The META Group??™s (currently Gartner)
survey estimates that the OLAP market will be worth almost $10 billion in 2008
(EDWMarket, 2004). For these reasons, it is important to understand the core technological
issues and challenges in the field of DW and OLAP.
x
Technological.and. Research. Challenges
The size of a DW, high complexity of OLAP queries as well as the heterogeneous
nature of integrated data pose serious research and technological challenges. Intensive
research is conducted in several fields, that include, among others: schema
design methodology and implementation models, data loading and DW refreshing
techniques, efficient query processing, metadata management, and data quality issues
(cf. Nguyen, Tjoa, & Trujillo, 2005).
A DW is designed for quick data retrieval by ad-hoc queries. These queries often
compute aggregates based on other aggregates by rolling them up or they analyze
details by drilling the aggregates down. Moreover, data are analyzed in the context
of other data, for example, the monthly sales of products by particular shops. In order
to support such kinds of analytical queries, a DW typically uses a multidimensional
data model (Gyssens & Lakshmanan, 1997). In this model, facts representing elementary
data being the subject of analysis are organized in n-dimensional spaces,
called data cubes.
An n-dimensional data cube can be implemented either in MOLAP servers or in
ROLAP servers.
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