ver the last few years data warehousing, data mining and analytical online processing emerged to form a new paradigm for the storage and analysis of data in business environments and increasingly in scientific environments. With the use of these technologies in a wide variety of application, the necessity for highly scalable and fast data management tools capable of handling multiple terabytes ...

There are a good number of introductory-level textbooks on data warehousing and OLAP technology, including Kimball and Ross [KR02], Imhoff, Galemmo and Geiger [IGG03], Inmon [Inm96], Berson and Smith [BS97], and Thomsen [Tho97]. Chaudhuri and Dayal [CD97] provide a general overview of data warehousing and OLAP technology. A set of research papers on materialized views and data warehouse impleme...

Data warehousing and OLAP applications must nowadays handle complex data that are not only numerical or symbolic. The XML language is well-suited to logically and physically represent complex data. However, its usage induces new theoretical and practical challenges at the modeling, storage and analysis levels; and a new trend toward XML warehousing has been emerging for a couple of years. Unfor...

Database optimization is one of the major concerns and the most important factor that needs to be considered when designing a very large database system. Database optimization and evaluation are usually concentrated on the query language and the organization of data. The recent advances in technologies of data-warehousing architecture, online analytical processing (OLAP), and data mining demand...

79 Using a common set of attributes to determine which methodology to use in a particular data warehousing project. have experienced explosive growth in the last few years, and data warehousing has played a major role in the integration process. A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data that supports managerial decision making [4]. Data...

Die Bedeutung von Geschäftsmodellen des collaborative Business ist gewachsen. Kollaboratives Data Warehousing ermöglicht es den partizipierenden Unternehmen, sich in einem steigenden Wettbewerb strategische Wettbewerbsvorteile zu sichern, und sich damit in den stark umkämpften Kundenmärkten zu behaupten. Vor diesem Hintergrund werden Ansätze zum kollaborativen Data Warehousing vermehrt an Bedeu...

Data warehouse systems are widely accepted as a new middleware layer between transactional applications and decision support applications, thereby decoupling systems tailored to an efficient handling of business transactions from systems tailored to an efficient support of business decisions. For companies maintaining direct contact with large numbers of customers, however, a growing number of ...

Managers of large data warehousing projects often put project failure down to organizational resistance. Technical requirements are usually not considered to be crucial for project success. However, the majority of the scientific work on data warehousing concentrates on technical aspects. As a consequence, a comprehensive framework or method for the introduction of a data warehouse is still mis...

Data warehouse systems are widely accepted as a new middleware layer between transactional applications and decision support applications, thereby decoupling systems tailored to an efficient handling of business transactions from systems tailored to an efficient support of business decisions. For companies maintaining direct contact with large numbers of customers, however, a growing number of ...