Data Warehousing and Knowledge Discovery technology is emerging as a key technology for enterprises that wish to improve their data analysis, decision support activities, and the automatic extraction of knowledge from data. It provides the common foundation of any analytical research, which is directed towards an empirical evaluation of projects and activities within the context of the emerging virtual enterprises. Typical benefits for decision makers include: statistical reports on empirical data based on advanced analytical techniques, the offline creation of decision support models in the context of e-business, the integration of pre-built models and data into real-time recommendations as to provide dynamically personalized Web applications, and the development of models and scenarios in the context of content billing.

One of the main challenges is the efficient integration and aggregation of huge amounts of data available for analysis. Web logs provide us with behavioural information on visitors of Web sites. Aiming for more than simple traffic analysis, we need to develop models and tools to integrate additional databases such as customer data, content structure data, and data from OLTP systems. The design and implementation of such integrated data sources, ie data Webhouses, is the necessary starting point of any analytical mining technique for the Web.