F20BD Big Data Management

Dr Alasdair GrayDr Phil BartieAssoc Prof. Hadj Batatia

Course co-ordinator(s): Dr Alasdair Gray (Edinburgh), Dr Phil Bartie (Edinburgh), Assoc Prof. Hadj Batatia (Dubai).

Aims:

  • 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.

Detailed Information

Course Description: Link to Official Course Descriptor.

Pre-requisites: Academic knowledge of fundamentals of databases and logic..

Location: Dubai, Edinburgh, Malaysia.

Semester: 2.

Syllabus:

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)

  • 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.
  • 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.
  • Make informed judgements about the applicability of semantic web solutions to big data questions.
  • Exercise 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 Methods: Due to covid, assessment methods for Academic Year 2021/22 may vary from those noted on the official course descriptor. Please see:
- Maths (F1) Course Weightings 2021/22
- Computer Science (F2) Course Weightings 2021/22
- AMS (F7) Course Weightings 2021/22

SCQF Level: 10.

Credits: 15.