2005-07-12: Recent article about the "Top 10 Unsolved Information Visualization Problems"
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:
too less usability studies and empirical evaluations are conducted; new evaluative methodologies are needed;
- 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;
- 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;
- 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;
- Intrinsic quality measures
need to establish intrinsic quality metrics;
large amounts of data; parallel computing; high-performance techniques;
understanding of how insights and aesthetics interact; insightful and visually appealing information visualization;
- Paradigm shift from structures to dynamics
shift the structure-centric paradigm to the visualization of dynamic properties of underlying phenomena;
- 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;
- 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;
- [Chen, 2005]: Chen, C. Top 10 Unsolved Information Visualization Problems, IEEE Computer Graphics and Applications, 25(4):12-16, July-Aug. 2005.