Dependency Inference

Keywords

Computer science, Inference, Dependency (UML), Artificial intelligence

References

• Dependency Structures of Data Base RelationshipsWilliam W. Armstrong — 1974
• Accommodating Exceptions in Databases, and Refining the Schema by Learning from themAlexander Borgida, Keith E. Williamson — 1985
• Decomposing an N-ary relation into a tree of binary relationsRina Dechter — 1987
• Inclusion dependencies and their interaction with functional dependenciesMarco A. Casanova, Ronald Fagin, Christos H. Papadimitriou — 1982
• Formal limits on the automatic generation and maintenance of integrity constraintsJames P. Delgrande — 1987
• Design by Example: An Application of Armstrong RelationsHeikki Mannila, Kari‐Jouko Räihä — 1986
• On the Structure of Armstrong Relations for Functional DependenciesCatriel Beeri, M. Dowd, Ronald Fagin — 1984
• Database Design Tools: An Expert System ApproachMokrane Bouzeghoub, Georges Gardarin, Elisabeth Métais — 1985

Cited by

• Normal Form Relation Schemes: A New CharacterizationJános Demetrovics, Gusztáv Hencsey, Leonid Libkin — 1992
• Measuring data dependencies in large databasesGregory Piatetsky-Shapiro, Christopher J. Matheus — 1993
• Reducing End-User Burden in Everyday Data OrganizationQian Li — 2014
• Efficient discovery of functional and approximate dependencies using partitionsY. Huhtala, Y. Huhtala, Juha Kärkkäinen — 1998
• Data Mining Applications for Empowering Knowledge SocietiesHakikur Rahman — 2008
• An incremental approach for maintaining functional dependenciesGhada Gasmi, Lotfi Lakhal, Yahya Slimani — 2012
• Fast Algorithms for Mining Association Rules in Large DatabasesRakesh Agrawal, Ramakrishnan Srikant — 1994
• Fast algorithms for mining association rules and sequential patternsRamakrishnan Srikant, Jeffrey F. Naughton — 1996
• Discovery of Multiple-Level Association Rules from Large DatabasesJiawei Han, Yongjian Fu — 1995
• A feasibility and performance study of dependency inference (database design)Dina Bitton, Jeffrey C. Millman, Solveig Torgersen — 1989
• Database dependency discovery: a machine learning approachPeter Flach, Iztok Savnik — 1999
• Functional and approximate dependency mining: database and FCA points of viewStéphane Lopes, Jean-Marc Petit, Lotfi Lakhal — 2002
• Efficient discovery of XML data redundanciesCong Yu, H. V. Jagadish — 2006
• How to juggle columns: an entropy-based approach for table compressionMarcus Paradies, Christian Lemke, Hasso Plattner — 2010
• Identifying the Minimal Transversals of a Hypergraph and Related ProblemsThomas Eiter, Georg Gottlob — 1995
• Mining Functional Dependency from Relational Databases Using Equivalent Classes and Minimal CoverJalal Omer Atoum, Dojanah Bader, Arafat Awajan — 2008
• An experiment in knowledge discovery using data dependenciesF.J. McErlean, David Bell — 1997
• Database sampling with functional dependenciesJesús Bisbal, Jane Grimson — 2001
• Approximate Inference of Functional Dependencies from RelationsJyrki Kivinen, Heikki Mannila — 1995
• Normal forms and syntactic completeness proofs for functional independenciesDuminda Wijesekera, M. Ganesh, Jaideep Srivastava — 2001

Related papers

• TANE: An Efficient Algorithm for Discovering Functional and Approximate DependenciesY. Huhtala — 1999
• Algorithms for Inferring Functional Dependencies from RelationsHeikki Mannila, Kari‐Jouko Räihä — 1994
• Mining association rules between sets of items in large databasesRakesh Agrawal, Tomasz Imieliński, Arun Swami — 1993
• Design by Example: An Application of Armstrong RelationsHeikki Mannila, Kari‐Jouko Räihä — 1986
• Approximate Inference of Functional Dependencies from RelationsJyrki Kivinen, Heikki Mannila — 1995
• Fast Algorithms for Mining Association Rules in Large DatabasesRakesh Agrawal, Ramakrishnan Srikant — 1994
• Fast Algorithms for Mining Association RulesRakesh Agrawal, Ramakrishnan Srikant — 1998
• A Feasibility and Performance Study of Dependency InferenceDina Bitton, Jeffrey C. Millman, Solveig Torgersen — 1989