45 citations · 52 across the 6 of their papers we have counts for
6 papers · 1 filter
AutoDDG: Automated Dataset Description Generation using Large Language Models
Haoxiang Zhang, Yurong Liu, Aécio Santos +2
The proliferation of datasets across open data portals and enterprise data lakes presents an opportunity for deriving data-driven insights. Widely-used dataset search systems rely…
Magneto: Combining Small and Large Language Models for Schema Matching
Yurong Liu, Eduardo Pena, Aecio Santos +2
Recent advances in language models opened new opportunities to address complex schema matching tasks. Schema matching approaches have been proposed that demonstrate the usefulness…
Efficiently Estimating Mutual Information Between Attributes Across Tables
Aécio Santos, Flip Korn, Juliana Freire
Relational data augmentation is a powerful technique for enhancing data analytics and improving machine learning models by incorporating columns from external datasets. However, it…
Sampling Methods for Inner Product Sketching
Majid Daliri, Juliana Freire, Christopher Musco +2
Recently, Bessa et al. (PODS 2023) showed that sketches based on coordinated weighted sampling theoretically and empirically outperform popular linear sketching methods like Johnso…
Weighted Minwise Hashing Beats Linear Sketching for Inner Product Estimation
Aline Bessa, Majid Daliri, Juliana Freire +4
We present a new approach for computing compact sketches that can be used to approximate the inner product between pairs of high-dimensional vectors. Based on the Weighted MinHash…
Correlation Sketches for Approximate Join-Correlation Queries
Aécio Santos, Aline Bessa, Fernando Chirigati +2
The increasing availability of structured datasets, from Web tables and open-data portals to enterprise data, opens up opportunities~to enrich analytics and improve machine learnin…