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Magellan: Toward Building Entity Matching Management Systems

Summary: Magellan reframes entity matching as an end-to-end EM management system, not just algorithms. It offers step-by-step guides, a Python-based toolchain across the EM pipeline, and an interactive scripting environment, validated by 44 users. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11417
Venue
VLDB
Year
2016
Pagerank
0.00017096361
Overall Rank
529 | 96.38%
DOI
10.14778/2994509.2994518

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{konda_vldb16,
        title = {{Magellan: Toward Building Entity Matching Management Systems}},
        author = {Konda, Pradap and Das, Sanjib and G.C., Paul Suganthan and Doan, AnHai and Ardalan, Adel and Ballard, Jeffrey R. and Li, Han and Panahi, Fatemah and Zhang, Haojun and Naughton, Jeff and Prasad, Shishir and Krishnan, Ganesh and Deep, Rohit and Raghavendra, Vijay},
        journal = {PVLDB},
        series = {{VLDB} '16},
        volume = {9},
        number = {12},
        pages = {1197--1208},
        doi = {10.14778/2994509.2994518},
        url = {https://doi.org/10.14778/2994509.2994518},
        year = {2016}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 51 citing papers.

Rank Citing Paper Year Venue Pagerank
11,945 CloudMatcher: A Hands-Off Cloud/Crowd Service for Entity Matching 2018 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 10 of 10 cited papers.

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

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