SWIX: A Memory-efficient Sliding Window Learned Index
Summary: SWIX is a memory-efficient, flat learned index for sliding-window streams, replacing tree-based indexes to reduce memory while preserving fast query times. It adapts to real-time distribution shifts and, for concurrent workloads, delivers up to 3.45× throughput with 34% of the memory, outperforming state-of-the-art approaches (22–42% footprint; up to 1.6× faster). (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Liang Liang (Imperial College London)
- 2. Guang Yang (Imperial College London)
- 3. Ali Hadian (Imperial College London)
- 4. Luis Alberto Croquevielle (Imperial College London)
- 5. Thomas Heinis (Imperial College London)
BibTeX Citation
@inproceedings{liang_sigmod24,
title = {{SWIX: A Memory-efficient Sliding Window Learned Index}},
author = {Liang, Liang and Yang, Guang and Hadian, Ali and Croquevielle, Luis Alberto and Heinis, Thomas},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3639296},
url = {https://dl.acm.org/doi/10.1145/3639296},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,591 | A New Paradigm in Tuning Learned Indexes: A Reinforcement Learning Enhanced Approach | 2025 | SIGMOD | 5.4059856e-05 |
| 10,262 | LINE: A Learned Index with Group-Enhanced Leaves and Cache-Optimized Inner Tree | 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 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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