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Online sampling-supported visual analytics is increasingly important, as itallows users to explore large datasets with acceptable approximate answers atinteractive rates. However, existing online spatiotemporal sampling techniquesare often biased, as most researchers have primarily focused on reducingcomputational latency. Biased sampling approaches select data with unequalprobabilities and produce results that do not match the exact datadistribution, leading end users to incorrect interpretations. In this paper, wepropose a novel approach to perform unbiased online sampling of largespatiotemporal data. The proposed approach ensures the same probability ofselection to every point that qualifies the specifications of a user'smultidimensional query. To achieve unbiased sampling for accuraterepresentative interactive visualizations, we design a novel data index and anassociated sample retrieval plan. Our proposed sampling approach is suitablefor a wide variety of visual analytics tasks, e.g., tasks that run aggregatequeries of spatiotemporal data. Extensive experiments confirm the superiorityof our approach over a state-of-the-art spatial online sampling technique,demonstrating that within the same computational time, data samples generatedin our approach are at least 50% more accurate in representing the actualspatial distribution of the data and enable approximate visualizations topresent closer visual appearances to the exact ones.

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