Figure 1: An analyst is using Constellations to investigate results generated by previous analysts. Constellations organizes these visualizations with projection and clustering. Adjusting the data coverage, encoding choice, and keywords sliders changes how pairwise chart similarities are scored and updates the projected layout and cluster groupings. Several charts are tagged to show how their positions change. AbstractMany data problems in the real world are complex and require multiple analysts working together to uncover embedded insights by creating chart-driven data stories. How, as a subsequent analysis step, do we interpret and learn from these collections of charts? We present Chart Constellations, a system to interactively support a single analyst in the review and analysis of data stories created by other collaborative analysts. Instead of iterating through the individual charts for each data story, the analyst can project, cluster, filter, and connect results from all users in a meta-visualization approach. Constellations supports deriving summary insights about prior investigations and supports the exploration of new, unexplored regions in the dataset. To evaluate our system, we conduct a user study comparing it against data science notebooks. Results suggest that Constellations promotes the discovery of both broad and high-level insights, including theme and trend analysis, subjective evaluation, and hypothesis generation.

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