STULL：大时空数据可视化探索的无偏在线采样
STULL: Unbiased Online Sampling for Visual Exploration of Large Spatiotemporal Data
摘要：
在线采样支持的可视化分析越来越重要，因为它允许用户以交互速率探索具有可接受的近似答案的大型数据集。然而，现有的在线时空采样技术往往是有偏见的，因为大多数研究人员主要集中在减少计算延迟。有偏差的抽样方法选择具有不等概率的数据，产生的结果与确切的数据分布不匹配，导致最终用户做出错误的解释。在本文中，我们提出了一种新的方法来执行无偏在线采样的大时空数据。所提出的方法确保了相同的概率选择到每一个点，有资格的规格用户的多维查询。为了实现准确的代表性交互式可视化无偏采样，我们设计了一个新的数据索引和相关的样本检索计划。我们提出的采样方法适用于各种各样的可视化分析任务，例如，运行时空数据聚合查询的任务。大量的实验证实了我们的方法优于最先进的空间在线采样技术，表明在相同的计算时间内，在我们的方法中生成的数据样本在表示数据的实际空间分布方面至少准确50%，并使近似可视化能够呈现更接近的视觉外观。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
