Learned Static Function Data Structures
Summary: Learned static functions: ML predicts per-key value distributions, then encodes each value with a key-specific prefix code stored in a static function structure. Breaks the zero-order entropy barrier for point queries, yielding major space savings over compressed static functions. (summarized by gpt-5.4-mini on Apr 12 2026)
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 101 | The Case for Learned Index Structures | 2018 | SIGMOD | 0.00049778866 |
| 844 | The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds | 2020 | VLDB | 0.00015964123 |
| 2,046 | Efficient Parallel Lists Intersection and Index Compression Algorithms using Graphics Processing Units | 2011 | VLDB | 9.6922338e-05 |
| 2,906 | SPARTAN: A Model-Based Semantic Compression System for Massive Data Tables | 2001 | SIGMOD | 7.9324961e-05 |
| 3,419 | LeCo: Lightweight Compression via Learning Serial Correlations | 2024 | SIGMOD | 7.1127752e-05 |
| 3,741 | DeepSqueeze: Deep Semantic Compression for Tabular Data | 2020 | SIGMOD | 6.7952067e-05 |
| 5,323 | Can Learned Models Replace Hash Functions? | 2023 | VLDB | 5.5671086e-05 |
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