Data Modelling
Code
11559
Academic unit
Faculdade de Ciências e Tecnologia
Department
Departamento de Informática
Credits
6.0
Teacher in charge
Carlos Augusto Isaac Piló Viegas Damásio, João Carlos Gomes Moura Pires
Weekly hours
4
Teaching language
Português
Objectives
Knowledge:
- Graph modelling and query languages
- Linked Open Data principles and Semantic Web concepts
- Languages for representing, reasoning and querying in the Semantic Web
- Concepts, architectures and models of a Data Warehouse
- Multidimensional data modelling for OLAP querying.
Application:
- Identify applications requiring graph modelling
- Model a graph database and query it (e.g. Neo4j with Cypher queries)
- Use a triple store and inference engine (e.g. Apache Jena) for querying with SPARQL data in the Semantic Web
- Analyse, design and query multidimensional models.
Soft-Skills
- To explore autonomously the recent bibliography of a topic
- To develop critical reasoning regarding recent technology
- To work in a team
- To orally present a survey on a recent topic
- To review a scientific work
Subject matter
Graph Modelling
Relational, semi-structured and graph data. Data modelling with graphs. Querying graph models. Graph databases. Relationship to NoSQL movement.
2. Semantic Web
Motivation. Linked Open Data. Language and semantics of the Resource Description Framework (RDF) and SPARQL query language. Ontologies in the Semantic Web: RDF Schema and Web Ontology Language (OWL).
3. Online Analytical Processing (OLAP)
Data Warehouses. (Conceptual) multidimensional data models. Typical OLAP operations and OLAP query languages. Metadata. Spatial and temporal dimensions. Interaction in the data analysis process.
4. Exercises and final project
Use of tools (graph database, RDF and OWL API, OLAP and multidimensional)
Bibliography
• Ian Robinson, Jim Webber, and Emil Eifrem. Graph Databases. O''''Reilly Media, Inc, 2013.
• Grigoris Antoniou, Paul Groth, Frank van Harmelen and Rinke Hoekstra . A Semantic Web Primer, 3rd Edition. MIT Press, August 2012.
• The Description Logic Handbook. Theory, Implementation and Applications. Edited by Franz Baader, Diego Calvanese, Deborah McGuinness, Daniele Nardi and Peter Patel-Schneider. Cambridge University Press, June 2010.
• The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling (Third Edition) - Ralph Kimball, Margy Ross. Wiley, 2013.
Evaluation method
Evaluation consists in 2 individual midterms (each worth 25% of the final grade), a final team project (35%), oral presentation and discussion of a colleague’s project (15% of the grade). Each midterm has a minimum grade of 8/20 and the average of the midterms must be at least 10/20, after rounding. Any student is admitted to final exam to substitute any or all of the midterm grades, which combine with the grading of the assignment and oral evaluation to produce the final grade.