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)
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 |
|---|---|---|---|---|
| 916 | PASE: PostgreSQL Ultra-High-Dimensional Approximate Nearest Neighbor Search Extension | 2020 | SIGMOD | 0.00013094482 |
| 1,391 | Creating Embeddings of Heterogeneous Relational Datasets for Data Integration Tasks | 2020 | SIGMOD | 0.00010816237 |
| 2,281 | Exploiting Latent Information in Relational Databases via Word Embedding and Application to Degrees of Disclosure | 2019 | CIDR | 8.7019033e-05 |
| 2,514 | Deep Learning for Blocking in Entity Matching: A Design Space Exploration | 2021 | VLDB | 8.3648432e-05 |
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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 |
|---|---|---|---|---|
| 20 | Similarity Search in High Dimensions via Hashing | 1999 | VLDB | 0.00057568153 |
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