SA-LSM: Optimize Data Layout for LSM-tree Based Storage using Survival Analysis
Summary: SA-LSM uses Survival Analysis to predict cold data and reflow LSM-tree layout beyond conventional compaction decisions. Implemented in X-Engine with an external training/inference service, it achieves up to 78.9% tail-latency reduction on real workloads. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Teng Zhang
- 2. Jian Tan
- 3. Xin Cai
- 4. Jianying Wang
- 5. Feifei Li
- 6. Jianling Sun
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,071 | Structural Designs Meet Optimality: Exploring Optimized LSM-tree Structures in A Colossal Configuration Space | 2024 | SIGMOD | 4.4025274e-05 |
| 10,021 | Hourglass: An Adaptive Range Filter with Lightweight Hybrid Encoding | 2026 | SIGMOD | 4.1945683e-05 |
| 10,176 | Improving Range Scan Performance in LSM-trees with Group Caching | 2026 | SIGMOD | 4.1945683e-05 |
| 10,367 | Aster: Enhancing LSM-structures for Scalable Graph Database | 2025 | SIGMOD | 4.1945683e-05 |
| 11,088 | Lindorm-UWC: An Ultra-Wide-Column Database for Internet of Vehicles | 2024 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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