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Fast-ER: GPU-Accelerated Record Linkage and Deduplication in Python Cover

Fast-ER: GPU-Accelerated Record Linkage and Deduplication in Python

Open Access
|Apr 2026

References

  1. Okuta R, Unno Y, Nishino D, Hido S, Loomis C. CuPy: A NumPy-Compatible Library for NVIDIA GPU Calculations. Proceedings of Workshop on Machine Learning Systems (LearningSys) in The Thirty-First Annual Conference on Neural Information Processing Systems (NIPS); 2017. pp. 17.
  2. Enamorado T, Fifield B, Imai K. fastLink: Fast Probabilistic Record Linkage with Missing Data [online]; 2017. Available at: https://github.com/kosukeimai/fastLink.
  3. Enamorado T, Fifield B, Imai K. Using a Probabilistic Model to Assist Merging of Large-Scale Administrative Records. American Political Science Review. 2019;113(2):353379. DOI: 10.1017/S0003055418000783
  4. De Bruin J, et al. Python Record Linkage Toolkit: A toolkit for record linkage and duplicate detection in Python. Zenodo. 2019;v0.16. DOI: 10.5281/zenodo.8169000
  5. Linacre R, Lindsay S, Manassis T, Slade Z, Hepworth T. Splink: Free software for probabilistic record linkage at scale. International Journal of Population Data Science. 2022;7(3). DOI: 10.23889/ijpds.v7i3.1794
  6. Kim SS, Schneider S, Alvarez RM. Evaluating the Quality of Changes in Voter Registration Databases. American Politics Research. 2019;48(6):113. DOI: 10.1177/1532673X19870512
  7. The Apache Software Foundation. Apache Airflow [online]; 2025. Available at: https://airflow.apache.org.
DOI: https://doi.org/10.5334/jors.556 | Journal eISSN: 2049-9647
Language: English
Submitted on: Jan 28, 2025
Accepted on: Apr 7, 2026
Published on: Apr 21, 2026
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2026 Jacob Morrier, Sulekha Kishore, R. Michael Alvarez, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.