Level-Agnostic Designation of Model Elements

Abstract

A large proportion of the domain information conveyed in models is contained in the model element “designators” — the characterizing and identifying textual expressions appearing in the headers of model element visualizations. However, the notational support for representing such designators is usually non-uniform, incomplete and sensitive to the classification level at which a model element resides. Moreover, the relationship between the “names” in a model element’s designator and the values of its linguistic and ontological attributes is often unclear. In the paper we present a simple but powerful Element Designation Notation (EDN) which allows the key information characterizing model elements to be expressed in a compact, uniform and level-agnostic way for the purposes of deep modeling. This not only simplifies and enriches the designation possibilities in traditional modeling scenarios, it paves the way for more expressive models of big data in which the location of data elements within the three key hierarchies — classification, containment and specialization — can be clearly and concisely expressed.