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)
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Authors
- 1. Sophie Pavia (Florida State University)
- 2. Rituparna Khan (Florida State University)
- 3. Anna Pyayt (University of South Florida)
- 4. Michael Gubanov (Florida State University)
BibTeX Citation
@inproceedings{pavia_sigmod22,
title = {{Simplifying Access to Large-scale Structured Datasets by Meta-Profiling with Scalable Training Set Enrichment}},
author = {Pavia, Sophie and Khan, Rituparna and Pyayt, Anna and Gubanov, Michael},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3520156},
url = {https://dl.acm.org/doi/10.1145/3514221.3520156},
year = {2022}
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 91 | WebTables: Exploring the Power of Tables on the Web | 2008 | VLDB | 0.00034838835 |
| 183 | Applying Model Management to Classical Meta Data Problems | 2003 | CIDR | 0.00026283359 |
| 762 | Model Management 2.0: Manipulating Richer Mappings | 2007 | SIGMOD | 0.00014231163 |
| 12,518 | IBM UFO Repository: Object-Oriented Data Integration | 2009 | VLDB | 5.093636e-05 |
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