ScaleViz: Scaling Visualization Recommendation Models on Large Data

TL;DR

ScaleViz introduces a reinforcement learning framework that optimizes visualization recommendation models for large datasets, significantly reducing computation time while maintaining accuracy, enabling practical use on complex real-world data.

Contribution

The paper presents a novel RL-based method to select effective statistics for visualization models within time constraints, improving scalability on large datasets.

Findings

Achieves about 10X faster visualization generation compared to baselines.

Effectively reduces computational time with minimal accuracy loss.

Validates approach on three large real-world datasets.

Abstract

Automated visualization recommendations (vis-rec) 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 very large number of expensive statistics and therefore using such models on large datasets become infeasible due to prohibitively large computational time, limiting the effectiveness of such techniques to most real world complex and large 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 that would be most effective while generating the visual insights within a given time budget, using the given model. Using two state-of-the-art vis-rec models applied on three large real-world datasets, we show the effectiveness of our technique in significantly reducing time-to visualize with very small amount of introduced error. Our approach is about 10X times faster compared to the baseline approaches that introduce similar amounts of error.