1 Brief description of aims and content
The course addresses the aspects needed to realize the vision of the Semantic Web through the use of Intelligent Agents. The traditional World Wide Web is an example of an application of computing on a global scale, thereby creating a Distributed Information Space. In order to be able to categorize, access and process the knowledge contained on the Web, the intelligent agents are required to sift and winnow data and information into knowledge. The course will survey enabling technologies for the semantic web and for linked data, including languages, tools and infrastructures for knowledge representation and rational agents. The role of intelligent agents will be studied in the Semantic Web framework.
2 Learning Outcomes
A. Knowledge and Understanding
At the end of the programme students should be able to demonstrate knowledge and understanding of
- The basic principles of different theoretical models of distributed knowledge-based systems, and assess their applicability to specific knowledge-sharing tasks (A1).
- How the architecture and design of distributed knowledge-based systems interacts with wider social and technological developments; (A3)
- The features, rationale, and advantages of Semantic Web technology; (A10, A14)
B. Cognitive/ Intellectual Skills/ Application of Knowledge
At the end of the programme students should be able to:
- Describe the motivations for, and effectiveness of, inference techniques in the implementation of distributed knowledge-based systems; (B4, B5)
- Analyze the requirements and features of web ontology language (OWL); (B6,B7,B9)
- Validate XML documents using DTDs (Document Type Definitions) and XML Schema; (B3, B7)
C. Communication/ICT/Numeracy/Analytic Techniques/Practical Skills
At the end of the programme students should be able to:
- Critically assess the adequacy of relevant standards (WSDL, RDF, OWL, etc) as a basis for building practical systems; (C3, C12)
- Discuss the methodologies in ontology engineering and research issues in Semantic Web technology; (C1, C2, C6)
- Build and analyze ontologies using an ontology editor; (C2, C5)
- Use Java API to manipulate RDF data model and ontology; (C3, C4, C7)
D. General transferable skills
At the end of the programme students should be able to:
- Use a variety of resources for the purpose of independent study; (D1, D6)
- Work as a member of a team by cooperating with others, negotiating, listening to others in the group, sharing responsibilities/tasks and meeting deadlines; (D8)
- Draft report on the concepts of graph-based RDF model, XML syntax-based RDF model, and RDF Schema. (D7)
3 Indicative Content
i. Introduction: Relationships between Semantic Web Technologies and Intelligent Agents, and between Linked Data and the Semantic Web: an introduction
i. Semantic Web: Technologies, Tools, Applications: Knowledge representation techniques: metadata, taxonomies, thesauri, ontologies, folksonomies; Structure of an ontology and design of conceptual ontologies; Semantic web technologies: RDF, OWL; Semantic search and reasoning; The Open Linked Data initiative; Triple stores, SPARQL; Overview of semantic applications and research trends
ii. Intelligent Agents and Semantics-aware intelligent agents: Intelligent agents: Rational agents; Languages, tools and infrastructures for rational agents
iii. Semantics-aware intelligent agents and web services: The FIPA Ontology Agent proposal; Extending languages for rational agents with ontological belief bases; WADE: merging web services and JADE
4 Learning and Teaching Strategy
A course handbook will be provided in advance and this will contain an in-depth information relating to the course content. This will give an opportunity to the students to prepare the course. The lecture materials will be posted on the web page that will also contain comprehensive web links for further relevant information. The module will be delivered through lectures, tutorial/practice sessions and group discussions. In addition to the taught element, students will be expected to undertake a range of self-directed learning activities.
5 Assessment Strategy
The laboratory or practical’s, tests and examination will assess whether the student has an acceptable level (50%) in the contents of the course. Formative assessment is by means of regular tutorial exercises and end of module assignment. Feedback to students on their solutions and their progress towards learning outcomes is provided during lectures and tutorial classes and off-campus assignments or min-projects. The major component of summative assessment is the written examination at the end of the module. This gives students the opportunity to demonstrate their overall achievement of learning outcomes. It also allows them to give evidence of the higher levels of knowledge and understanding required for above average marks.
6 Assessment Criteria:
For the assignment, criteria will be drawn up appropriate to the topic, based on the learning outcomes.
1. Module description
Developing an information systems security program that adheres to the principle of security as a business enabler, must be the first step in an enterprise’s effort to build an effective security program. This course provides students with an understanding of the threats posed to networked information systems and the knowledge required securing them. This course provides students with a clear understanding of the fundamentals of information systems security required to address the range of issues they will experience in the field. It examines the essential elements of information system security issues and methods in networking systems.
2. Learning Outcomes
A. Knowledge and Understanding
At the end of the programme students should be able to demonstrate knowledge and understanding of
- Fundamental concepts of data encryption /decryption in information systems security
- Fundamental concepts of authentication systems and security systems
- Social impact of computer security in online transactions, e-commerce and e-management
- The principles of design and development including an awareness of standards of practice.
