FREDDY: Fast Word Embeddings in Database Systems

Summary: FREDDY integrates word embeddings into PostgreSQL, exposing UDFs for novel embedding queries. It uses multiple indexes and approximation techniques to speed high-dimensional vector ops, demonstrated on IMDB and large word2vec models. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 5499
Venue: SIGMOD
Year: 2018
Pagerank: 9.2132247e-05
Overall Rank: 2,075 | 85.77%
DOI: 10.1145/3183713.3183717

Incoming Non-self Citations Over Time

Authors

1. Michael Günther (Technical University of Dresden)

BibTeX Citation

@inproceedings{gunther_sigmod18,
        title = {{FREDDY: Fast Word Embeddings in Database Systems}},
        author = {Günther, Michael},
        series = {{SIGMOD} '18},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3183713.3183717},
        url = {https://dl.acm.org/doi/10.1145/3183713.3183717},
        year = {2018}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
1,065	PASE: PostgreSQL Ultra-High-Dimensional Approximate Nearest Neighbor Search Extension	2020	SIGMOD	0.00012335063
1,402	Creating Embeddings of Heterogeneous Relational Datasets for Data Integration Tasks	2020	SIGMOD	0.00010888094
2,264	Exploiting Latent Information in Relational Databases via Word Embedding and Application to Degrees of Disclosure	2019	CIDR	8.8419988e-05
2,475	Deep Learning for Blocking in Entity Matching: A Design Space Exploration	2021	VLDB	8.5277654e-05

Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
21	Similarity Search in High Dimensions via Hashing	1999	VLDB	0.00056760516

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