Knowledge-assisted Visualization

Call for Participation

Aim and Scope of the Workshop

Most of the existing visualization techniques and systems were not designed to utilize the knowledge and information derived from the process of scientific visualization or from abstract data analysis. As visual exploration is an inherently iterative process, it is highly desirable to enable more effective visualization by utilizing information about the visualization process itself (e.g., users' chosen visualization parameters and abstractions), and information about the scientific data to be visualized (e.g., high level abstract characterization, and findings). The combination of such information from different visualization processes can also infer new knowledge that can aid data visualization in an intelligent manner if it is stored and organized in a structured fashion.

We begin to see growing efforts to collect and use such information and knowledge, especially when the cost of visualization is high or when the visualization work is collaborative in nature. In addition, information visualization techniques are increasingly used in the context of scientific visualization due to the diverse types of information that need to be looked at for more comprehensive data analysis.

This workshop aims at stimulating the research efforts for knowledge- and information-assisted visualization by providing a forum for shaping this important and exciting research area. We solicit submissions on work in progress as well as mature results. In particular, the utilization of information and knowledge in producing visually effective data visualization and facilitating efficient visualization processes is the main focus of this workshop.

Topics include but are not limited to:

  • Metadata visualization
  • Visualization enabled by topological information of the data
  • Visualization enabled by statistical information of the data
  • Visualization enabled by geometric information of the data
  • Visualization enabled by semantic information of the data
  • Visualization via learning
  • Visualization via shared knowledge in a collaborative setting
  • Knowledge representation for visualization

You may find this position paper by the main organizers of Kav2007 and KaV2008 helpful in appreciating the research directions which this workshop tries to stimulate.

Abstract Submission

The workshop is a full-day event held in conjunction with the IEEE Visualization 2010 Conference. It will follow the successful format of KaV2007, which attracted between 50-80 participants throughout the day, when the selected contributions were presented at the workshop in a very interactive fashion. For each contribution, there was a 15 minute presentation, and 15 minute question and answer session led by experienced researchers from a steering committee of the workshop.

Participants, who are interested in presenting their work in areas of knowledge- and information-assisted visualization, are asked to submit an extended abstract (limited to 2 pages), which will be reviewed by an international program committee (IPC). The selected contributions will be presented in KaV2008 in a format similar to KaV2007. We are also planning to organize a special issue of a journal or a book after the workshop. A number of participants will be invited to make a full submission for a publication in such a special issue or book. The selection for full submission will be based on the quality of both the extended abstract and the oral presentation in the workshop.

For due dates please referto the Important Dates page. Each extended abstract must contain the following information:

  • Title, Author(s) and Affiliation(s)
  • Introduction (or Problem Statement) - In this section, the author(s) should include a clear statement(s) about the scientific or technical problem to be addressed, and if appropriate, a brief description of the relevant background (such as application, data modality).
  • Approach (or Methods) - In this section, the author(s) can describe what is the information extracted from the data, and if any, describe what is the knowledge inferred from the information; and outline the methods for information extraction and/or knowledge inference, explain why do you think the methods are technically sound.
  • Results and Evaluation - In this section, the author(s) can present results and evaluation of the work.
  • Discussions and Conclusions (and Relationship to KaV) - In this section, the author(s) should state the main novel contributions of this work; outline how the extracted information and/or inferred knowledge is used in visualizing the input data; and describe the benefits of such a knowledge-assisted visualization in relation to the stated visualization problem and/or the application.
  • References to major prior works in the field.

We recommend following the formatting guideline of VGTC conferences, which can be found at the following link. Please submit your contribution online.


[1] Min Chen, David Ebert, Hans Hagen, Robert S. Laramee, Robert van Liere, Kwan-Liu Ma, William Ribarsky, Gerik Scheuermann, and Deborah Silver, Data, Information and Knowledge in Visualization, in: IEEE Computer Graphics and Applications (IEEE CG&A), Vol. 29, No. 1, January/February 2009, pages 12-19.

[2] Robert van Liere, Robert S. Laramee, Gerik Scheuermann, and Kwan-Liu Ma, Guest Editors of Computers & Graphics (C&G), Vol. 33, No. 5, October 2009, pages 583-584, Special Issue on Knowledge Assisted Visualization (KAV)

[3] IEEE Computer Graphics and Applications, M. Chen and H. Hagen (guest editors), Vol. 30, No. 1, 2010

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