Applications might require more complex relationships between abstract objects than would be possible with simple static relationships (eg. detailed information about conversion from one data element to another). These requirements are fulfilled by implementing a concept of 'generic transformation' enabling definition of transformations, mappings and transitions of any complexity, exploiting recursion to flatten complex transformations. A limited set of transformation types ('templates') is defined on user-interface level to simplify creation and management of these transformations.

Transformations are decomposed and stored as hierarchies of binary operations based on basic operators (arithmetic operators, predicates, functions), compound operators (templates built from basic operators) and extended relational algebra. Such decomposition enables storage of transformations of arbitrary complexity, making it possible to capture everything from arithmetic operations and simple mappings to complex statistical methods and lengthy ETL procedures in SQL.

Having semantic information (ontologies), business rules and technical metadata (data structures) together with transformation metadata and relationships linking them all together makes it possible to achieve functionality that would not be possible with separated metadata subsystems, like metadata driven processing environments, full impact analysis, data-storage-aware business dictionaries etc.