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Data-Driven Domain Discovery for Structured Datasets

Summary: Data-driven domain discovery across heterogeneous tables uses cross-column value co-occurrence to derive context signatures and infer attribute domains. Robust to incomplete or noisy data, scales to millions of terms, and outperforms state-of-the-art on real urban datasets, enabling richer queries and integration. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12474
Venue
VLDB
Year
2020
Pagerank
6.356638e-05
Overall Rank
5,125 | 64.84%
DOI
10.14778/3384345.3384346

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{ota_vldb20,
        title = {{Data-Driven Domain Discovery for Structured Datasets}},
        author = {Ota, Masayo and Müller, Heiko and Freire, Juliana and Srivastava, Divesh},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {7},
        pages = {953--965},
        doi = {10.14778/3384345.3384346},
        url = {https://doi.org/10.14778/3384345.3384346},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 7 of 7 citing papers.

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Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

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

Rank Cited Paper Year Venue Pagerank
367 InfoGather: Entity Augmentation and Attribute Discovery By Holistic Matching with Web Tables 2012 SIGMOD 0.00019979463
514 Goods: Organizing Google's Datasets 2016 SIGMOD 0.00017178673
749 Data Lake Management: Challenges and Opportunities 2019 VLDB 0.00014379989
878 Table Union Search on Open Data 2018 VLDB 0.00013463374
2,169 Data Integration: After the Teenage Years 2017 PODS 9.0436759e-05
2,913 Constance: An Intelligent Data Lake System 2016 SIGMOD 7.9684737e-05
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