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Knowledge-Assisted Visualization

Min Chen, Swansea University
Hans Hagen, University of Kaiserslautern

Pages: pp. 15-16

Visualization is an exploratory process. For example, given a data set, a user first decides which visualization tools to use to explore it. The user then experiments with different controls, such as styles, layout, viewing position, color maps, and transfer functions, until he or she obtains a collection of satisfactory visualization results. For complex visualizations, interaction alone often can't reduce the search space rapidly. So, we need to advance the visualization technology, from today's interactive visualization to tomorrow's knowledge-assisted visualization.

Knowledge-assisted visualization's objectives include sharing domain knowledge among different users and reducing the burden on users to acquire knowledge about complex visualization techniques. It also aims to enable the visualization community to learn and model the best practices, so that powerful visualization infrastructures can evolve. 1 Knowledge-assisted visualization is in its infancy. Most visualization techniques and systems don't yet utilize the knowledge captured from domain experts or the visualization process. In several recent developments, researchers have made noticeable effort to capture and make use of knowledge in visualization. These developments confirm the technical feasibility of knowledge-assisted visualization and indicate its great potential.

In This Issue

We're delighted to present four articles representing the first step toward knowledge-assisted visualization. The first two demonstrate the benefits of incorporating domain experts' knowledge in visualization. The third proposes to evolve an information-assisted visualization system to a knowledge-assisted one. The last article describes a software environment for developing knowledge-assisted-visualization applications.

In "Knowledge-Assisted Reconstruction of the Human Rib Cage and Lungs," Christopher Koehler and Thomas Wischgoll address the nontrivial problem of 3D reconstruction from x-ray images. They exploit the domain knowledge of the general shape of human ribs and lungs to overcome the difficulty caused by missing 3D information in the x-ray images. This technique facilitates more accurate visualization for helping medical professionals detect diseases such as lung cancer at early stages. In addition, it reduces the need for more costly computed-tomography scans.

In "Knowledge-Assisted Visualization and Segmentation of Geologic Features," Benjamin Kadlec and his colleagues present a technique for analyzing 3D seismic data sets. Geologic surface analysis usually requires a trained eye and the subjective analysis of experts. The proposed technique incorporates knowledge of seismic attributes with level-set surface evolution into an interactive environment in which users can store and manage domain knowledge about geologic features. A user study shows that this technique can transfer domain knowledge to nonexpert users.

In "Visual Analysis of Flow Features Using Information Theory," Heike Jänicke and Gerik Scheuermann build on their previous research on information-assisted visualization of time-dependent multivariate flow data sets. They propose extensions to the -machines representation that depicts local flow patterns, their temporal evolution, and interactions between them. Extracted information, such as coherent structures or unusual patterns, can be stored as knowledge and later used to aid the analysis of new data sets.

In "Prajna: Adding Automated Reasoning to the Visual-Analysis Process," Edward Swing presents the Prajna Project, a Java toolkit consisting of software components for automated reasoning and visualization. In particular, Prajna provides visualization application developers with a set of reasoners that can augment data using automated reasoning based on precaptured knowledge. Prajna suggests an exciting potential to build powerful knowledge-assisted-visualization infrastructures. With such an infrastructure, users could capture and reason about attributes of visualization processes and user interactions, automatically create visualization ontologies, and use inferred knowledge to help other users.

Interactive visualization has matured. Information-assisted visualization is undergoing significant development. With a large amount of information being collected locally and globally, a transition to knowledge-assisted visualization is inevitable. We hope that these articles inspire visualization scientists and researchers to take a leap in developing a new generation of visualization systems. We need to exploit advances in other areas of computing technology, including semantic computing, autonomic computing, knowledge-based systems, data warehousing, machine learning, and search engine optimization. We also need to start collecting our own data about visualization processes and to gain a scientific understanding—that is, knowledge—from such data.


About the Authors

Min Chen is a professor of computer science at Swansea University. He's also the codirector of the Wales Research Institute of Visual Computing. His main research interests include visualization, computer graphics, and interactive computing. Chen has a PhD in graphical user interfaces from the University of Wales. He was the papers cochair of IEEE Visualization 2007 and 2008. He's on the editorial boards of IEEE Transactions on Visualization and Computer Graphics and Computers & Graphics. Contact him at
Hans Hagen is a professor of computer science at the University of Kaiserslautern and the scientific director of the International Research Training Group, a computer science graduate school. His research interests include scientific visualization, computer graphics, and geometric modeling. Hagen has a PhD in mathematics (geometry) from the University of Dortmund. He's an associate editor of Computer Aided Geometric Design, Computing, and Mathematical Engineering in Industry . Contact him at
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