Der Idee des Data Warehousing liegt zugrunde, dass transaktionsorientierte Systeme (OLTP) und Analysesysteme (OLAP) zwar auf den gleichen Unternehmensdaten operieren, sie jedoch fundamental verschiedene Anforderungen an die Zugreifbarkeit dieser Daten haben. Transaktionen im OLTP-Bereich ändern nur wenige Daten und müssen mit hohem Durchsatz ausgeführt werden. OLAP-Analysen hingegen greifen auf...

Data warehousing has largely developed with little or no reference to ObjectOriented Software Engineering (OOSE) [1]. This is consistent with (a) its development out of two-tier client/server relational database methodology, and (b) its character as a kind of high-level systems integration, rather than software development, activity. Data Warehousing assembles components, rather than creating t...

In a data warehousing process, mastering the data preparation phase allows substantial gains in terms of time and performance when performing multidimensional analysis or using data mining algorithms. Furthermore, a data warehouse can require external data. The web is a prevalent data source in this context. In this paper, we propose a modeling process for integrating diverse and heterogeneous ...

Preface Complex, spatial and spatio-temporal data arise in a plethora of modern database and data mining applications and complex information systems. Complex, spatial and spatio-temporal data require more and more for effective and efficient models, algorithms and techniques for representing, managing, querying , indexing and discovering useful knowledge beyond such kind of data. A successful ...

Rapidly improving computing and networking technology enables enterprises to collect data from virtually all its business units. The main challenge today is to extract useful information from an overwhelmingly large amount of raw data. To support complex analysis queries, data warehouses were introduced. They manage data, which is extracted from the different operational databases and from exte...

Data Warehousing is now a well-established part of the business and scientific worlds. However, up until recently, data warehouses were restricted to modeling essentially numerical data – examples being sales figures in the business arena (in say Wal-Mart’s data warehouse (Westerman, 2000)) and astronomical data (for example SKICAT) in scientific research, with textual data providing a descript...