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Simplifying Access to Large-scale Structured Datasets by Meta-Profiling with Scalable Training Set Enrichment

Summary: Large-scale structured datasets with millions of tables and diverse schemas hinder topic-centric discovery and querying. A deep-learning driven, unsupervised training-set enrichment yields Meta-profile, a standardized topic interface enabling access to all relevant topical tables across ultra-large corpora. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6371
Venue
SIGMOD
Year
2022
Pagerank
4.1945683e-05
Overall Rank
11,344 | 21.09%
DOI
10.1145/3514221.3520156

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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
107 WebTables: Exploring the Power of Tables on the Web 2008 VLDB 0.00048377684
188 Applying Model Management to Classical Meta Data Problems 2003 CIDR 0.00035968389
1,078 Model Management 2.0: Manipulating Richer Mappings 2007 SIGMOD 0.00014245848
12,324 IBM UFO Repository: Object-Oriented Data Integration 2009 VLDB 4.1945683e-05
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