arXiv:2411.18657v1 Announce Type: new
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.
Automated Visualization Recommendations in Data Analysis
Automated visualization recommendations have become indispensable tools in data analysis, helping users to extract crucial insights from complex datasets. These recommendations are generated by models that calculate numerous statistics from the dataset and then employ machine learning algorithms to score and classify various visualization options, suggesting the most effective ones based on the statistics. However, existing models heavily rely on a large number of computationally expensive statistics, making them impractical for analyzing large datasets. As a result, these techniques often fail to provide efficient and effective visualization recommendations for real-world complex datasets.
To overcome this limitation, the authors propose a novel framework based on reinforcement learning (RL) to optimize visualization recommendations within a given time budget. The user provides a vis-rec model and a predefined time budget, and the RL algorithm identifies the most effective set of input statistics for generating visual insights within the given time constraints.
The multi-disciplinary nature of this research is evident in the integration of machine learning, data analysis, and reinforcement learning techniques. By combining these different fields, the authors aim to improve the efficiency and effectiveness of automated visualization recommendations.
Experimental Results
In order to evaluate their proposed framework, the authors conducted experiments using two state-of-the-art vis-rec models on three large real-world datasets. The results demonstrated the effectiveness of their technique in significantly reducing the time required to generate visualizations, while introducing only a small amount of error.
Compared to baseline approaches that introduce similar amounts of error, the proposed RL-based framework was found to be approximately 10 times faster. This substantial reduction in computational time makes it feasible to apply automated visualization recommendations on large and complex datasets, thus enhancing the usefulness of these techniques in real-world scenarios.
Future Directions
This research opens up several avenues for further exploration. Firstly, there is scope to investigate different reinforcement learning algorithms and their impact on the optimization of visualization recommendations. Additionally, examining the applicability of the proposed framework to different types of datasets and vis-rec models could provide valuable insights.
Furthermore, exploring the potential of incorporating domain knowledge and user preferences into the RL framework could lead to more personalized and context-aware visualization recommendations. By considering the unique characteristics of each dataset and the specific needs of users, the framework can generate recommendations that align with domain-specific requirements.
Overall, this research sheds light on the importance of efficient visualization recommendation techniques and introduces a promising approach using reinforcement learning. By addressing the computational challenges associated with large datasets, this framework paves the way for more effective and scalable automated visualization recommendations in diverse domains.