MSc ACS: Data and Knowledge Management

Year of entry: 2024

Course unit details:
Modelling Data on the Web

Course unit fact file
Unit code COMP60411
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Available as a free choice unit? No


This course unit discusses formalisms to capture, describe, transform and query semi-structured data (in particular XML, different schema languages, XQuery, etc.), and looks at their respective strengths and weakness. It will provide students with the background and skills to work with these and similar formalisms and tools, and with the understanding of relevant features to analyze their strengths and weaknesses.


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.

Learning outcomes

  • 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

Employability skills

Analytical skills
Problem solving
Written communication

Assessment methods

Method Weight
Written exam 80%
Written assignment (inc essay) 20%

Feedback methods

Weekly coursework will be collected via Blackboard, and feedback is provided through the same mechanism.

Study hours

Scheduled activity hours
Lectures 20
Practical classes & workshops 15
Independent study hours
Independent study 115

Teaching staff

Staff member Role
Uli Sattler Unit coordinator

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