2005-07-12: Recent article about the "Top 10 Unsolved Information Visualization Problems": Difference between revisions
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The Top 10 Unresolved Problems identified by C. Chen are: | The Top 10 Unresolved Problems identified by C. Chen are: | ||
#'''Usability''' | #'''Usability'''<br>too less usability studies and empirical evaluations are conducted; new evaluative methodologies are needed; | ||
#'''Understanding elementary perceptual–cognitive tasks''' | #'''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''' | #'''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''' | #'''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''' | #'''Intrinsic quality measures'''<br>need to establish intrinsic quality metrics; | ||
#'''Scalability''' | #'''Scalability'''<br>large amounts of data; parallel computing; high-performance techniques; | ||
#'''Aesthetics''' | #'''Aesthetics'''<br>understanding of how insights and aesthetics interact; insightful and visually appealing information visualization; | ||
#'''Paradigm shift from structures to dynamics''' | #'''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''' | #'''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''' | #'''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 == | == 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. | *[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. | ||
Revision as of 15:38, 12 July 2005
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]. http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=31454&arnumber=1463074&count=14&index=3
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:
- Usability
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; - Scalability
large amounts of data; parallel computing; high-performance techniques; - Aesthetics
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;
References
- [Chen, 2005]: Chen, C. Top 10 Unsolved Information Visualization Problems, IEEE Computer Graphics and Applications, 25(4):12-16, July-Aug. 2005.