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Discovering Topical Structures of Databases

Summary: iDisc uses a multi-strategy learning approach over schemas and data to cluster tables by topic with representations and meta-clustering. Extensible, it adds aggregation, clusterer boosting, a table-importance measure, and representations for semantic browsing, with strong accuracy on databases. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4112
Venue
SIGMOD
Year
2008
Pagerank
6.819835e-05
Overall Rank
4,228 | 71.00%
DOI
10.1145/1376616.1376717

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{wu_sigmod08,
        title = {{Discovering Topical Structures of Databases}},
        author = {Wu, Wensheng and Reinwald, Berthold and Sismanis, Yannis and Manjrekar, Rajesh},
        series = {{SIGMOD} '08},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1376616.1376717},
        url = {https://dl.acm.org/doi/10.1145/1376616.1376717},
        year = {2008}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
517 Query by Output 2009 SIGMOD 0.00017169735
3,572 Summary Graphs for Relational Database Schemas 2011 VLDB 7.2980153e-05
10,603 Schuyler: Self-Supervised Clustering of Tables in Relational Databases 2026 VLDB 5.093636e-05
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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.

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