While this is certainly an immensely useful and promising development, one requirement remains – the matching of a newly acquired dataset with the appropriate segment of the library storing the expert knowledge. As a means to overcome this inherent problem, efforts have begun to store visualization expertise directly with the visualization method and possibly the dataset, to then be utilized for user guidance in the data visualization, suggesting to the user both the visualization method and its best parameters for the data and task at hand. Furthermore, comprehensive expertise is often not available in a centralized venue, but distributed over sub-communities. The ever-growing arsenal of methods and parameters available for data visualization can be daunting to the casual user and even to domain experts.
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