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High Performance or Low Memory? An Updatable Learned Index Framework for Time-Space Tradeoff
Summary: Introduces LIFT, an updatable learned-index framework that derives theoretical time–space correlation models and minimizes a time-space cost function to navigate the performance vs. memory tradeoff. Adds structural adjustments to resist dense/duplicate inserts and poisoning attacks, yielding robust, consistently optimal time-space tradeoffs and outperforming prior learned and traditional indexes.
(summarized by gpt-5-mini on Feb 11 2026)
- Paper ID
- 7397
- Venue
- SIGMOD
- Year
- 2026
- Pagerank
- 4.1905499e-05
- Overall Rank
- 10,087 | 29.90%
- DOI
-
10.1145/3769800
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Incoming Citations (Sorted by Pagerank)
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| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
Outgoing Citations (Sorted by Pagerank)
Showing 27 of 27 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 101 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049778866 |
| 329 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027301488 |
| 819 |
ALEX: An Updatable Adaptive Learned Index |
2020 |
SIGMOD |
0.00016237497 |
| 844 |
The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds |
2020 |
VLDB |
0.00015964123 |
| 1,169 |
SuRF: Practical Range Query Filtering with Fast Succinct Tries |
2018 |
SIGMOD |
0.00013530267 |
| 1,365 |
FITing-Tree: A Data-aware Index Structure |
2019 |
SIGMOD |
0.00012379754 |
| 1,438 |
Benchmarking Learned Indexes |
2021 |
VLDB |
0.00011965956 |
| 1,887 |
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads |
2021 |
VLDB |
0.00010201938 |
| 2,108 |
LISA: A Learned Index Structure for Spatial Data |
2020 |
SIGMOD |
9.5283642e-05 |
| 2,550 |
Updatable Learned Index with Precise Positions |
2021 |
VLDB |
8.5569576e-05 |
| 3,136 |
FINEdex: A Fine-grained Learned Index Scheme for Scalable and Concurrent Memory Systems |
2022 |
VLDB |
7.4926368e-05 |
| 3,945 |
APEX: A High-Performance Learned Index on Persistent Memory |
2022 |
VLDB |
6.605467e-05 |
| 4,056 |
Are Updatable Learned Indexes Ready? |
2022 |
VLDB |
6.4905689e-05 |
| 4,086 |
The Case for a Learned Sorting Algorithm |
2020 |
SIGMOD |
6.4579358e-05 |
| 4,830 |
CARMI: A Cache-Aware Learned Index with a Cost-based Construction Algorithm |
2022 |
VLDB |
5.8884997e-05 |
| 5,327 |
DILI: A Distribution-Driven Learned Index |
2023 |
VLDB |
5.5660777e-05 |
| 5,463 |
The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data |
2023 |
SIGMOD |
5.4920768e-05 |
| 5,655 |
NFL: Robust Learned Index via Distribution Transformation |
2022 |
VLDB |
5.3877506e-05 |
| 6,442 |
Updatable Learned Indexes Meet Disk-Resident DBMS - From Evaluations to Design Choices |
2023 |
SIGMOD |
5.0541252e-05 |
| 6,488 |
FILM: a Fully Learned Index for Larger-than-Memory Databases |
2023 |
VLDB |
5.0378878e-05 |
| 7,870 |
SALI: A Scalable Adaptive Learned Index Framework based on Probability Models |
2023 |
SIGMOD |
4.6271153e-05 |
| 8,100 |
Hyper: A High-Performance and Memory-Efficient Learned Index via Hybrid Construction |
2024 |
SIGMOD |
4.5821762e-05 |
| 8,221 |
Sieve: A Learned Data-Skipping Index for Data Analytics |
2023 |
VLDB |
4.5511941e-05 |
| 8,636 |
WISK: A Workload-aware Learned Index for Spatial Keyword Queries |
2023 |
SIGMOD |
4.4758336e-05 |
| 8,668 |
Algorithmic Complexity Attacks on Dynamic Learned Indexes |
2024 |
VLDB |
4.4671214e-05 |
| 9,290 |
PriPL-Tree: Accurate Range Query for Arbitrary Distribution under Local Differential Privacy |
2024 |
VLDB |
4.358174e-05 |
| 9,826 |
PLATON: Top-down R-tree Packing with Learned Partition Policy |
2023 |
SIGMOD |
4.2710095e-05 |
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