Online Analytical Processing (OLAP) is a popular technique for explorative data analysis. Usually, a fixed set of dimensions (such as time, place, etc.) is used to explore and analyze various subsets of a given, multi-dimensional data set. These subsets are selected by constraining one or several of the dimensions, for instance, showing sales only in a given year and geographical location. Still, such aggregates are often not enough. Important information can only be discovered by combining several dimensions in a multidimensional analysis. Most existing approaches allow to add new dimensions either statically or dynamically. These approaches support, however, only the creation of global dimensions that are not interactive for the user running the report. Furthermore, they are mostly restricted to data clustering and the resulting dimensions cannot be interactively refined.

In this paper we propose a technique and an architectural solution that is based on an interaction concept for creating OLAP dimensions on subsets of the data dynamically, triggered interactively by the user, based on arbitrary multi-dimensional grouping mechanisms. This approach allows combining the advantages of both, OLAP exploration and interactive multidimensional analysis. We demonstrate the industry-strength of our solution architecture using a setup of IBM® InfoSphere™ Warehouse data mining and Cognos® BI as reporting engine. Use cases and industrial experiences are presented showing how insight derived from data mining can be transparently presented in the reporting front end, and how data mining algorithms can be invoked from the front end, achieving closed-loop integration.