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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
12286
Venue
VLDB
Year
2020
Pagerank
5.4566641e-05
Overall Rank
5,529 | 61.54%
DOI
10.14778/3384345.33843446

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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
420 InfoGather: Entity Augmentation and Attribute Discovery By Holistic Matching with Web Tables 2012 SIGMOD 0.00023719065
610 Goods: Organizing Google's Datasets 2016 SIGMOD 0.00019232674
939 Data Lake Management: Challenges and Opportunities 2019 VLDB 0.00015187344
1,178 Table Union Search on Open Data 2018 VLDB 0.00013468118
2,209 Data Integration: After the Teenage Years 2017 PODS 9.2868035e-05
3,281 Constance: An Intelligent Data Lake System 2016 SIGMOD 7.2823287e-05
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