ScaleViz: Scaling Visualization Recommendation Models on Large Data

Automated visualization recommendation (Vis-Rec) models help users to derive crucial insights from new datasets. Typically, such automated Vis-Rec models first calculate a large number of statistics from the datasets and then use machine-learning models to score or classify multiple visualizations choices to recommend the most effective ones, as per the statistics. However, state-of-the-art models rely on a very large number of expensive statistics and therefore using such models on large datasets becomes infeasible due to prohibitively large computational time, limiting the effectiveness of such techniques to most large real-world datasets. In this paper, we propose a novel reinforcement-learning (RL) based framework that takes a given Vis-Rec model and a time budget from the user and identifies the best set of input statistics, specifically for a target dataset, that would be most effective while generating accurate enough visual insights. We show the effectiveness of our technique as it enables two state of the art Vis-Rec models to achieve up to 10X speedup in time-to-visualize on four large real-world datasets.