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Dedoop: Efficient Deduplication with Hadoop

Summary: Dedoop offers browser-based composition of MapReduce entity-resolution workflows, including blocking, matching, and learned classifiers. It compiles them to Hadoop jobs with load balancing and redundant-comparison avoidance for scalable, observable deduplication. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
10613
Venue
VLDB
Year
2012
Pagerank
9.2512023e-05
Overall Rank
2,055 | 85.91%
DOI
10.14778/2367502.2367523

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{kolb_vldb12,
        title = {{Dedoop: Efficient Deduplication with Hadoop}},
        author = {Kolb, Lars and Thor, Andreas and Rahm, Erhard},
        journal = {PVLDB},
        series = {{VLDB} '12},
        volume = {5},
        number = {12},
        pages = {1878--1881},
        doi = {10.14778/2367502.2367523},
        url = {https://doi.org/10.14778/2367502.2367523},
        year = {2012}
}

Incoming Citations (Sorted by Pagerank)

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Outgoing Citations (Sorted by Pagerank)

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

Rank Cited Paper Year Venue Pagerank
248 Evaluation of entity resolution approaches on real-world match problems 2010 VLDB 0.00023278354
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