Designing an Effective Graphical Modelling Language

How can visual modelling languages in enterprise architecture be designed to genuinely communicate meaning — rather than confusing or alienating the stakeholders they're meant to serve?

These slides accompany the paper: Designing Effective Visual Languages for Enterprise Modelling and a video of the presentation is available here: Design and Support of Modelling Languages for Effective Graphical Representation, Analysis and Communication

Graphical models are everywhere in enterprise architecture — yet a persistent gap exists between the effort invested in building them and the value they deliver. Models are too technical for business audiences, too homogenous to highlight what matters, or presented in formats that stakeholders simply cannot parse. When practitioners try to bridge this gap by converting rigorous models into PowerPoint slides or Word documents, they sever the connection to the underlying repository — destroying integrity, reusability, and currency in the process.

This paper presents the research programme Graham McLeod is pursuing at the University of Duisburg-Essen, supervised by Prof. Ulrich Frank, to address these problems at a foundational level. The research draws on human visual cognition, semiotics, information encoding theory, the Physics of Notations, and the emerging field of polymetric diagramming — a technique that modifies visual symbol properties such as size, colour, and shape to reflect underlying data, enabling pre-attentive processing and rapid identification of important patterns in large, complex models. The proposed contributions include extended theory for visual notation design, a meta-meta model supporting multiple visual languages over the same semantic model, and a layered tool architecture enabling runtime adaptation of models to purpose, audience, and medium.

For enterprise architects, this research points toward a future where modelling tools can produce representations genuinely suited to a CFO, a process owner, or a technical architect — from the same underlying repository, without manual translation.

Originally published as a doctoral consortium paper by Graham McLeod in the PoEM 2018 Doctoral Consortium Proceedings (CEUR-WS Vol. 2234), Vienna, Austria, 2018.

More Insights Without More Effort: Polymetric Modelling and Visual Intelligence in Enterprise Architecture

How can enterprise architects extract far more insight from their models without significantly increasing the effort required?

The effort required to collect, validate, analyse, and report on enterprise architecture information is itself one of the biggest obstacles to EA delivering value — and yet most approaches simply accept that effort as a given. This 2013 presentation by Graham McLeod challenges that assumption directly, arguing that the right combination of integrated meta models, inferencing, derived values, and visual techniques can dramatically increase the insight produced by an EA repository without requiring proportionally more effort to maintain it. A particularly compelling section introduces polymetric diagramming — a technique that modifies the visual properties of model symbols (size, colour, shape, border width, position) based on the actual data values of the objects they represent, turning what would otherwise be static structural diagrams into rich, information-dense pictures that exploit the human visual system's innate ability to detect patterns, movement, and anomalies. Worked examples show function models where symbol width reflects delay time, process models where width maps to duration, height to cost, and colour intensity to resource consumption, and application maps clustered and sized by investment or number of non-standard interfaces. The underlying architecture — a separation of logical model types from their visual representations, with polymetric specifications scripted in a flexible DSL — is implemented in Pharo Smalltalk using the Mondrian and Roassal graphics libraries and the EVA Graphical Modeler. For practitioners wrestling with the gap between the volume of data in their EA repositories and the quality of insight they can extract from it, this presentation offers both a compelling vision and a concrete technical path.

Originally presented by Graham McLeod at an Inspired event, September 2013.