- Information systems threats, vulnerabilities, risks and controls
B. Cognitive/ Intellectual Skills/ Application of Knowledge
At the end of the programme students should be able to:
- Apply technical knowledge to produce a technical risk assessment
- Use principles of encryption /decryption and authentication in the development of solutions to problems in information systems security
- apply known encryption /decryption and authentication algorithms to produce innovative designs of information systems security products
4.Integration of theory and practice within the constraints of a given framework
C. Communication/ICT/Numeracy/Analytic Techniques/Practical Skills
At the end of the programme students should be able to:
- Use competently and safely any information systems security related monitoring instruments
- Specify, plan, manage, conduct and report on information systems security research project
- Analyse, evaluate and interpret data and apply them to the solution of information systems security problems.
- Detection of information systems security attacks and configuration of protective mechanisms e.g. firewalls
D. General transferable skills
At the end of the programme students should be able to:
- Efficiently manage time and resources in maintaining information systems security
- Demonstrate problem solving skills specific to information systems security
- Have the capacity for self-learning in familiar and unfamiliar situations
- Carry out independently a sustained investigation.
3 Indicative Content
General concepts of Information systems security:
Definition and examples of information systems security, CIA trend, The Challenges of Information systems Security, Attacks, threats and vulnerabilities in information systems (Types of attacks, Threats and Assets, Classification of threats, Vulnerabilities and risk analysis), Malicious software and software security (Types of Malicious Software, System corruption & information theft )
Organisation Security:
Organisation security threats, Managing organisation security, Organizational Security Model, Creating and maintaining a user security Policies, Standards, Guidelines, Procedures
Information systems security and Risk Management:
Security Management, Security Administration, Information security risk assessment and Risk analysis, Information Classification, Layers of Responsibility, Security Awareness Training
Conventional and modern Encryption:
Overview of Services, Mechanisms and Attacks, Classical Encryption Techniques-Cipher model, Substitution techniques, Transportation techniques, Rotor machines, Stenography, Block ciphers.Advanced encryption standard-AES cipher, Triple DES, Blowfish, RC5, Traffic Confidentiality.
Public Key Encryption and Authentication:
Fermat’s and Euler’s theorem, Principles of public key cryptosystems, RSA algorithm, Diffie-Hellman key exchange algorithm, Message authentication codes, Hash functions, Digital signatures.
Security Practice:
User Authentication Applications: (Password-Based Authentication, Token-Based Authentication, Remote-user Authentication,Biometric authentication), Kerberos & X.509, Electronic Mail security, IP security- Architecture, Authentication Header, Encapsulating security payloads, Web Security- Secure Socket Layer & Transport Layer Security, Secure Electronic Transaction (SET).
Application Security:
Software and applications security issues, Database Security, Secure systems development, Application development and security, Object-oriented systems and security, Distributed computing and security, Expert systems and security, Mobile and telecommunication security, Patch management
Security Technologies:
Access Control (Access control Principles, Discretionary Access Control, Role-based Access control), Firewalls, Intrusion Detection Systems (IDS)
4 Learning and Teaching Strategy
A course handbook will be provided in advance and this will contain an in-depth information relating to the course content. This will give an opportunity to the students to prepare the course. The lecture materials will be posted on the web page that will also contain comprehensive web links for further relevant information. The module will be delivered through lectures, tutorial/practice sessions and group discussions. In addition to the taught element, students will be expected to undertake a range of self-directed learning activities.
5 Assessment Strategy
The laboratory or practical’s, tests and examination will assess whether the student has an acceptable level (50%) in the contents of the course. Formative assessment is by means of regular tutorial exercises and end of module assignment. Feedback to students on their solutions and their progress towards learning outcomes is provided during lectures and tutorial classes and off-campus assignments or min-projects. The major component of summative assessment is the written examination at the end of the module. This gives students the opportunity to demonstrate their overall achievement of learning outcomes. It also allows them to give evidence of the higher levels of knowledge and understanding required for above average marks.
6 Assessment Criteria:
For the assignment, criteria will be drawn up appropriate to the topic, based on the learning outcomes.
1. Course description
This advanced module introduces the learners to the principles and practice to gain knowledge of algorithms and methods of Data Mining and Knowledge Discovery. It aims to cover each stage of the Data Mining and Knowledge Discovery, including preliminary data exploration, data cleansing, pre-processing and the various data analysis tasks that fall under the heading of data mining.
