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Abstract: By providing visual representation of data, visualization can help people carry out some tasks more effectively. Given a data set, however, there are have too many different visualization techniques, where each technique has many parameters to be tweaked. We are asking if it is possible to automatically design a visualization that is best suited to pursue a given task on given input data. We have developed five new techniques to achieve this goal for specific data sets: perception-driven dimensionality reduction, the selection of line chart or scatter plot for time-series data, a framework for aspect ratio selection, consistency-preserving word cloud editing and constrained graph exploration.
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