Cube v3 has been a powerful tool to examine reports of the parallel performance tool Scalasca, but was basically unable to perform analyses on its own. With Cube v4, we addressed several shortcomings of Cube v3. We generalized the Cube data model, extended the list of supported data types, and allow operations with nontrivial algebras, e.g. for performance models or statistical data. Additionally, we introduced two major new features that greatly enhance the performance analysis features of Cube: Derived metrics and GUI plugins. Derived metrics can be used to create and manipulate metrics directly within the GUI, using a powerful domain-specific language called CubePL. Cube GUI plugins allow the development of novel performance analysis techniques and visualizations based on Cube data without changing the source code of the Cube GUI.