Qd-tree: Learning Data Layouts for Big Data Analytics
Summary: qd-tree: learning-based data layouts route records to storage blocks, minimizing I/O for analytics. Two methods, greedy and deep RL, build the qd-tree, delivering large I/O speedups over blocking and near 2× data-skipping lower bound, with semantic block descriptions. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zongheng Yang (Microsoft; University of California Berkeley)
- 2. Badrish Chandramouli (Microsoft)
- 3. Chi Wang (Microsoft)
- 4. Johannes Gehrke (Microsoft)
- 5. Yinan Li (Microsoft)
- 6. Umar Farooq Minhas (Microsoft)
- 7. Per-Åke Larson (Microsoft)
- 8. Donald Kossmann (Microsoft)
- 9. Rajeev Acharya (Microsoft)
BibTeX Citation
@inproceedings{yang_sigmod20,
title = {{Qd-tree: Learning Data Layouts for Big Data Analytics}},
author = {Yang, Zongheng and Chandramouli, Badrish and Wang, Chi and Gehrke, Johannes and Li, Yinan and Minhas, Umar Farooq and Larson, Per-Åke and Kossmann, Donald and Acharya, Rajeev},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3389770},
url = {https://dl.acm.org/doi/10.1145/3318464.3389770},
year = {2020}
}
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