A Critical Analysis of Recursive Model Indexes
Summary: Broad, inventor-independent analysis of Recursive Model Indexes reveals that hyperparameters beyond model type and layer size--notably error bounds and search strategies--impact performance. A simple configuration guideline delivers competitive RMIs and learned indexes without costly enumeration, with 2.5x--6.3x build-time speedups. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Marcel Maltry
- 2. Jens Dittrich
Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,128 | Are Updatable Learned Indexes Ready? | 2022 | VLDB | 6.4292373e-05 |
| 5,074 | Learned Index: A Comprehensive Experimental Evaluation | 2023 | VLDB | 5.7175726e-05 |
| 6,297 | Towards instance-optimized data systems | 2021 | VLDB | 5.1227886e-05 |
| 7,869 | SALI: A Scalable Adaptive Learned Index Framework based on Probability Models | 2023 | SIGMOD | 4.6315248e-05 |
| 8,417 | The Case for Learned In-Memory Joins | 2023 | VLDB | 4.5194164e-05 |
| 9,346 | Can Learned Indexes be Built Efficiently? A Deep Dive into Sampling Trade-offs | 2024 | SIGMOD | 4.3532026e-05 |
| 10,038 | Understanding Robustness Issues of Updatable Learned Indexes: [Experiments & Analysis] | 2026 | SIGMOD | 4.1945683e-05 |
| 10,396 | VEGA: An Active-tuning Learned Index with Group-Wise Learning Granularity | 2025 | SIGMOD | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 102 | The Case for Learned Index Structures | 2018 | SIGMOD | 0.00049545203 |
| 183 | Automatic Database Management System Tuning Through Large-scale Machine Learning | 2017 | SIGMOD | 0.00036721403 |
| 359 | Self-Driving Database Management Systems | 2017 | CIDR | 0.0002592783 |
| 381 | FAST: Fast Architecture Sensitive Tree Search on Modern CPUs and GPUs | 2010 | SIGMOD | 0.00024873637 |
| 608 | DeepDB: Learn from Data, not from Queries! | 2020 | VLDB | 0.00019235898 |
| 826 | ALEX: An Updatable Adaptive Learned Index | 2020 | SIGMOD | 0.00016224841 |
| 857 | The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds | 2020 | VLDB | 0.00015882892 |
| 1,375 | FITing-Tree: A Data-aware Index Structure | 2019 | SIGMOD | 0.00012303141 |
| 1,460 | Benchmarking Learned Indexes | 2021 | VLDB | 0.00011887068 |
| 4,060 | CDFShop: Exploring and Optimizing Learned Index Structures | 2020 | SIGMOD | 6.4836825e-05 |
| 5,157 | Hist-Tree: Those Who Ignore It Are Doomed to Learn | 2021 | CIDR | 5.6589595e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,157 | Hist-Tree: Those Who Ignore It Are Doomed to Learn | 2021 | CIDR | 5.6589595e-05 |
| 10,748 | Benchmarking Adaptive Multidimensional Indices | 2025 | VLDB | 4.1945683e-05 |
| 5,337 | Learned Index Benefits: Machine Learning Based Index Performance Estimation | 2022 | VLDB | 5.5635208e-05 |
| 8,101 | Hyper: A High-Performance and Memory-Efficient Learned Index via Hybrid Construction | 2024 | SIGMOD | 4.5854141e-05 |
| 4,060 | CDFShop: Exploring and Optimizing Learned Index Structures | 2020 | SIGMOD | 6.4836825e-05 |
| 5,428 | The Price of Tailoring the Index to Your Data: Poisoning Attacks on Learned Index Structures | 2022 | SIGMOD | 5.5091613e-05 |
| 5,074 | Learned Index: A Comprehensive Experimental Evaluation | 2023 | VLDB | 5.7175726e-05 |
| 102 | The Case for Learned Index Structures | 2018 | SIGMOD | 0.00049545203 |
| 1,460 | Benchmarking Learned Indexes | 2021 | VLDB | 0.00011887068 |
| 4,646 | CARMI: A Cache-Aware Learned Index with a Cost-based Construction Algorithm | 2022 | VLDB | 6.0250374e-05 |