Data warehouse systems offer novel techniques and services for data-rich applications both from a data modeling and a data processing point of view. In this paper, we investigate how well state-of-the-art concepts fulfill the requirements of real-world data warehouse applications. Referring to a concrete example from the market research area, key problems with current approaches are identified ...

Healthcare organizations practicing evidence-based medicine strive to unite their data resources in order to achieve a wider knowledge base for sophisticated research and matured decision support service. The central point of such an integrated system is a data warehouse, to which all participants have access. In order to insure a better protection of highly sensitive healthcare data, the wareh...

Integration is one of the most important aspects of a Data Warehouse. When data passes from the sources of the application-oriented operational environment to the Data Warehouse, possible inconsistencies and redundancies should be resolved, so that the warehouse is able to provide an integrated and reconciled view of data of the organization. We describe a novel approach to data integration and...

Metadata is essential for understanding information stored in data warehouses. It helps increase levels of adoption and usage of data warehouse data by knowledge workers and decision makers. A metadata model is important to the implementation of a data warehouse; the lack of a metadata model can lead to quality concerns about the data warehouse. A highly successful data warehouse implementation...

The goal of a data warehouse is to integrate applications at the data level. Data warehouse architectures are developing in response to our increasing data and information requirements. The traditional notion of data warehouses is evolving into a federated warehouse augmented by a set of processes and services to support integrated and consistent access to heterogeneous, decentralized warehouse...

Data warehouse modeling is a complex task, which involves knowledge of business processes of the domain of discourse, understanding the structural and behavioral system’s conceptual model, and familiarity with data warehouse technologies. The suitability of current data warehouse modeling methods for large-scale systems is questionable, as they require multiple manual actions to discover measur...

Most work on data warehousing addresses aspects related to the internal operation of a data warehouse server, such as selection of views to materialise, maintenance of aggregate views and performance of OLAP queries. Issues related to data warehouse maintenance, i.e. how changes to autonomous sources should be detected and propagated to a warehouse, have been addressed in a fragmented manner. A...

Due to shifting of healthcare data in electronic form, huge volumes of data have been accumulated in organizations related to medical claim processing domain. This paper presents design of a data warehouse implemented as integral part of a rule based expert system. The system is being used for scrubbing of medical claims’ data. Approximate star schema has been used for designing the proposed da...

Information requirements analysis for data warehouse systems differs significantly from requirements analysis for conventional information systems. Existing data warehouse specific approaches are reviewed. A comprehensive methodology that supports the entire process of determining information requirements of data warehouse users, matching information requirements with actual information supply,...

The pre-build development of warehouse systems leads from a specific customer request to a specific customer quotation. This involves a process of configuring a warehouse system using a sequence of steps that contain increasingly more details. Simulation is a helpful tool in analyzing warehouse design alternatives, but setting up a detailed simulation is too expensive early in the development p...