The management of an enterprise requires a comprehensive view of all aspects of
a company, thus it requires access to all possible data of interest stored in multiple
subsystems. However, an analysis of data stored in distributed, heterogeneous, and
autonomous subsystems is likely to be difficult, slow, and inefficient. Therefore, the
ability to integrate information from multiple data sources is crucial for today??™s business.
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Data.Warehouse.and.OLAP
One of the most important approaches to the integration of data sources is based
on a data warehouse architecture. In this architecture, data coming from multiple
external data sources (EDSs) are extracted, filtered, merged, and stored in a central
repository, called a data warehouse (DW). Data are also enriched by historical and
summary information. From a technological point of view, a data warehouse is a
huge database from several hundred GB to several dozens of TB. Thanks to this
architecture, users operate on a local, homogeneous, and centralized data repository
that reduces access time to data. Moreover, a data warehouse is independent of EDSs
that may be temporarily unavailable. However, a data warehouse has to be kept up
to date with respect to the content of EDSs, by being periodically refreshed.
The content of a DW is analyzed by the so called online analytical processing
(OLAP) applications for the purpose of discovering trends, patterns of behavior, and
anomalies as well as for finding hidden dependencies between data.
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