Self-Tuning, GPU-Accelerated Kernel Density Models for Multidimensional Selectivity Estimation
Summary: Self-tuning, GPU-accelerated KDE for multidimensional selectivity estimation; numeric optimization and continuous adaptation to data and workload. Experiments show scalable models that adapt to changes and outperform both KDE baselines and state-of-the-art multidimensional histograms. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Max Heimel (Technical University of Berlin)
- 2. Martin Kiefer (Technical University of Berlin)
- 3. Volker Markl (Technical University of Berlin)
BibTeX Citation
@inproceedings{heimel_sigmod15,
title = {{Self-Tuning, GPU-Accelerated Kernel Density Models for Multidimensional Selectivity Estimation}},
author = {Heimel, Max and Kiefer, Martin and Markl, Volker},
series = {{SIGMOD} '15},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2723372.2749438},
url = {https://dl.acm.org/doi/10.1145/2723372.2749438},
year = {2015}
}
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