Towards NLP-Enhanced Data Profiling Tools
Summary: Propose NLP-enhanced data profiling that leverages column/table name semantics to prioritize profiling methods and targets when time or compute budget is limited. Name-derived signals guide targeted analyses, reducing cost and improving relevance of discovered metadata. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Immanuel Trummer (Cornell University)
BibTeX Citation
@inproceedings{trummer_cidr22,
address = {Amsterdam, Netherlands},
series = {{CIDR} '22},
title = {{Towards NLP-Enhanced Data Profiling Tools}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Trummer, Immanuel},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,242 | CHORUS: Foundation Models for Unified Data Discovery and Exploration | 2024 | VLDB | 8.8823802e-05 |
| 5,354 | Can Large Language Models Predict Data Correlations from Column Names? | 2023 | VLDB | 6.2515841e-05 |
| 7,228 | Mind the Data Gap: Bridging LLMs to Enterprise Data Integration | 2025 | CIDR | 5.66667e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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