Data Warehouses and the Semantic Web - Data Warehouse Systems: Design and Implementation - page 564

created from the requirements and the knowledge that can be inferred from
the ontologies. This schema is then semiautomatically populated from the
ontology.

14.8 Review Questions

14.1 What are the two main approaches to perform OLAP analysis with
semantic web data?
14.2 Briefly describe RDF and RDFS and their main constructs.
14.3 Give an example of the RDF/XML and Turtle serializations of RDF
data.
14.4 What is SPARQL? How does its semantics differ from the one of SQL?
14.5 Give an example of a SPARQL query, describe its elements, and
discuss how it will be evaluated.
14.6 Explain the two standard approaches to represent relational data in
RDF. How do they differ from each other?
14.7 How can we represent multidimensional data in RDF?
14.8 Briefly explain the data cube vocabulary QB.
14.9 How can hierarchies be represented in QB?
14.10 Is it possible to perform a roll-up operation on data represented in
QB?
14.11 How does the QB4OLAP vocabulary overcome the limitations above?
14.12 Analyze and discuss the implementation of roll-up in QB4OLAP.
14.13 Explain how to perform OLAP queries in SPARQL.

14.9 Exercises

14.1 Given the Northwind data cube, show the QB representation of the
Sales fact. Provide at least two observations.
14.2 Given the Northwind data cube, show the QB4OLAP representation
of the dimension Customer .
14.3 Do the same as Ex. 14.2 for the dimension Employee .
14.4 Write the R2RML mapping that represents the Northwind data
warehouse using the QB4OLAP vocabulary.
14.5 Show the SPARQL query implementing the operation

ROLLUP(Northwind, Product → Category, SUM(SalesAmount)).

14.6 Show the SPARQL query implementing the operation

SLICE(Northwind, Customer, City= ' Paris ' ).