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This study paper introduces different tools, i.e. analytical methods and visualizations, in business intelligence environments. It especially emphasizes the use of OLAP-based technologies as a tradtional kind of data analysis in contrast to as graph analysis and formal concept analysis as rather new approaches in the area of visual analytics.
The amount of data produced and stored in multiple types of distributed data sources is growing steadily. A crucial factor that determines whether data can be analyzed efficiently is the use of adequate visualizations. Almost simultaneously with the ongoing availability of data numerous types of visualization techniques have emerged. Since ordinary business intelligence users typically lack expert visualization knowledge, the selection and creation of visualizations can be a very time- and knowledge-consuming task. To encounter these problems an architecture that aims at supporting ordinary BI users in the selection of adequate visualizations is developed in this thesis. The basic idea is to automatically provide visualization recommendations based on the concrete BI scenario and formalized visualization knowledge. Ontologies that formalize all relevant knowledge play an important role in the developed architecture and are the key to make the knowledge machine-processable.