Introduction - Data Warehouse Systems: Design and Implementation - page 10

reasons, in this topic we decided to address mature technologies and provide
brief comments on those topics in Chap. 15 .
1.4 Review Questions
1.1 Why are traditional databases called operational or transactional? Why
are these databases inappropriate for data analysis?
1.2 Discuss four main characteristics of data warehouses.
1.3 Describe the different components of a multidimensional model, that
is, facts, measures, dimensions, and hierarchies.
1.4 What is the purpose of online analytical processing (OLAP) systems
and how are they related to data warehouses?
1.5 Specify the different steps used for designing a database. What are the
specific concerns addressed in each of these phases?
1.6 Explain the advantages of using a conceptual model when designing a
data warehouse.
1.7 What is the difference between the star and the snowflake schemas?
1.8 Specify several techniques that can be used for improving performance
in data warehouse systems.
1.9 What is the extraction, transformation, and loading (ETL) process?
1.10 What languages can be used for querying data warehouses?
1.11 Describe what is meant by the term data analytics . Give examples of
techniques that are used for exploiting the content of data warehouses.
1.12 Why do we need a method for data warehouse design?
1.13 What is spatial data? What is spatiotemporal data? Give examples of
applications for which such kinds of data are important.
1.14 Explain the differences between spatial databases and spatial data
warehouses.
1.15 What is big data and how is it related to data warehousing? Give
examples of technologies that are used in this context.
1.16 Describe why it is necessary to take into account web data in the context
of data warehousing. Motivate your answer by elaborating an example
application scenario.