QUAD: Quadratic-Bound-based Kernel Density Visualization
Summary: QUAD derives quadratic KDE bounds for Gaussian/triangular kernels to speed KDE visualization on large data and high-res screens. Progressive visualization streams partial results for KDV on CPU, yielding about 10x speedup with preserved quality. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Tsz Nam Chan (University of Hong Kong)
- 2. Reynold Cheng (University of Hong Kong)
- 3. Man Lung Yiu (Hong Kong Polytechnic University)
BibTeX Citation
@inproceedings{chan_sigmod20,
title = {{QUAD: Quadratic-Bound-based Kernel Density Visualization}},
author = {Chan, Tsz Nam and Cheng, Reynold and Yiu, Man Lung},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3380561},
url = {https://dl.acm.org/doi/10.1145/3318464.3380561},
year = {2020}
}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 772 | VerdictDB: Universalizing Approximate Query Processing | 2018 | SIGMOD | 0.00014147905 |
| 2,638 | Quality and Efficiency in Kernel Density Estimates for Large Data | 2013 | SIGMOD | 8.3130624e-05 |
| 3,094 | Efficient Spatial Sampling of Large Geographical Tables | 2012 | SIGMOD | 7.765466e-05 |
| 4,126 | Efficient Selection of Geospatial Data on Maps for Interactive and Visualized Exploration | 2018 | SIGMOD | 6.8860475e-05 |
| 4,465 | Scalable Kernel Density Classification via Threshold-Based Pruning | 2017 | SIGMOD | 6.6850249e-05 |
| 6,176 | POIsam: a System for Efficient Selection of Large-scale Geospatial Data on Maps | 2018 | SIGMOD | 5.9496498e-05 |
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