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)
Incoming Non-self Citations Over Time
Authors
- 1. Lars Kolb (University of Leipzig)
- 2. Andreas Thor (University of Leipzig)
- 3. Erhard Rahm (University of Leipzig)
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)
Showing 14 of 14 citing papers.
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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 |
|---|---|---|---|---|
| 248 | Evaluation of entity resolution approaches on real-world match problems | 2010 | VLDB | 0.00023278354 |
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