This course address data modelling and query languages for semantically enriched data, big data and/or open data applications in the Web. The core of the course will cover graph models, in particular those allowing the representation of relationships between resources in the Web. Other alternative models like documentg and column stores are also discussed and compared to. Besides representing interconnections among data, it is necessary to model and explore multidimensional data for online analytical processing, in particular for representing time and space dimensions for understanding how data evolves and moves. The representation of information in novel (spatial)temporal databases will be addressed and applied.

Knowledge:

• NoSQL models
  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.
• Use a temporal database

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 reviewa scientific work

1. NoSQL data models

Alternative models for storing big volumes ofdata. Column, document and graph models. Relational, semi-structured and graph data. Data modelling with graphs. Querying graph models. Graph databases. Relationship to NoSQL movement and key-value stores.

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, temporal databases, RDF and OWL API, OLAP and multidimensional)

• 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.

• Guy Harrison. Next Generation Databases: NoSQL, NewSQL and Big Data. Apress, 2015.
ISBN:978-1484213308.

• Dan Sullivan. NoSQL for Mere Mortals. Addison-Wesley, 2015.
ISBN:978-0134023212

To take this curricular unit you should first get approval in Database Systems.