TABLE OF CONTENTS The DWQ project research is based on the framework shown in Figure 7.2. The project focuses its research effort on the issues described in the rest of this section and investigates the relationships between these issues and data quality factors.
Although many data warehouses have been built, there is no common methodology that supports database system administrators in designing and evolving a data warehouse. The problem with architecture models for data warehouses is that practice has preceded research and continues to do so. The sheer amount of data to be manipulated under tight constraints forces the industry to quickly introduce new implementation methods. For example, caching and logging are used to recover from failures in the data transfer between data warehouse components. Such methods are typically implementation-oriented. Consequently, the task of providing an abstract model of the architecture becomes more difficult. The following questions come up:
What kinds of components are used in a data warehouse environment?
What architectures are used for data warehouses?
How do the components exchange (meta-) data?
What is the role of metadata in a data warehouse?
How can quality factors and architecture be combined?
The strategy for dealing with this dynamic situation in data warehouse architectures is to use a representation language whose features can be adapted to an evolving understanding of what an architecture for data warehouses is about. The framework uses meta-models for encoding the key concepts and their semantics. Such meta-models can be updated...
