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)
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Authors
- 1. Liang Liang
- 2. Guang Yang
- 3. Ali Hadian
- 4. Luis Alberto Croquevielle
- 5. Thomas Heinis
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,618 | A New Paradigm in Tuning Learned Indexes: A Reinforcement Learning Enhanced Approach | 2025 | SIGMOD | 4.3131993e-05 |
| 10,172 | HIRE: A Hybrid Learned Index for Robust and Efficient Performance under Mixed Workloads | 2026 | SIGMOD | 4.1905499e-05 |
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