Comprehensive data analysis has become indispensable in a variety of environments. Standard OLAP systems, however, are too rigid for some domains such as medicine, science, or government, where analysts are confronted with complex, imprecise, incomplete, or irregular data.

The aim of this research is to identify modeling requirements for supporting complex data found in real-world applications and extend the multidimensional data model accordingly. The ultimate usefulness of the proposed solutions at the conceptual level is determined by their implementability in a visual OLAP interface. We verify our concepts by implementing an OLAP application called UniVis Explorer as a client user interface to an open-source university data warehouse SuperX.