Accelerating String-key Learned Index Structures via Memoization-based Incremental Training
Summary: Memoizes QR decomposition to incrementally retrain string-key learned indexes by touching only updated keys, eliminating dependence on total data size. FPGA offload speeds training and frees CPU; SIA boosts throughput 2.6–3.4× vs ALEX/LIPP/SIndex. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Minsu Kim
- 2. Jinwoo Hwang
- 3. Guseul Heo
- 4. Seiyeon Cho
- 5. Divya Mahajan
- 6. Jongse Park
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,618 | A New Paradigm in Tuning Learned Indexes: A Reinforcement Learning Enhanced Approach | 2025 | SIGMOD | 4.3131993e-05 |
| 10,331 | LiBox: A Learned Index as an Array to Minimize Last-Mile Search | 2026 | VLDB | 4.1905499e-05 |
| 10,407 | VEGA: An Active-tuning Learned Index with Group-Wise Learning Granularity | 2025 | SIGMOD | 4.1905499e-05 |
| 10,571 | FB+-tree: A Memory-Optimized B+-tree with Latch-Free Update | 2025 | VLDB | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 29 of 29 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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