Database Paper Browser

Back to papers

Information-Theoretic Tools for Mining Database Structure from Large Data Sets

Summary: Information-theoretic summaries infer database structure when the model is unknown or incomplete, robust to noise, missing values, and duplicates. Scalable algorithms extract these summaries from large categorical data; ranking functional dependencies by redundancy guides vertical decompositions that boost information content, with real-data validation. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3563
Venue
SIGMOD
Year
2004
Pagerank
0.00010126101
Overall Rank
1,908 | 86.73%
DOI
-

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 11 of 11 citing papers.

Rank Citing Paper Year Venue Pagerank
492 Query by Output 2009 SIGMOD 0.00021974699
1,153 SQAK: Doing More with Keywords 2008 SIGMOD 0.00013642866
1,762 Tuning Schema Matching Software using Synthetic Scenarios 2005 VLDB 0.00010646894
2,066 DBLife: A Community Information Management Platform for the Database Research Community 2007 CIDR 9.6399561e-05
3,426 Discovering Topical Structures of Databases 2008 SIGMOD 7.1063105e-05
3,467 Data Profiling – A Tutorial 2017 SIGMOD 7.069081e-05
7,571 Reducing Ambiguity in Json Schema Discovery 2021 SIGMOD 4.7075853e-05
8,044 Information Theory for Data Management 2010 SIGMOD 4.5993522e-05
10,791 FDepHunter: Harnessing Negative Examples to Expose Fakes and Reveal Ghosts 2025 VLDB 4.1945683e-05
11,366 Statistical Schema Learning using Occam's Razor 2022 SIGMOD 4.1945683e-05
12,355 Information Theory For Data Management 2009 VLDB 4.1945683e-05
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 12 of 12 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers