- Review principle abstractions, methods and techniques for the management of large and complex data sets (“Big Data”).
- Develop an understanding of the foundations and tools of the Semantic Web.
- Enable students to appreciate critically a range of data integration solutions.
Course Description: Link to Official Course Descriptor.
Pre-requisites: Academic knowledge of fundamentals of databases and logic..
Location: Dubai, Edinburgh.
- Complex data sets:
- RDF, triple stores, SPARQL, Big Data vs Smart Data vs Broad Data, NoSQL, indexing data.
- Semantic Web Foundations:
- RDFS, OWL, Ontologies, Reasoning, Protégé.
- Data Integration:
- Linked Data, Mash-ups, Ontology mapping, Data Provenance.
Learning Outcomes: Subject Mastery
Understanding, Knowledge and Cognitive Skills Scholarship, Enquiry and Research (Research-Informed Learning)
- A detailed and integrated knowledge and understanding of a range of data representation and data management techniques for big data sets.
- Critical understanding of the role of semantic web technologies in the context of big data management.
- Extensive knowledge of the mechanisms that underlie data integration techniques.
- To be able to demonstrate a critical understanding of appropriateness and effectiveness of different techniques.
Learning Outcomes: Personal Abilities
Industrial, Commercial & Professional Practice Autonomy, Accountability & Working with Others Communication, Numeracy & ICT
- Conceptualize and define new abstract problems within the context of complex data sets.
- Deal with complex issues and make informed judgements about the applicability of semantic web solutions to big data questions.
- Exercise substantial autonomy, initiative and creativity in the application of data integtration techniques.
- Demonstrate critical reflection. (PDP)
- Communicate with professional level peers, senior colleagues and specialists. (PDP)
Assessment: Examination: (weighting – 70%) Coursework: (weighting – 30%)
Re-assessment: Examination: (weighting – 100%)
SCQF Level: 11.