Data Warehouse Concepts - Data Warehouse Systems: Design and Implementation - page 77

language for exchanging multidimensional data between client applications
and OLAP servers. Further, MDX (MultiDimensional eXpressions) is a query
language for OLAP databases. As it is supported by a number of OLAP
vendors, MDX became a de facto standard for querying OLAP systems. The
SQL standard has also been extended for providing analytical capabilities;
this extension is referred to as SQL/OLAP. In Chap. 6 , we present a detailed
study of both MDX and SQL/OLAP.
3.4.4 Front-End Tier
The front-end tier in Fig. 3.5 contains client tools that allow users to
exploit the contents of the data warehouse. Typical client tools include the
following:
￿ OLAP tools allow interactive exploration and manipulation of the
warehouse data. They facilitate the formulation of complex queries that
may involve large amounts of data. These queries are called ad hoc
queries , since the system has no prior knowledge about them.
￿ Reporting tools enable the production, delivery, and management of
reports, which can be paper-based reports or interactive, web-based
reports. Reports use predefined queries , that is, queries asking for
specific information in a specific format that are performed on a regular
basis. Modern reporting techniques include key performance indicators and
dashboards.
￿ Statistical tools are used to analyze and visualize the cube data using
statistical methods.
￿ Data mining tools allow users to analyze data in order to discover
valuable knowledge such as patterns and trends; they also allow predictions
to be made on the basis of current data.
In Chap. 9 , we show some of the tools used to exploit the data warehouse,
like data mining tools, key performance indicators, and dashboards.
3.4.5 Variations of the Architecture
Some of the components in Fig. 3.5 can be missing in a real environment.
In some situations, there is only an enterprise data warehouse without data
marts, or alternatively, an enterprise data warehouse does not exist. Building
an enterprise data warehouse is a complex task that is very costly in time
and resources. In contrast, a data mart is typically easier to build than an
enterprise warehouse. However, this advantage may turn into a problem when