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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
4050
Venue
SIGMOD
Year
2008
Pagerank
7.1063105e-05
Overall Rank
3,426 | 76.17%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
492 Query by Output 2009 SIGMOD 0.00021974699
1,796 Summary Graphs for Relational Database Schemas 2011 VLDB 0.00010524897
10,305 Schuyler: Self-Supervised Clustering of Tables in Relational Databases 2026 VLDB 4.1945683e-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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