Learned Index: A Comprehensive Experimental Evaluation
Summary: Comprehensive empirical evaluation and taxonomy of learned indexes, dissecting design choices for position inference/refinement, inserts, concurrency, and bulk-loading within a unified testbed. Benchmarks state-of-the-art learned indexes under identical settings and gives actionable guidance for selection and design. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zhaoyan Sun
- 2. Xuanhe Zhou
- 3. Guoliang Li
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,038 | Understanding Robustness Issues of Updatable Learned Indexes: [Experiments & Analysis] | 2026 | SIGMOD | 4.1945683e-05 |
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| 6,445 | Updatable Learned Indexes Meet Disk-Resident DBMS - From Evaluations to Design Choices | 2023 | SIGMOD | 5.0589805e-05 |
| 5,337 | Learned Index Benefits: Machine Learning Based Index Performance Estimation | 2022 | VLDB | 5.5635208e-05 |
| 7,390 | Making In-Memory Learned Indexes Efficient on Disk | 2024 | SIGMOD | 4.7431654e-05 |
| 102 | The Case for Learned Index Structures | 2018 | SIGMOD | 0.00049545203 |
| 2,552 | Updatable Learned Index with Precise Positions | 2021 | VLDB | 8.5530411e-05 |
| 8,811 | Tuning Hierarchical Learned Indexes on Disk and Beyond | 2022 | SIGMOD | 4.4441574e-05 |
| 4,128 | Are Updatable Learned Indexes Ready? | 2022 | VLDB | 6.4292373e-05 |
| 1,460 | Benchmarking Learned Indexes | 2021 | VLDB | 0.00011887068 |