Are Updatable Learned Indexes Ready?
Summary: First comprehensive empirical evaluation of updatable learned indexes across ten real datasets and varied workloads. Compared with traditional indexes under changing distributions and concurrency, it yields deployment guidance and design takeaways. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Chaichon Wongkham
- 2. Baotong Lu
- 3. Chris Liu
- 4. Zhicong Zhong
- 5. Eric Lo
- 6. Tianzheng Wang
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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| 10,087 | High Performance or Low Memory? An Updatable Learned Index Framework for Time-Space Tradeoff | 2026 | SIGMOD | 4.1945683e-05 |
| 6,445 | Updatable Learned Indexes Meet Disk-Resident DBMS - From Evaluations to Design Choices | 2023 | SIGMOD | 5.0589805e-05 |
| 5,074 | Learned Index: A Comprehensive Experimental Evaluation | 2023 | VLDB | 5.7175726e-05 |
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| 10,038 | Understanding Robustness Issues of Updatable Learned Indexes: [Experiments & Analysis] | 2026 | SIGMOD | 4.1945683e-05 |