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
- 2. Tianyi Li
- 3. Anni Wang
- 4. Xiaoze Liu
- 5. Lu Chen
- 6. Jie Chen
- 7. Jianye Liu
- 8. Junyang Wu
- 9. Feifei Li
- 10. Yunjun Gao
Incoming Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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| 5,645 | Database Workload Characterization with Query Plan Encoders | 2022 | VLDB | 5.3928148e-05 |