design and Development: Practical Concepts and Techniques for Creating Mobile Sites and Web Apps, Brian Fling, O'Reilly Media Inc., 2009, ISBN 0596155441, 9780596155445.

Handbook of research in mobile business: technical, methodological, and social perspectives, Bhuvan Unhelkar, 2nd Edition, Vol.1 & 2, Idea Group Inc (IGI), 2009, ISBN1605661562, 9781605661568.

Software Development Methodologies

Aims

The ever growing penetration of computers in everyday life has led to the need to develop a vast number of software programs, which in turn resulted to the emergence of a large number of programming languages, frameworks, SDKs, paradigms and techniques. Being able to write functional and maintainable code entails good knowledge of the most important programming concepts, methodologies and techniques. This is even more necessary now because of the extended fragmentation of the programming market. This course aims to teach students popular principles, techniques, tools and methods used to develop software efficiently. Requirement analysis, UML, Object-oriented analysis, design and programming, usage of Application Programming Interfaces (APIs), software maintenance, project and version management are some of the topics covered through theory and practice.

Learning Outcomes

On completing the course students will be able to:

• Appreciate principles, concepts, and techniques used to develop software efficiently
• Demonstrate how to effectively apply software engineering methods, tools and techniques
• Plan, manage and collaborate on a Software Development group project
• Obtain the knowledge and skills required for effective management of the software maintenance process
• Have developed effective software engineering, management and communication skills

Content

• Software development principles, techniques, methods and tools
• Requirement analysis
• UML
• Object-oriented analysis, design and programming
• Application Programming Interfaces (APIs)
• Software maintenance and evolution
• Project and version management

Reading

D. Avison, G. Fitzgerald, Information Systems Development methodologies, techniques and tools, 4e, McGraw Hill, 2006.

A. Dennis, B.H. Wixom, D. Teagarden, Systems Analysis and Design: An Object-Oriented Approach with UML, Wiley, 4th ed., 2012.

S. Bennett, S .McRobb, R. Farmer, Object-Oriented Systems Analysis and Design, 4th ed., McGraw Hill, 2010,

B. Oestereich, Developing software with UML : object-oriented analysis and design in practice, 2nd ed. Addison Wesley, 2002.

M. O' Docherty, Object-Oriented Analysis & Design. Understanding System Development with UML 2.0, Wiley, 2005.

R.S. Pressman, Software Engineering- A Practitioner's Approach, 8th ed. McGraw Hill, 2014.

I. Sommerville, Software Engineering, 9E ed. Addison-Wesley, 2010.

Information Retrieval

Teaching Hours and Credit Allocation:   30 Hours, 6 Credits

Course Assessment:  Exam & Coursework

Aims

The course covers the basic principles and techniques of information retrieval, which is the process by which a computer system can respond to a query about a given topic. A successful and meaningful response requires efficient data organization and classification, as well as efficient indexing and clustering algorithms. The students will study all aspects of data organization and processing that allow for efficient information retrieval as well as the underlying computational models and tools.

Learning Outcomes

On completing the course, users will be able to:

• Understand key concepts of information retrieval techniques and be able to apply these concepts into practice.
• Apply information retrieval principles to locate relevant information in large collections of data.
• Understand and deploy efficient techniques for the indexing of document objects that are to be retrieved.
• Implement features of retrieval systems for web-based and other search tasks.
• Analyse the performance of retrieval systems.

Content

• Introduction to information retrieval.
• Retrieval Models.
• Dictionaries, term vocabulary and postings lists.
• Index construction and compression.
• Vector space model and classification.
• Support vector machines and machine learning on documents.
• Search systems.
• Latent semantic indexing.
• Link analysis.
• Evaluation.

Reading

• C.D. Manning, P. Raghavan and H. Schütze (2008), Introduction to Information Retrieval, Cambridge University Press.
• S. Büttcher, C.L. A. Clarke and G.V. Cormack (20016), Information Retrieval, Implementing and Evaluating Search Engines, MIT Press.
• Grossman, D.A., Frieder, O. (2004), Information Retrieval, Algorithms and Heuristics, Springer.

