2005-07-12: Recent article about the "Top 10 Unsolved Information Visualization Problems"

From InfoVis:Wiki
Jump to: navigation, search

In the curent issue of the IEEE Computer Graphics and Applications Journal, Chaomei Chen points out the Top 10 Unsolved Information Visualization Problems [Chen, 2005] (article available at http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=31454&arnumber=1463074&count=14&index=3, requires IEEE subscription).

The article itself is a revised and extended version of the top unresolved problems pointed out at a panel discussion at IEEE Visualization 2004 conference.

The Top 10 Unresolved Problems identified by C. Chen are:

  1. Usability
    too less usability studies and empirical evaluations are conducted; new evaluative methodologies are needed;
  2. Understanding elementary perceptual–cognitive tasks
    the general understanding of elementary perceptual-cognitive tasks must be substantially revised and updated in the context of information visualization; empirical evidence has to be gathered;
  3. Prior knowledge
    information visualization and its users must have a common ground; users need two types of prior knowledge: knowledge of how to operate the device & domain knowledge of how to interpret the content; level of necessary prior knowledge has to be determined;
  4. Education and training
    internally: share various principles and skills of visual communication and semiotics; consolidation of the field's theoretical foundations
    externally: need to show the value of information visualization to potential beneficiaries outside the field; need for compelling showcase examples, widely accessible tutorials for general audiences, raising the awareness of inormation visualization's potential;
  5. Intrinsic quality measures
    need to establish intrinsic quality metrics;
  6. Scalability
    large amounts of data; parallel computing; high-performance techniques;
  7. Aesthetics
    understanding of how insights and aesthetics interact; insightful and visually appealing information visualization;
  8. Paradigm shift from structures to dynamics
    shift the structure-centric paradigm to the visualization of dynamic properties of underlying phenomena;
  9. Causality, visual inference, and predictions
    InfoVis as a powerful medium for finding causality, forming hypotheses, and assessing available evidence; complex analysis algorithms needed; features that facilitate users in finding what-ifs and test their hypotheses should be provided;
  10. Knowledge domain visualization
    information vs. knowledge (values established by social construction process); conveying information structures with added values (knowledge); show amounts of information that are beyond the capacity of textual display;

References[edit]