unsolved information visualization problems
24 August 2005
an interesting recent academic paper, written by information visualization guru Chaomei Chen, discussing the Top 10 Unsolved Information Visualization Problems. 'problem 7: aesthetics. the purpose of information visualization is the insights into data that it provides, not just pretty pictures. but what makes a picture pretty? what can we learn from making a pretty picture & enhancing the representation of insights? it’s important, therefore, to understand how insights & aesthetics interact, & these two goals could sustain insightful & visually appealing information visualization.'
I am quite glad that aesthetics is becoming an accepted aspect in visualization research. but here is a question for you: is 'aesthetics' (in visualization) more than 'pretty pictures'?
[computer.org & computer.org (pdf)]
air pollution helium balloon span> brand-based collaborative tag clouds span> tkaap video tracking interface span> BMW kinetic sculpture span> flokoon visual search span> pulsating emotion organism span> cinema redux film mosaics span> laser-cut sound analysis sculptures span> caffeine usage arcs span> zoomii visual amazon store span> mapping scientific citations span> collective prediction network span> all the water in the world span> reflect conversation table span>
the Top 10 Unsolved Information Visualization Problems, according to Chamoi Chen:
1. usability: the need to perform usability studies & emperical evaluations on new visualization methods,
2. understanding elementary perceptual-cognitive tasks: studying users' perceptual & cognitive needs when browsing & searching,
3. prior knowledgeon how to operate the visualization & how to interpret its content,
4. education & training: of the general audience by showcasing examples & tutorials to raise the awareness of information visualization's potential,
5. intrinsic quality measures: to benchmark & evaluate new visualization approaches against,
6. scalability: to enable parallel computing & the visualization of large data streams,
7. aesthetics: see the original post above,
8. paradigm shift from structures to dynamics: towards time-varying datasets, data streams & immediate trend-detection,
9. causality, visual inference, & predictions: by specific forecasting algorithms that assist in resolving conflicting hypotheses,
10. knowledge domain visualization: or the communication of knowledge instead of the display of abstract data as information.


