CDFShop: Exploring and Optimizing Learned Index Structures
Summary: CDFShop enables exploration and optimization of recursive model indexes (RMIs), a class of learned indexes, for data lookups. It exposes tuning knobs and automatic optimization to reveal space/time tradeoffs and gains over traditional B-trees. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ryan Marcus
- 2. Emily Zhang
- 3. Tim Kraska
Incoming Citations (Sorted by Pagerank)
Showing 19 of 19 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 826 | ALEX: An Updatable Adaptive Learned Index | 2020 | SIGMOD | 0.00016224841 |
| 1,087 | HOT: A Height Optimized Trie Index for Main-Memory Database Systems | 2018 | SIGMOD | 0.00014162909 |
| 1,478 | Learning Multi-dimensional Indexes | 2020 | SIGMOD | 0.00011762542 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,678 | Effectively Learning Spatial Indices | 2020 | VLDB | 8.3252088e-05 |
| 4,128 | Are Updatable Learned Indexes Ready? | 2022 | VLDB | 6.4292373e-05 |
| 10,087 | High Performance or Low Memory? An Updatable Learned Index Framework for Time-Space Tradeoff | 2026 | SIGMOD | 4.1945683e-05 |
| 12,095 | A Performance Study of Three Disk-based Structures for Indexing and Querying Frequent Itemsets | 2013 | VLDB | 4.1945683e-05 |
| 7,390 | Making In-Memory Learned Indexes Efficient on Disk | 2024 | SIGMOD | 4.7431654e-05 |
| 5,157 | Hist-Tree: Those Who Ignore It Are Doomed to Learn | 2021 | CIDR | 5.6589595e-05 |
| 6,724 | A Critical Analysis of Recursive Model Indexes | 2022 | VLDB | 4.9484506e-05 |
| 5,074 | Learned Index: A Comprehensive Experimental Evaluation | 2023 | VLDB | 5.7175726e-05 |
| 1,460 | Benchmarking Learned Indexes | 2021 | VLDB | 0.00011887068 |
| 102 | The Case for Learned Index Structures | 2018 | SIGMOD | 0.00049545203 |