Rapid Sampling for Visualizations with Ordering Guarantees
Summary: Proposes sampling algorithms that preserve visual orderings to rapidly generate approximate visualizations (e.g., bar charts) with provable ordering guarantees. Shows optimal sample complexity and practical speedups - orders of magnitude fewer samples and run time than conventional methods, with broad applicability to related visual properties. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Albert Kim (Massachusetts Institute of Technology)
- 2. Eric Blais (Massachusetts Institute of Technology; University of Waterloo)
- 3. Aditya Parameswaran (Massachusetts Institute of Technology; University of Illinois Urbana-Champaign)
- 4. Piotr Indyk (Massachusetts Institute of Technology)
- 5. Sam Madden (Massachusetts Institute of Technology)
- 6. Ronitt Rubinfeld (Massachusetts Institute of Technology; Tel Aviv University)
BibTeX Citation
@article{kim_vldb15,
title = {{Rapid Sampling for Visualizations with Ordering Guarantees}},
author = {Kim, Albert and Blais, Eric and Parameswaran, Aditya and Indyk, Piotr and Madden, Sam and Rubinfeld, Ronitt},
journal = {PVLDB},
series = {{VLDB} '15},
volume = {8},
number = {5},
pages = {521--532},
doi = {10.14778/2735479.2735486},
url = {https://doi.org/10.14778/2735479.2735486},
year = {2015}
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