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Journal Article

Contextual Adaptive Knowledge Visualization Environments  pp1-14

Xiaoyan Bai, David White, David Sundaram

© Jan 2012 Volume 10 Issue 1, ECKM 2011, Editor: Franz Lehner, pp1 - 109

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As an essential component of knowledge management systems, visualizations assist in creating, transferring and sharing knowledge in a wide range of contexts where knowledge workers need to explore, manage and get insights from tremendous volumes of data. Knowledge visualization context may incorporate any information in regard to the decisional problem context within which visualizations are applied, the visualization profiles of knowledge workers as well as their intended purposes. Due to the inherent dynamic nature, these contextual factors may cause the changing visualization requirements and difficulties in maintaining the effectiveness of a knowledge visualization when contextual changes occur. To address the contextual complexities, visualization systems to support knowledge management need to provide flexible support for the creation, manipulation, transformation and improvement of visualization solutions. Furthermore, they should be able to sense, analyze and respond to the contextual changes so as to support in maintaining the effectiveness of the solutions. In addition, they need to possess the capability to mediate between the problem and the knowledge workers through provision of action and presentation languages. However, many visualization systems tend to provide weak support for fulfilling these system requirements. They do not provide adequate flexibility for adapting the visualizations to fit different knowledge visualization contexts. This motivated us to propose and implement a flexible knowledge visualization system for better aiding knowledge creation, transfer and sharing, namely, Contextual Adaptive Visualization Environment (CAVE). CAVE provides flexible support for (1) sensing and being aware of changes in the problem, purpose and/or knowledge worker contexts, (2) interpreting the changes through relevant analysis and (3) responding to the changes through appropriate re‑design and re‑modelling of visual compositions to address the problem. In order to fulfil the requirements posed above, we developed and proposed conceptual models and frameworks which are further elucidated through system‑oriented architectures and implementations.


Keywords: knowledge visualization, knowledge visualization context, knowledge creation and sharing, CAVE model, CAVE framework, and CAVE implementation


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