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Exploiting Latent Information in Relational Databases via Word Embedding and Application to Degrees of Disclosure

Summary: Textify relations and train word embeddings on database tokens to expose cross-attribute latent semantics (similarity, analogy, induction) to SQL via UDFs. Apply this cognitive DB for policy-driven degrees of disclosure and semantic sharing, noting engine-integration challenges and theoretical limits of embedding coding. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
329
Venue
CIDR
Year
2019
Pagerank
8.8419988e-05
Overall Rank
2,264 | 84.47%
DOI
10.1145/nnnnnnn.nnnnnnn

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{bordawekar_cidr19,
        address = {Amsterdam, Netherlands},
        series = {{CIDR} '19},
        title = {{Exploiting Latent Information in Relational Databases via Word Embedding and Application to Degrees of Disclosure}},
        booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
        author = {Bordawekar, Rajesh and Shmueli, Oded},
        year = {2019}
}

Incoming Citations (Sorted by Pagerank)

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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
2,075 FREDDY: Fast Word Embeddings in Database Systems 2018 SIGMOD 9.2132247e-05
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