Revealing Real Problems in Real Data Warehouse Applications

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 in the areas of dimensional modeling, aggregation management, metric definitions, versioning and duality of both master and tracking data, context-sensitive fact calculations, derived attributes, heterogeneous reports, and data security. For some of these problems, solutions are shown both within and beyond the framework of the data warehouse platform used for building GfK’s market research data warehouse system. The paper concludes with a list of requirements for extensions of current data warehouse and OLAP systems.