In many application domains, data involves a large number of attributes and categories. In industrial manufacturing, for example, numerous quality indicators are measured for each produced item along with process information such as the order ID, the used machinery, and much more. For such complex data, manually searching for visualizations that reveal interesting patterns such as correlations, trends, and outliers may become very tedious and time-consuming.
The goal of this work is to extend well-known views such as scatterplots, histograms, or categorical views by integrating recommendations on demand of view parameterizations which may be worth looking at. Typical examples could include “list all scatterplots showing correlations between data attributes for any data subset”, or “rank all time-series plots by the amount of showing a clear trend over the past weeks”. Important aspects of this work are thus to:
Starting point(s) for research:
Characterizing Guidance in Visual Analytics, IEEE Transactions on Visualization and Computer Graphics, 23(1):111-120, 2017.
K., Wongsuphasawat, Z. Qu, D. Moritz, R. Chang, F. Ouk, A. Anand, J. Mackinlay, B. Howe, J. Heer,
Voyager 2: Augmenting Visual Analysis with Partial View Specifications, forthcoming, CHI’17, May 06-11, 2017.