Knowledge Management in the Web

Aims

This course examines basic concepts of Knowledge and Knowledge Management, placing emphasis on knowledge encountered in the Web. At first, it briefly deals with the notion of knowledge and its sources, the architecture and Life Cycle of Knowledge Management Systems, how knowledge is captured, how knowledge is formally represented using various formalisms, such as frames, ontologies, deductive and production rules, representation, and finally how knowledge is used for reasoning with the above knowledge representation formalisms. The core theme of the course covers extensively information and knowledge representation and interchange technologies in the Web, such as information representation using XML, information processing using XPath/XSLT, metadata representation using RDF, vocabulary descriptions using RDF Schema, and finally, knowledge representation in the web, using ontologies (OWL), and rules (SWRL, OWL2 RL, RIF). During the course various knowledge management web systems and tools are demonstrated and practiced.

Learning Outcomes

On completing the course participants will:

• Acquire essential skills on Knowledge Management Systems
• Comprehend web Knowledge Management languages and technologies, including XML, XPath, XSLT, RDF, RDFS and OWL
• Experiment with creating their own Knowledge Management systems through a carefully selected series of assignments.

Content

• Basic concepts of Knowledge and Knowledge Management
• Architecture and Life Cycle of Knowledge Management Systems; Knowledge capture
• Knowledge representation and reasoning: Frames, Ontologies, Deductive and Production Rules
• Web Knowledge Management languages and technologies: XML, XPath, XSLT, RDF, RDF Schema and OWL
• Demonstration and practice of various web Knowledge Management systems (XML editors, XPath/XSLT processors, Ontology/Rule editors, Reasoners, Rule engines).

Reading

Antoniou G. and van Harmelen F. (2008), A Semantic Web Primer, 2nd Edition.

Awad E. M., Ghaziri H. M. (2004), Knowledge Management, Prentice Hall.

Gomez-Perez A., Corcho O., Fernandez-Lopez M. (2004), Ontological Engineering: with examples from the areas of Knowledge Management, e-Commerce and the Semantic Web, Springer-Verlag.

Holzner, Steven, XML: a beginner's guide : go beyond the basics with Ajax, XHTML, XPath 2.0, XSLT 2.0, and XQuery, McGraw-Hill, 2009.

Allemang, Dean, Semantic web for the working ontologist: modeling in RDF, RDFS and OWL, Morgan Kaufmann Publishers/Elsevier, 2008.

World Wide Web Consortium (W3C) Site: http://www.w3.org/

Data Mining

Aims

The course covers Knowledge Discovery in Databases (KDD) and Data Mining (DM) as a set of computational tools and technologies, which provide valuable assistance for business analysis and strategic business decision making. This is a hands-on course that provides an understanding of the key methods of data visualization, exploration, classification, prediction, and clustering. Students will learn how to apply various data mining techniques for solving practical problems and how to develop and use simple business analytics systems.

Learning Outcomes

By the end of this course you should be able to

• Organise and efficiently process any knowledge, either given a priori or extracted
• Understand the basic concepts of data mining
• Understand and apply various data mining approaches, including Classification, Clustering and Association Rules.
• Model complex problems
• Develop skills on a broad range of business intelligence problems
• Understand, evaluate and utilise knowledge extracted from large volumes of data.Identify the basic components and special characteristics of a business decision problem and develop a solution.

Content

• Introduction to Knowledge Discovery in Databases (KDD) and Data Mining (DM)
• Classification and Regression
• Clustering
• Association Rules
• Exploratory vs. Confirmatory analysis
• DM Systems, Data pre-processing and EvaluationBusiness use cases

Reading

J. Han and M. Kamber, Data Mining: Concepts and Techniques, 3rd ed., The Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann Publishers, 2011.

I. Witten, E. Frank, and M. Hall, “Data Mining: Practical Machine Learning Tools and Techniques”, 3rd Ed., Morgan Kaufmann, 2011.

J. Ledolter, Data Mining and Business Analytics with R, Wiley, 2013.

P.N. Tan, M. Steinbach, and V. Kumar, “Introduction to Data Mining” Int’l Ed., 1/e, Pearson Higher Education, 2006.

