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LDA*: A Robust and Large-scale Topic Modeling System

Summary: LDA* is a production topic-modeling service scaling to 1.8TB datasets across thousands of heterogeneous machines. It combines a document-length-aware hybrid sampler with an asymmetric parameter server, exploiting workload and communication tradeoffs for up to 10× speedups. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
11613
Venue
VLDB
Year
2017
Pagerank
5.093636e-05
Overall Rank
11,999 | 17.68%
DOI
10.14778/3137628.3137649

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Authors

BibTeX Citation

@article{yu_vldb17,
        title = {{LDA*: A Robust and Large-scale Topic Modeling System}},
        author = {Yu, Lele and Zhang, Ce and Shao, Yingxia and Cui, Bin},
        journal = {PVLDB},
        series = {{VLDB} '17},
        volume = {10},
        number = {11},
        pages = {1406--1417},
        doi = {10.14778/3137628.3137649},
        url = {https://doi.org/10.14778/3137628.3137649},
        year = {2017}
}

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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
452 An Architecture for Parallel Topic Models 2010 VLDB 0.00018146809
2,162 Heterogeneity-aware Distributed Parameter Servers 2017 SIGMOD 9.0581831e-05
6,969 WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation 2016 VLDB 5.7303405e-05
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