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Entity Matching Meets Data Science: A Progress Report from the Magellan Project

Summary: Magellan treats entity matching as a data-science ecosystem with interoperable tools (PyMatcher, CloudMatcher) for power and lay users. Over 3.5 years, it reports production deployments across 21 EM tasks in 12 companies and a cloud-native, Docker/Kubernetes ecosystem. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5716
Venue
SIGMOD
Year
2019
Pagerank
5.6311132e-05
Overall Rank
7,373 | 49.42%
DOI
10.1145/3299869.3314042

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{govind_sigmod19,
        title = {{Entity Matching Meets Data Science: A Progress Report from the Magellan Project}},
        author = {Govind, Yash and Konda, Pradap and C., Paul Suganthan G. and Martinkus, Philip and Nagarajan, Palaniappan and Li, Han and Soundararajan, Aravind and Mudgal, Sidharth and Ballard, Jeffrey R. and Zhang, Haojun and Ardalan, Adel and Das, Sanjib and Paulsen, Derek and Saini, Amanpreet and Paulson, Erik and Park, Youngchoon and Carter, Marshall and Sun, Mingju and Fung, Glenn M. and Doan, AnHai},
        series = {{SIGMOD} '19},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3299869.3314042},
        url = {https://dl.acm.org/doi/10.1145/3299869.3314042},
        year = {2019}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
4,905 Medical Entity Disambiguation Using Graph Neural Networks 2021 SIGMOD 6.4497268e-05
6,167 Sparkly: A Simple yet Surprisingly Strong TF/IDF Blocker for Entity Matching 2023 VLDB 5.9524736e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

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

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