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Model-based Approximate Query Processing
Interactive visualizations are arguably the most important tool to explo...
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SCATTERSEARCH: Visual Querying of Scatterplot Visualizations
Scatterplots are one of the simplest and most commonly-used visualizatio...
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InfiniViz: Interactive Visual Exploration using Progressive Bin Refinement
Interactive visualizations can accelerate the data analysis loop through...
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Kyrix: Interactive Visual Data Exploration at Scale
Scalable interactive visual data exploration is crucial in many domains ...
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Mosaic: A Sample-Based Database System for Open World Query Processing
Data scientists have relied on samples to analyze populations of interes...
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Selection Bias Tracking and Detailed Subset Comparison for High-Dimensional Data
The collection of large, complex datasets has become common across a wid...
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Do We Really Sample Right In Model-Based Diagnosis?
Statistical samples, in order to be representative, have to be drawn fro...
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STULL: Unbiased Online Sampling for Visual Exploration of Large Spatiotemporal Data
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 spatial distribution of the data and enable approximate visualizations to present closer visual appearances to the exact ones.
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