LITS: An Optimized Learned Index for Strings
Summary: LITS targets variable-length, skewed strings with a hash-enhanced prefix table plus per-node linear models and compact hybrid subtries, reducing learned-index last-mile costs. It outperforms HOT/ART by up to 2.43×/2.27× on point lookups while matching scan performance. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yifan Yang (State Key Laboratory of Processors, Institute of Computing Technology, Chinese Academy of Sciences; University of Chinese Academy of Sciences)
- 2. Shimin Chen (State Key Laboratory of Processors, Institute of Computing Technology, Chinese Academy of Sciences; University of Chinese Academy of Sciences)
BibTeX Citation
@article{yang_vldb24,
title = {{LITS: An Optimized Learned Index for Strings}},
author = {Yang, Yifan and Chen, Shimin},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {11},
pages = {3415--3427},
doi = {10.14778/3681954.3682010},
url = {https://doi.org/10.14778/3681954.3682010},
year = {2024}
}
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,262 | LINE: A Learned Index with Group-Enhanced Leaves and Cache-Optimized Inner Tree | 2026 | SIGMOD | 5.093636e-05 |
| 10,267 | Mathematical Foundations of Poisoning Attacks on Linear Regression over Cumulative Distribution Functions | 2026 | SIGMOD | 5.093636e-05 |
| 10,461 | HIRE: A Hybrid Learned Index for Robust and Efficient Performance under Mixed Workloads | 2026 | SIGMOD | 5.093636e-05 |
| 10,505 | The Case For Language Model Approximated LIKE Predicate | 2026 | SIGMOD | 5.093636e-05 |
| 10,617 | LiBox: A Learned Index as an Array to Minimize Last-Mile Search | 2026 | VLDB | 5.093636e-05 |
| 10,829 | FB+-tree: A Memory-Optimized B+-tree with Latch-Free Update | 2025 | VLDB | 5.093636e-05 |
| 10,955 | DobLIX: A Dual-Objective Learned Index for Log-Structured Merge Trees | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 43 | The Case for Learned Index Structures | 2018 | SIGMOD | 0.00046060254 |
| 447 | ALEX: An Updatable Adaptive Learned Index | 2020 | SIGMOD | 0.00018322593 |
| 882 | HOT: A Height Optimized Trie Index for Main-Memory Database Systems | 2018 | SIGMOD | 0.0001342403 |
| 1,551 | Updatable Learned Index with Precise Positions | 2021 | VLDB | 0.00010381398 |
| 2,233 | FINEdex: A Fine-grained Learned Index Scheme for Scalable and Concurrent Memory Systems | 2022 | VLDB | 8.8968964e-05 |
| 2,806 | Are Updatable Learned Indexes Ready? | 2022 | VLDB | 8.1013097e-05 |
| 3,865 | The Case for a Learned Sorting Algorithm | 2020 | SIGMOD | 7.0621718e-05 |
| 4,300 | DILI: A Distribution-Driven Learned Index | 2023 | VLDB | 6.773869e-05 |
| 5,285 | Order-Preserving Key Compression for In-Memory Search Trees | 2020 | SIGMOD | 6.2821588e-05 |
| 5,954 | When Tree Meets Hash: Reducing Random Reads for Index Structures on Persistent Memories | 2023 | SIGMOD | 6.0292226e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 882 | HOT: A Height Optimized Trie Index for Main-Memory Database Systems | 2018 | SIGMOD |
| 2 | 10,262 | LINE: A Learned Index with Group-Enhanced Leaves and Cache-Optimized Inner Tree | 2026 | SIGMOD |
| 3 | 8,591 | A New Paradigm in Tuning Learned Indexes: A Reinforcement Learning Enhanced Approach | 2025 | SIGMOD |
| 4 | 2,648 | On Effective Multi-Dimensional Indexing for Strings | 2000 | SIGMOD |
| 5 | 43 | The Case for Learned Index Structures | 2018 | SIGMOD |
| 6 | 4,660 | Hist-Tree: Those Who Ignore It Are Doomed to Learn | 2021 | CIDR |
| 7 | 847 | Benchmarking Learned Indexes | 2021 | VLDB |
| 8 | 7,368 | Accelerating String-key Learned Index Structures via Memoization-based Incremental Training | 2024 | VLDB |
| 9 | 1,551 | Updatable Learned Index with Precise Positions | 2021 | VLDB |
| 10 | 3,792 | Learned Index: A Comprehensive Experimental Evaluation | 2023 | VLDB |