Page 269 - DCAP603_DATAWARE_HOUSING_AND_DATAMINING

Unit 14: Quality Driven Data Warehouse Design

recent techniques from meta modeling and distributed software engineering will help to notes
identify and maintain inter-view relationships. This requires deep integration of AI and
database techniques, building on experiences in view integration and maintenance, and
meta-level integration of heterogeneous databases.

Task Discuss the differences between no coupling, loose coupling, semi tight
coupling and tight coupling architectures for the integration of a data mining system with
a Database or data warehouse system.

14.2.1 expected results and innovations

The DWQ project will produce semantic foundations for DW design and evolution linked
explicitly to formal quality models, as indicated in the middle of Figure 14.5. These semantic
foundations will be made accessible by embedding them in methodological advice and prototype
tools. Their usefulness will be validated in the context of Software AG’s methodology and tool
suite and a number of sample applications.

figure 14.5: the DWQ framework

After developing an initial reference model jointly with the industrial committee, the results will
be delivered in two stages to enable effective project control and coherence. The first group of
results will develop enriched formal meta models for describing the static architecture of a DW
and demonstrate how these enriched foundations are used in DW operation. The corresponding
tools include architecture modeling facilities including features for addressing DW-specific
issues such as resolution of multiple sources and management of partially aggregated multi-
dimensional data, as well as semantics-based methods for query optimization and incremental
update propagation.

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