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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Authors
- 1. Stefan Hermann (Karlsruhe Institute of Technology)
- 2. Hans-Peter Lehmann (Karlsruhe Institute of Technology)
- 3. Giorgio Vinciguerra (University of Pisa)
- 4. Stefan Walzer (Karlsruhe Institute of Technology)
BibTeX Citation
@article{hermann_vldb26,
title = {{Learned Static Function Data Structures}},
author = {Hermann, Stefan and Lehmann, Hans-Peter and Vinciguerra, Giorgio and Walzer, Stefan},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {5},
pages = {917--930},
doi = {10.14778/3796195.3796205},
url = {https://doi.org/10.14778/3796195.3796205},
year = {2026}
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 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 |
| 477 | The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds | 2020 | VLDB | 0.00017851226 |
| 1,655 | Efficient Parallel Lists Intersection and Index Compression Algorithms using Graphics Processing Units | 2011 | VLDB | 0.00010103504 |
| 2,611 | SPARTAN: A Model-Based Semantic Compression System for Massive Data Tables | 2001 | SIGMOD | 8.34729e-05 |
| 3,092 | DeepSqueeze: Deep Semantic Compression for Tabular Data | 2020 | SIGMOD | 7.7679406e-05 |
| 4,065 | LeCo: Lightweight Compression via Learning Serial Correlations | 2024 | SIGMOD | 6.930275e-05 |
| 4,780 | Can Learned Models Replace Hash Functions? | 2023 | VLDB | 6.5118885e-05 |
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