F21BD - Big Data Management

Course leader(s):

Albert Georg Burger (Edinburgh, January)
Radu-Casian Mihailescu (Dubai, January)
Drishty Sobnath (Dubai, January)
To be announced (Malaysia, January)

Aims

Syllabus

1. Web of data (1.1 Open data, Linked Open data , 1.2 Five-star framework, 1.3 Knowledge graphs)

2. Semantic web technologies (2.1 IRI, 2.2 RDF, 2.3 RDFS, SKOS, 2.4 OWL, 2.5 Serialisation: RDF/XML, Turtle, Trig, JSON-LD, RDF-a)

3. Ontology modelling (3.1 Class properties, 3.2 Property restrictions, 3.3 Reification, 3.4 Named graphs, 3.5 Reasoning, 3.6 6. Alignment, 3.7 7. Validation and Evaluation)

4. RDF storage (4.1 RDF triple stores, 4.2 SPARQL)

5. Big data and relational databases (5.1 Characteristics of big data, 5.2 ACID properties, 5.3 Distributed database systems, 5.4 CAP theorem)

6. NoSQL databases (6.1 BASE principles, 6.2 PACELEC theorem, 6.3 noSQL characteristics)

7. NoSQL techniques (7.1 Partitioning and replication, 7.2 Eventual consistency, 7.3 Consistent hashing, 7.4 Object versioning and vector clock, 7.5 Quorum and conflict resolution, 7.6 Merkel trees and anti-entropy, 7.7 Gossip protocol)

8. NoSQL data models (8.1 Key-value, 8.2 Document, 8.3 Wide-column, 8.4 Graph, 8.5 NewSQL)

9. Data Integration (9.1 Data warehousing, 9.2 Virtual data integration, 9.3 Semantic data integration, 9.4 Data Wrangling, 9.5 Data mashing)

Learning outcomes

By the end of the course, students should be able to do the following:

implement a range of data representation and data management techniques for big data sets.
describe the role of semantic web technologies in the context of big data management.
apply mechanisms that underlie data integration techniques.
appraise the appropriateness and effectiveness of different techniques including semantic technologies applied to big data management, supported by research.
conceptualize new abstract problems within the context of complex data sets.

Further details

Curriculum explorer: Click here

SCQF Level: 11

Credits: 15