MosaicJoin: Compact Semantic Sketches for Value-Level Join Discovery
Summary: MosaicJoin uses compact value-level semantic sketches to approximate column joinability, preserving fine-grained alignment without exhaustive comparisons. Training-free query subsampling provides accuracy guarantees and up to 66× faster retrieval, scaling to million-value columns. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Grace Fan (New York University)
- 2. Eden Wu (New York University)
- 3. Majid Daliri (New York University)
- 4. Juliana Freire (New York University)
BibTeX Citation
@article{fan_vldb26,
title = {{MosaicJoin: Compact Semantic Sketches for Value-Level Join Discovery}},
author = {Fan, Grace and Wu, Eden and Daliri, Majid and Freire, Juliana},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {11},
pages = {3745--3758},
doi = {10.14778/3836663.3836722},
url = {https://doi.org/10.14778/3836663.3836722},
year = {2026}
}Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,047 | Semantic Data Systems: From Data Management to Data Understanding | 2026 | VLDB | 4.9769913e-05 |
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