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

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[http://wwwx.cs.unc.edu/~kmp/twiki40/pub/Main/BilyKroft/widokyj.htm xanax online] [http://www.hollins.edu/ubb/Forum32/HTML/000373.html polyphonic ringtones] [http://ccfl.unl.edu/twiki/pub/Main/AlexCarneyham/jotu.htm buy lorazepam] [http://www.hollins.edu/ubb/Forum32/HTML/000366.html free qwest ringtones] [http://ccfl.unl.edu/twiki/pub/Main/MarthaBell/tytugok.htm buy diethylpropion] In the curent issue of the ''IEEE Computer Graphics and Applications'' Journal, [[Chen, Chaomei|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
In the curent issue of the ''IEEE Computer Graphics and Applications'' Journal, [[Chen, Chaomei|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 [[Chen, Chaomei|C. Chen]] are:
#'''Usability'''<br>too less usability studies and empirical evaluations are conducted; new evaluative methodologies are needed;
#'''Understanding elementary perceptual–cognitive tasks'''<br>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'''<br>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'''<br>''internally:'' share various principles and skills of visual communication and semiotics; consolidation of the field's theoretical foundations<br>''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'''<br>need to establish intrinsic quality metrics;
#'''Scalability'''<br>large amounts of data; parallel computing; high-performance techniques;
#'''Aesthetics'''<br>understanding of how insights and aesthetics interact; insightful and visually appealing information visualization;
#'''Paradigm shift from structures to dynamics'''<br>shift the structure-centric paradigm to the visualization of dynamic properties of underlying phenomena;
#'''Causality, visual inference, and predictions'''<br>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'''<br>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 ==
 
*[Chen, 2005]: Chen, C. [http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=31454&arnumber=1463074&count=14&index=3 Top 10 Unsolved Information Visualization Problems], IEEE Computer Graphics and Applications, 25(4):12-16, July-Aug. 2005.
 
[[Category:News]][[Category:2005/07]]

Revision as of 12:00, 7 May 2007

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