Making In-Memory Learned Indexes Efficient on Disk
Summary: Shows how to adapt in-memory learned indexes to disk via six generic transformations/optimizations tailored to storage behavior. The resulting indexes Pareto-dominate B+trees and prior disk learned indexes in throughput and space efficiency. (summarized by gpt-5.4-mini on May 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jiaoyi Zhang
- 2. Kai Su
- 3. Huanchen Zhang
Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,746 | Why Are Learned Indexes So Effective but Sometimes Ineffective? | 2025 | VLDB | 4.2897489e-05 |
| 10,038 | Understanding Robustness Issues of Updatable Learned Indexes: [Experiments & Analysis] | 2026 | SIGMOD | 4.1945683e-05 |
| 10,172 | HIRE: A Hybrid Learned Index for Robust and Efficient Performance under Mixed Workloads | 2026 | SIGMOD | 4.1945683e-05 |
| 10,396 | VEGA: An Active-tuning Learned Index with Group-Wise Learning Granularity | 2025 | SIGMOD | 4.1945683e-05 |
| 10,484 | Femur: A Flexible Framework for Fast and Secure Querying from Public Key-Value Store | 2025 | SIGMOD | 4.1945683e-05 |
| 10,562 | FB+-tree: A Memory-Optimized B+-tree with Latch-Free Update | 2025 | VLDB | 4.1945683e-05 |
| 10,712 | DobLIX: A Dual-Objective Learned Index for Log-Structured Merge Trees | 2025 | VLDB | 4.1945683e-05 |
| 10,981 | Enabling Adaptive Sampling for Intra-Window Join: Simultaneously Optimizing Quantity and Quality | 2024 | SIGMOD | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 25 of 25 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,076 | Accelerating String-key Learned Index Structures via Memoization-based Incremental Training | 2024 | VLDB | 4.5917398e-05 |
| 10,087 | High Performance or Low Memory? An Updatable Learned Index Framework for Time-Space Tradeoff | 2026 | SIGMOD | 4.1945683e-05 |
| 9,746 | Why Are Learned Indexes So Effective but Sometimes Ineffective? | 2025 | VLDB | 4.2897489e-05 |
| 8,474 | Adaptive Index Structures | 2002 | VLDB | 4.5029015e-05 |
| 4,128 | Are Updatable Learned Indexes Ready? | 2022 | VLDB | 6.4292373e-05 |
| 102 | The Case for Learned Index Structures | 2018 | SIGMOD | 0.00049545203 |
| 5,074 | Learned Index: A Comprehensive Experimental Evaluation | 2023 | VLDB | 5.7175726e-05 |
| 8,811 | Tuning Hierarchical Learned Indexes on Disk and Beyond | 2022 | SIGMOD | 4.4441574e-05 |
| 1,460 | Benchmarking Learned Indexes | 2021 | VLDB | 0.00011887068 |
| 6,445 | Updatable Learned Indexes Meet Disk-Resident DBMS - From Evaluations to Design Choices | 2023 | SIGMOD | 5.0589805e-05 |