Smart Data Management

Smart Data Management covers traditional database systems and emerging Web-scale knowledge graph technologies. Students begin by mastering relational database design, normalization, SQL querying, and practical data modeling, applying these foundations to a focused domain scenario. Building on this experience, the course then expands into Semantic Web approaches, introducing RDF, ontologies, and SPARQL for constructing interoperable, machine-readable data ecosystems. The use of Linked Data principles, enables connections across datasets and integrates insights from modern knowledge graph practices.

• Explain key concepts of data modelling, databases and linked data technologies standards and recommendations and their use in forming a Web of Data

• Interpret data model diagrams using different notations (E-R diagrams)

• Design and implement a Database

• Use Linked Data technologies for retrieving information in the Semantic Web (i.e. use of SPARQL)

• Use of existing vocabularies for annotating data and endpoints for finding data for a particular domain

• Develop ontologies to represent data for a particular domain

• Written test with closed questions (20%) Data management theory
• Written test with open and closed questions (20%) Linked data theory
• Assignment poster (20%) Combined grade of the two casework posters and presentations
• Assignment other (20%) Casework Data Management assignment
• Assignment other (20%) Casework Linked Data Assignment

Basic computer skills

• The book for the first part of the course is: Richard T. Watson. Data Management, Databases and Organizations. 6th ed. (e-book). For the second part of the course: The course Brightspace offers further support material.