Back to papers
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
- 7396
- Venue
- SIGMOD
- Year
- 2026
- Pagerank
- 4.1945683e-05
- Overall Rank
- 10,087 | 29.83%
- DOI
-
10.1145/3769800
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| 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 |
| 102 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049545203 |
| 333 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027206884 |
| 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 |
| 1,169 |
SuRF: Practical Range Query Filtering with Fast Succinct Tries |
2018 |
SIGMOD |
0.00013536447 |
| 1,375 |
FITing-Tree: A Data-aware Index Structure |
2019 |
SIGMOD |
0.00012303141 |
| 1,460 |
Benchmarking Learned Indexes |
2021 |
VLDB |
0.00011887068 |
| 1,889 |
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads |
2021 |
VLDB |
0.00010200865 |
| 2,115 |
LISA: A Learned Index Structure for Spatial Data |
2020 |
SIGMOD |
9.5257379e-05 |
| 2,552 |
Updatable Learned Index with Precise Positions |
2021 |
VLDB |
8.5530411e-05 |
| 3,131 |
FINEdex: A Fine-grained Learned Index Scheme for Scalable and Concurrent Memory Systems |
2022 |
VLDB |
7.4985793e-05 |
| 4,084 |
APEX: A High-Performance Learned Index on Persistent Memory |
2022 |
VLDB |
6.4622113e-05 |
| 4,097 |
The Case for a Learned Sorting Algorithm |
2020 |
SIGMOD |
6.4551616e-05 |
| 4,128 |
Are Updatable Learned Indexes Ready? |
2022 |
VLDB |
6.4292373e-05 |
| 4,646 |
CARMI: A Cache-Aware Learned Index with a Cost-based Construction Algorithm |
2022 |
VLDB |
6.0250374e-05 |
| 5,319 |
DILI: A Distribution-Driven Learned Index |
2023 |
VLDB |
5.5713974e-05 |
| 5,572 |
The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data |
2023 |
SIGMOD |
5.4277273e-05 |
| 5,642 |
NFL: Robust Learned Index via Distribution Transformation |
2022 |
VLDB |
5.3929294e-05 |
| 6,445 |
Updatable Learned Indexes Meet Disk-Resident DBMS - From Evaluations to Design Choices |
2023 |
SIGMOD |
5.0589805e-05 |
| 6,492 |
FILM: a Fully Learned Index for Larger-than-Memory Databases |
2023 |
VLDB |
5.042727e-05 |
| 7,869 |
SALI: A Scalable Adaptive Learned Index Framework based on Probability Models |
2023 |
SIGMOD |
4.6315248e-05 |
| 8,101 |
Hyper: A High-Performance and Memory-Efficient Learned Index via Hybrid Construction |
2024 |
SIGMOD |
4.5854141e-05 |
| 8,222 |
Sieve: A Learned Data-Skipping Index for Data Analytics |
2023 |
VLDB |
4.5555621e-05 |
| 8,636 |
WISK: A Workload-aware Learned Index for Spatial Keyword Queries |
2023 |
SIGMOD |
4.4801284e-05 |
| 8,671 |
Algorithmic Complexity Attacks on Dynamic Learned Indexes |
2024 |
VLDB |
4.4714076e-05 |
| 9,285 |
PriPL-Tree: Accurate Range Query for Arbitrary Distribution under Local Differential Privacy |
2024 |
VLDB |
4.3623546e-05 |
| 9,827 |
PLATON: Top-down R-tree Packing with Learned Partition Policy |
2023 |
SIGMOD |
4.2751057e-05 |
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 6,445 |
Updatable Learned Indexes Meet Disk-Resident DBMS - From Evaluations to Design Choices |
2023 |
SIGMOD |
5.0589805e-05 |
| 8,655 |
Adaptive Hybrid Indexes |
2022 |
SIGMOD |
4.4739554e-05 |
| 8,671 |
Algorithmic Complexity Attacks on Dynamic Learned Indexes |
2024 |
VLDB |
4.4714076e-05 |
| 10,038 |
Understanding Robustness Issues of Updatable Learned Indexes: [Experiments & Analysis] |
2026 |
SIGMOD |
4.1945683e-05 |
| 7,390 |
Making In-Memory Learned Indexes Efficient on Disk |
2024 |
SIGMOD |
4.7431654e-05 |
| 8,101 |
Hyper: A High-Performance and Memory-Efficient Learned Index via Hybrid Construction |
2024 |
SIGMOD |
4.5854141e-05 |
| 2,552 |
Updatable Learned Index with Precise Positions |
2021 |
VLDB |
8.5530411e-05 |
| 5,074 |
Learned Index: A Comprehensive Experimental Evaluation |
2023 |
VLDB |
5.7175726e-05 |
| 1,460 |
Benchmarking Learned Indexes |
2021 |
VLDB |
0.00011887068 |
| 4,128 |
Are Updatable Learned Indexes Ready? |
2022 |
VLDB |
6.4292373e-05 |