The ongoing rapid growth of online data due to the Internet and the widespread use of large scale databases have created an immense need for Data Mining and Knowledge Discovery methodologies. The challenge of extracting knowledge from data draws upon research in statistics, databases, pattern recognition, machine learning, data visualization, optimization, and high-performance computing, to deliver advanced business intelligence and web discovery solutions.
2 Learning Outcomes
A. Knowledge and Understanding
At the end of the programme students should be able to demonstrate knowledge and understanding of
- Principles applied in the development of Data Mining and Knowledge Discovery.
- Current standards of practice used in developing Data Mining for systems.
- Use of software quality metrics and benchmarks in the development computer algorithms based on Data Mining and Knowledge Discovery.
B. Cognitive/ Intellectual Skills/ Application of Knowledge
At the end of the programme students should be able to:
1. Apply data mining software engineering standards, metrics and bench marks to produce innovative designs of computer, software data mining systems and components.
2. Critically assess data pre-processing and exploration techniques to specified Data Mining and Knowledge Discovery work done by others.
C. Communication/ICT/Numeracy/Analytic Techniques/Practical Skills
At the end of the programme students should be able to:
1. Specify data mining models prepare relevant technical documents.
2. Prepare technical reports and deliver technical presentations on Knowledge sharing at an advanced level.
3. Analyse, evaluate and interpret existing data mining algorithms and apply them to the solution of practical real problems.
4. Use appropriate software tools and packages appropriate to Data Mining and Knowledge Discovery analysis and research.
D. General transferable skills
At the end of the programme students should be able to:
1. Involve in research and development on Data Mining and Knowledge Discovery.
2. Carry out independently a sustained investigation and research in Knowledge
Discovery.
3. Draft &Evaluate, select and interpret patterns and knowledge discovered as a result of applying Knowledge Discoverydocuments effectively (written, verbal, drafting, sketching etc.)
3 Indicative Content
- Data Mining Overview
Background to data mining; Understanding the differences between data, information and knowledge; Objectives of data mining; Knowledge Discovery in databases; Data Mining Applications - Marketing, Finance, Banking, Fraud detection, Manufacturing, Telecommunications, discovering knowledge on the Internet. Current state of data mining.
- Principles of Data Mining
Data mining process/approaches e.g. Crisp-DM, SEMMA; Categories of data mining problems; Evaluation and interpretation of output patterns.
- Data Mining Model Functions
Investigate some of the following supervised and unsupervised techniques: classification, clustering, dependency modelling, sequence modelling, data summarisation, and change and deviation analysis/anomaly detection. Matching the model function(s) to the data mining problem at hand.
- Data Mining Model Representations
Using a data mining tool to mine the data, investigate some of the following data mining representations: decision trees and rules; neural networks; machine learning; case-based reasoning; data visualisation: clustering, hierarchies, and self-organised networks, geo-positioning/landscaping.
- Interpretation & Refinement
Interpreting patterns, removing redundant patterns, translating patterns, refining the data mining process based on knowledge learned. Testing and validating the accuracy of the models using various techniques e.g. simple split, k-fold cross-validation, bootstrapping.
- Data Mining Software
Using data mining and forecasting software (e.g. SAS, RapidMiner, R, SPSS) to manipulate algorithms, build and test models for a variety of data sets.
4 Learning and Teaching Strategy
A course handbook will be provided in advance and this will contain in depth information relating to the course content. This will give an opportunity to the students to prepare the course. The lecture materials will be posted on the web page that will also contain comprehensive web links for further relevant information. The module will be delivered through lectures, tutorial/practice sessions and group discussions. In addition to the taught element, students will be expected to undertake a range of self-directed learning activities.
Students should be able to compare and contrast the differences between the major data mining tasks, in terms of their assumptions, requirement for a specific kind of data, and the different kinds of knowledge discovered by algorithms performing different kinds of task.
The students should also be able to identify which data mining task and which algorithm is the most appropriate for a given data mining project, taking into account both the nature of the data to be mined and the goals of the user of the discovered knowledge
5 Assessment Strategy
In-Course and End of Module assessment add up to 100% and includes:
- Fundamental concepts-seminar, oral examination
- Basis of project management –seminar, oral examination
- Project management –seminar, oral examination
- Management of development project-seminar, oral examination
- Project-group assignment, written report and seminar
As this is a Theoretical and Practical module: The Final assessment shall include 50% of continuous and 50% of End of Module assessment.
The assessments shall be made 50% each for practical and theoretical aspects.
For Example:
one quiz (5%), one/two practical assignment (10%), one mini project for presentation (10%), one tutorial session (5%), short practical test (10%) and a short written test (10%) followed by final assessment (50%) of End of Module Examination divided equally into practical viva-voce and theoretical examination.
6 Assessment Criteria:
For the assignment, criteria will be drawn up appropriate to the topic, based on the learning outcomes.