R. Sharda, D. Delen, E. Turban, Decision Support and Business Intelligence Systems Int’l Ed. 10/E, Pearson Higher Education, 2015.

M.H. Dunham, “Data Mining: Introductory and Advanced Topics”, Prentice Hall, 2003.

M.M. Gaber (ed.), Journeys to data mining: experiences from 15 renowned researchers, Springer, 2012

Digital Organisations: eCommerce and eGovernment

Aims

The aim of this course is to broaden and expand knowledge of the concepts and techniques required for the design, operation and control of the modern upcoming e-commerce applications and e-government systems that are massively introduced by western governments to fight bureaucracy. The essential computing background to support such systems is presented, along with the individual requirements for a wide variety of modern life activities that can be performed online.

Learning Outcomes

On completing the course students will:

• Develop knowledge of the information and communication skills to support and develop this type of information systems
• Broaden their knowledge into e-commerce, covering business, marketing, organisational and payment security issues
• Explain the concepts, processes behind developing an e-learning facility
• Understand the technological, ethical, legal and practical requirements of an electronic government information system

Content

• Current and emerging business models
• The use of information and communications technology
• Mobile commerce
• E-marketing and e-business strategy
• E-consumer behaviour and advertisement
• Organisational and managerial challenges in the electronic environment
• E-Payment systems
• E-learning; security issues and the legal environment
• Understanding eGovernment
• eAdministration/G2G
• eCitizens/ eAccountability
• eDemocracy/eParticipation
• eServices/G2C & G2B
• Legislation for eGovernment
• Integrated eGovernment, Group Presentations

Reading

Laudon K., Guercio-Traver C. (2008) E-Commerce 2009: Business, Technology, Society, Prentice Hall, 5th edition.

Turban E., Lee J. K., King D., McKay J., Marshall P., (2008) Electronic Commerce 2008, Prentice Hall. Abramson M., Morin T. (2003) E-Government 2003, Rowman & Littlefield, Lanham, MD.

Heeks R. B. (2006) Implementing and Managing eGovernment: An International Text, Sage Publications, London

Big Data and Cloud Computing

Aims

The big data explosion has led to new computing paradigms, the most prevalent among them being cloud computing. Cloud computing is about vast computing resources on demand, that allow for centralized data storage and online access. Big data is a broad term that includes several concepts and tasks, such as data capture, storage, sharing, management and analysis. This course focuses mostly on the big data storage and management part, rather than the analysis as well as cloud service models, architectures and tools. Students will familiarize with modern big data and cloud technologies, understand the privacy and security concerns and learn about popular big data and cloud computing platforms.

Learning Outcomes

On completing the course students will be able to:

• Develop the knowledge, understanding and skills to work with Big data
• Deploy a structured lifecycle approach to data analytics problems
• Apply appropriate analytic techniques and tools to analyzing big data
• Understand Cloud Computing Concepts and Mechanisms
• Learn the concepts, principles, techniques and methodologies you need to manage cloud services and resources

Content

• Big data concepts, principles and practical applications
• Big data capture, storage, sharing, management and analysis
• Cloud Computing Concepts and Mechanisms
• Cloud Architectures
• Working with Clouds
• Managing Cloud Services and Resources
• Big data and cloud computing platforms

Reading

T. Erl, R. Puttini, Z. Mahmood, Cloud Computing: Concepts, Technology & Architecture, Pearson, 2013.

EMC Education Services (Editor), Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley 2015.

Consulting Project

Credit Allocation:   6 Credits

Course Assessment:  Final deliverable

Aims

The Consulting Project will require students to apply knowledge gained in classroom into practice. Students will tackle real-life problems and challenges facing companies or organisations in order to provide actual business solutions. Following a procedure of specifications/requirements, design and implementation, students will prepare and present their concrete and practical solutions in a final deliverable report.

Learning Outcomes

On completing the course, students will be able to:

• Understand real-world problem faced by companies/firms and propose functional solutions.
• Develop critical thinking and ability to integrate data and information towards the optimal solution.
• Understand the structure, operational mode and challenges of real-world companies.

Content

• Understanding and recording a company’s needs and challenges.
• Project requirements.
• Data analysis, implementation and company feedback.
• Producing a deliverable.