Workload-Aware Incremental Reclustering in Cloud Data Warehouses
Summary: Separates reclustering policy from clustering-key selection via boundary micro-partitions at query-range edges. WAIR selectively reclusters pruning-critical partitions, achieving near-optimal query performance with substantially lower cost and a theoretical bound under dynamic workloads. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Yipeng Liu (Tsinghua University)
- 2. Renfei Zhou (Carnegie Mellon University)
- 3. Jiaqi Yan (Snowflake)
- 4. Huanchen Zhang (Shanghai Qi Zhi Institute; Tsinghua University)
BibTeX Citation
@inproceedings{liu_sigmod26,
title = {{Workload-Aware Incremental Reclustering in Cloud Data Warehouses}},
author = {Liu, Yipeng and Zhou, Renfei and Yan, Jiaqi and Zhang, Huanchen},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802127},
url = {https://dl.acm.org/doi/10.1145/3802127},
year = {2026}
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