Page 267 - DCAP603_DATAWARE_HOUSING_AND_DATAMINING

Unit 14: Quality Driven Data Warehouse Design

notes
figure 14.3: from transaction processing to analytic processing

Vendors agree that data warehouses cannot be off-the-shelf products but must be designed and
optimized with great attention to the customer situation. Traditional database design techniques
do not apply since they cannot deal with DW-specific issues such as data source selection, temporal
and aggregated data, and controlled redundancy management. Since the wide variety of product
and vendor strategies prevents a low-level solution to these design problems at acceptable costs,
only an enrichment of metadata services linking heterogeneous implementations is a promising
solution. But this requires research in the foundations of data warehouse quality.
The goal of the DWQ project is to develop a semantic foundation that will allow the designers
of data warehouses to link the choice of deeper models, richer data structures and rigorous
implementation techniques to quality-of-service factors in a systematic manner, thus improving
the design, the operation, and most importantly the evolution of data warehouse applications.
DWQ’s research objectives address three critical domains where quality factors are of central
importance:

1. Enrich the semantics of meta databases with formal models of information quality to enable
adaptive and quantitative design optimization of data warehouses;
2. Enrich the semantics of information resource models to enable more incremental change
propagation and conflict resolution;
3. Enrich the semantics of data warehouse schema models to enable designers and query
optimizers to take explicit advantage of the temporal, spatial and aggregate nature of DW
data.
The results will be delivered in the form of publications and supported by a suite of protoptype
modules to achieve the following practical objectives:
1. Validating their individual usefulness by linking them with related methods and tools
of Software AG, a leading European vendor of DW solutions. The research goal is to
demonstrate progress over commercial state-of-the-art, and to give members of the
industrial steering committee a competitive advantage by early access to results
2. Demonstrating the interaction of the different contributions in the context of case studies in
telecommunications and and environmental protection.
Linking Data Warehousing and Data Quality. DWQ provides assistance to DW designers by
linking the main components of a DW reference architecture to a formal model of data quality,
as shown in Figure 14.4.

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