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Chimera: Large-Scale Classification using Machine Learning, Rules, and Crowdsourcing

Summary: Chimera classifies tens of millions of products into 5,000+ types by integrating machine learning, analyst-authored rules, and crowdsourcing. Its WalmartLabs deployment shows that scale invalidates conventional assumptions, making hybrid workflows and large-scale rule management essential for accuracy, cost, and continual improvement. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
11001
Venue
VLDB
Year
2014
Pagerank
8.9303727e-05
Overall Rank
2,219 | 84.78%
DOI
10.14778/2733004.2733024

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{sun_vldb14,
        title = {{Chimera: Large-Scale Classification using Machine Learning, Rules, and Crowdsourcing}},
        author = {Sun, Chong and Rampalli, Narasimhan and Yang, Frank and Doan, AnHai},
        journal = {PVLDB},
        series = {{VLDB} '14},
        volume = {7},
        number = {13},
        pages = {1529--1540},
        doi = {10.14778/2733004.2733024},
        url = {https://doi.org/10.14778/2733004.2733024},
        year = {2014}
}

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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
439 Corleone: Hands-Off Crowdsourcing for Entity Matching 2014 SIGMOD 0.00018464913
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