Towards Flexible Graphical Communication Using Adaptive Diagrams

IEEE Computer Graphics and Applications, 2001

Diagrams are essential in documenting large information systems. They capture, communicate and leverage knowledge indispensable for solving problems and are conceived to act as "cognitive externalizations". 1 A diagram provides a mapping from the problem domain to the visual representation by supporting cognitive processes that involve perceptual pattern finding and cognitive symbolic operations. 2 However not all mappings are equivalent, and to be effective a diagram's representation needs to be embedded with characteristics such that meaningful patterns can be easily perceived. Consequently a diagram's effectiveness depends, to some extent, on how well it is constructed as an input to our visual system. In our research, we focus on a class of diagrams commonly referred to as graphs or nodelink diagrams. Nodes representing entities, objects or processes, and links or edges representing relationships between the nodes characterize them. Their most common form is that of outline circles or boxes denoting nodes, and lines of different types representing links between the nodes. Entity-relationship diagrams, software structure diagrams and data flow models are examples of node-link diagrams used to model the structure of processes, software, or data.

2003

Of the set of behaviors that will be required of an autonomous artificially intelligent agent, a somewhat neglected member has been the ability to deal with diagrammatic information. We describe a theoretical and computational foundation for machine processing of certain forms of diagrammatic information and show its usefulness in enabling a system to draw inferences from diagrammatic information, make decisions using diagrammatic information, learn from diagrammatic information, solve problems posed diagrammatically, and communicate diagrammatically in a variety of domains.

1995

This paper reports on an experiment to test the feasibility of using a diagramming tool to access an architectural knowledge base. Our hypothesis is that designers only use a small set o conventional elements to make diagrams of architect concepts. If this true, then a scheme for retrieving information from computer knowledge bases using diagrams would work. Therefore we asked designers to make diagrams from texts, to interpret'. diagrams into texts, to pair diagrams and texts, and to comment on existing pars from the case base. We found from the experiment that common features are shared between different designers' diagrams and texts.

1998

Given the recent move towards visual languages in real-world system specification and design, the need for algorithmic procedures that produce clear and eye-pleasing layouts of complex diagrammatic entities arises in full force. This talk addresses a modest, yet still very difficult version of the problem, in which the diagrams are merely general undirected graphs with straight-line edges.

Theory and application of diagrams, 2000

In producing diagrams for a variety of contexts, people use a small set of schematic figures to convey certain context specific concepts, where the forms themselves suggest meanings. These same schematic figures are interpreted appropriately in context. Three examples will support these conclusions: lines, crosses, and blobs in sketch maps; bars and lines in graphs; and arrows in diagrams of complex systems.

Proceedings of Balisage: The Markup Conference 2020

Models for XML documents often focus on text documents, but XML is used for many other kinds of data as well: databases, math, music, vector graphic images, and more. This paper examines how basic document models in the “text” world, do and do not fit a quite different kind of data: vector graphic images, and in particular their very common application for many kinds of diagrams.

Lecture Notes in Computer Science, 2004

Smart Diagram Environments (SDEs) are software applications that use structured diagrams to provide a natural visual interface that behaves as if the computer "understands" the diagram. Unfortunately, despite their potential usefulness, SDEs are not easy to build. We present CIDER a Java toolkit for building SDEs which greatly simplifies this task. CIDER is a generic component-based system which is designed to be easily embedded in Java applications. It provides automatic interpretation of diagrams as they are constructed and manipulated, structure preserving manipulation, and a powerful transformation system for specifying diagram manipulations and user interaction. CIDER's main innovation is its component-based approach to SDE development which provides substantially increased architectural flexibility to the application programmer.

2017

A classic Integrated Development Environment (IDE) allows displaying information only with a textual representation. This kind of representation is perfect for the linear aspect of the code, but not effective to represent links between code fragments. Current graphical code representation modules in IDE are suited to apprehend the system from a global point of view. However, the cognitive integration cost of those diagrams is disproportionate related to the elementary coding task. Our approach considers graphical representation but only with code elements that are parts of the developer's mental model during his programming task. The corresponding cognitive integration of our graphical representation is then less costly and the information that text struggles to display will be clearly explicit. We use UML for this representation because it is a widespread and well-known formalism. We want to show that dynamic diagrams, whose content is modified and adapted in real-time by monit...

Proceedings of the …, 2005

—In this position paper, we present ideas about creating a next generation framework towards an adaptive interface for data communication and visualisation systems. Our objective is to develop a system that accepts large data sets as inputs and provides user-centric, meaningful visual information to assist owners to make sense of their data collection. The proposed framework comprises four stages: (i) the knowledge base compilation, where we search and collect existing state-of-the-art visualisation techniques per domain and user preferences; (ii) the development of the learning and inference system, where we apply artificial intelligence techniques to learn, predict and recommend new graphic interpretations (iii) results evaluation; and (iv) reinforcement and adaptation, where valid outputs are stored in our knowledge base and the system is iteratively tuned to address new demands. These stages, as well as our overall vision, limitations and possible challenges are introduced in this article. We also discuss further extensions of this framework for other knowledge discovery tasks.