The Data Warehouse Quality project aims to improve the quality of data warehouse design and operation through the systematic enrichment of the semantic foundations of data warehousing. Logic-based knowledge representation and reasoning techniques were developed to control accuracy, consistency, and completeness via advanced conceptual modeling techniques for source integration, data reconciliation, and multi-dimensional aggregation. This is complemented by quantitative optimization techniques for view materialization, optimizing timeliness and responsiveness without losing the semantic advantages from the conceptual approach. At the operational level, query rewriting and materialization refreshment algorithms exploit the knowledge developed at design time.

The DWQ project develops techniques and tools to support the rigorous design and operation of data warehouses. It uses well-defined data quality factors and a rich semantic approach in bringing together enabling technologies. The potential of such an approach has been demonstrated already by successful commercial usage of the ConceptBase metamodeling tool developed in the COMPULOG project.

Data warehouse development time for a given level of quality will be significantly reduced and adaptation to changing user demands will be facilitated. There is a high demand for design tools for distributed databases that is not satisfied by current products. DWQ has the potential to satisfy this demand for an increasingly important market segment. In addition, a host of quality-enhanced query and update services can be derived from DWQ results. Prototypes of specific models and tools developed in the project will be experimented with in various industry and public administration settings, in order to gain...