Real-time Workload Pattern Analysis for Large-scale Cloud Databases
Summary: AWM: real-time mining of complex cloud DB workloads by encoding streaming SQL into semantic+execution embeddings and per-business-group clustering. Integrates offline-labeled classification and pattern-based query optimizations; +66% accuracy, -22% latency vs SOTA. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jiaqi Wang (Zhejiang University)
- 2. Tianyi Li (Aalborg University)
- 3. Anni Wang (Alibaba)
- 4. Xiaoze Liu (Purdue University)
- 5. Lu Chen (Zhejiang University)
- 6. Jie Chen (Alibaba)
- 7. Jianye Liu (Alibaba)
- 8. Junyang Wu (Zhejiang University)
- 9. Feifei Li (Alibaba)
- 10. Yunjun Gao (Zhejiang University)
BibTeX Citation
@article{wang_vldb23,
title = {{Real-time Workload Pattern Analysis for Large-scale Cloud Databases}},
author = {Wang, Jiaqi and Li, Tianyi and Wang, Anni and Liu, Xiaoze and Chen, Lu and Chen, Jie and Liu, Jianye and Wu, Junyang and Li, Feifei and Gao, Yunjun},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {12},
pages = {3689--3701},
doi = {10.14778/3611540.3611557},
url = {https://doi.org/10.14778/3611540.3611557},
year = {2023}
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