MSc ACS: Artificial Intelligence / Course details
Year of entry: 2022
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Course unit details:
Modelling Data on the Web
|Unit level||FHEQ level 7 – master's degree or fourth year of an integrated master's degree|
|Teaching period(s)||Semester 1|
|Offered by||Department of Computer Science|
|Available as a free choice unit?||Yes|
This course unit aims to give students a good understanding of the core concepts of data modelling, and will give them some familiarity with various formalisms, APIs, and languages that have been developed for modelling data on the web, as well as various design and representation issues that arise. Students will learn how to compare different data modelling formalisms, and how to design or analyse a data management system, i.e., whether it makes good use of the features provided by the formalisms used, and whether it fits its purpose.
Laboratory sessions will ground the abstract notions on practical cases and tools.
Have an understanding of the foundations of various forms of (semi-/un-)structured data and their formalisms.
Have an understanding of the four fundamental concepts (core data models, schema languages, query languages, and update mechanisms), their relevant properties, and be able to analyse their use in a given data management system.
Are able to use the basic range of techniques for representing, modelling, and querying (semi- or un-)structured data, and be able to use tools developed for them.
Have an understanding of various formalisms developed for the fundamental concepts for (semi- or un-)structured data (XML, JSON, RDF, XML Schema, XQuery, SPARQL, etc), and be able to use them to model, describe, and query data.
Be able to discuss trade-offs between various formalisms, and between different data models.
• Introduction: four fundamental concepts of data modelling, i.e., core data models, schema languages, query languages, and update mechanisms
• Tree data and formalisms (XML, JSON)
• Schema Languages for tree data (DTDs, XML Schema, JSON Schema, and more)
• Query Languages for tree data (XPath, XQuery)
• APIs for tree data (DOM, SAX, ...)
• Updating tree data and robustness
• Graph data and formalisms (RDF, GraphML)
• Schema Languages for graph data (RDFS)
• Query Languages for graph data (SPARQL)
• APIs for graph data
• Updating graph data and robustness
- Analytical skills
- Problem solving
- Written communication
|Written assignment (inc essay)||70%|
Weekly coursework will be collected via Blackboard, and feedback is provided through the same mechanism.
|Scheduled activity hours|
|Practical classes & workshops||15|
|Independent study hours|
|Ulrike Sattler||Unit coordinator|
Links to course unit teaching materials can be found on the Department of Computer Science website for current students.