DBScholar

Back to papers

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)

Paper ID
5836
Venue
SIGMOD
Year
2020
Pagerank
5.093636e-05
Overall Rank
11,755 | 19.36%
DOI
10.1145/3318464.3380561

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

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}
}

Incoming Citations (Sorted by Pagerank)

Showing 10 of 10 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers