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Improving Visualizations to Support Rapid Decision Making

Improving Visualizations to Support Rapid Decision Making

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Data visualization is an effective mechanism for identifying trends,insights, and anomalies in data. On large datasets, however, generating visualizations can take a long time, delaying the extraction. One solution is to use online sampling-based schemes to generate visualizations faster while improving the displayed estimates incrementally, eventually converging to the exact visualization computed on the entire data. We have developed the algorithms in the context of an incremental visualization tool, titled INCVISAGE, for trendline and heatmap visualizations. We evaluate the usability of INCVISAGE via user studies and demonstrate that users are able to make effective decisions with incrementally improving visualizations, especially compared to vanilla online-sampling based schemes.
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