Sort it Like You Mean It: Discovering Semantically Interesting Attribute Augmentations to Sort Tables
Summary: InsightSort automates semantically meaningful table sorting by discovering attribute augmentations through data-lake table linking. An LLM then synthesizes diverse top-k sorting attributes, enabling richer exploratory analysis than manual sort design. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Akash Khatri (University of Utah)
- 2. Mir Mahathir Mohammad (University of Utah)
- 3. El Kindi Rezig (University of Utah)
BibTeX Citation
@article{khatri_vldb25,
title = {{Sort it Like You Mean It: Discovering Semantically Interesting Attribute Augmentations to Sort Tables}},
author = {Khatri, Akash and Mohammad, Mir Mahathir and Rezig, El Kindi},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {12},
pages = {5427--5430},
doi = {10.14778/3750601.3750688},
url = {https://doi.org/10.14778/3750601.3750688},
year = {2025}
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 624 | Data Lake Management: Challenges and Opportunities | 2019 | VLDB | 0.00015476813 |
| 2,378 | DeepJoin: Joinable Table Discovery with Pre-trained Language Models | 2023 | VLDB | 8.5495442e-05 |
| 7,220 | DICE: Data Discovery by Example | 2021 | VLDB | 5.5806325e-05 |
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