The ability to continuously adapt its business processes is a crucial ability for any company in order to survive in todays dynamic world. In order to accomplish this task, a company needs to profoundly analyze all its business data. This generates the need for data integration and analysis techniques that allow for a comprehensive analysis.

A particular challenge when conducting this analysis is the integration of process data generated by workflow engines and operational data that is produced by business applications and stored in data warehouses. Typically, these two types of data are not matched as their acquisition and analysis follows different principles, i.e., a process-oriented view versus a view focusing on business objects.

To address this challenge, we introduce a framework that allows to improve business processes considering an integrated view on process data and operational data.We present and evaluate various architectural options for the data warehouse that provides this integrated view based on a specialized federation layer. This integrated view is also reflected in a set of operators that we introduce.We show how these operators ease the definition of analysis queries and how they allow to extract hidden optimization patterns by using data mining techniques.