STULL: Unbiased Online Sampling for Visual Exploration of Large Spatiotemporal Data

TL;DR

This paper introduces STULL, an unbiased online sampling method for large spatiotemporal datasets that improves the accuracy of visual analytics by ensuring equal selection probability for qualifying data points, outperforming biased methods.

Contribution

The paper presents a novel unbiased sampling approach with a new data index and retrieval plan, enhancing the accuracy of interactive visualizations of large spatiotemporal data.

Findings

Samples are at least 50% more accurate in representing spatial distribution.

The approach enables more accurate approximate visualizations.

It outperforms existing biased sampling techniques in experiments.

Abstract

Online sampling-supported visual analytics is increasingly important, as it allows users to explore large datasets with acceptable approximate answers at interactive rates. However, existing online spatiotemporal sampling techniques are often biased, as most researchers have primarily focused on reducing computational latency. Biased sampling approaches select data with unequal probabilities and produce results that do not match the exact data distribution, leading end users to incorrect interpretations. In this paper, we propose a novel approach to perform unbiased online sampling of large spatiotemporal data. The proposed approach ensures the same probability of selection to every point that qualifies the specifications of a user's multidimensional query. To achieve unbiased sampling for accurate representative interactive visualizations, we design a novel data index and an associated sample retrieval plan. Our proposed sampling approach is suitable for a wide variety of visual analytics tasks, e.g., tasks that run aggregate queries of spatiotemporal data. Extensive experiments confirm the superiority of our approach over a state-of-the-art spatial online sampling technique, demonstrating that within the same computational time, data samples generated in our approach are at least 50% more accurate in representing the actual spatial distribution of the data and enable approximate visualizations to present closer visual appearances to the exact ones.