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Title

Discover dependencies from data: a review

Author(s)

Liu, Jixue;  Li, Jiuyong;  Liu, Chengfei;  Chen, Yongfeng

Abstract

Traditionally dependencies are used in database design and data quality control. In knowledge discovery, dependencies represent knowledge discovered from the data of databases. Some of the discovered dependencies represent new knowledge in the application area, which is critical to the advance of the area, and some are used to verify existing knowledge. In recent years, as more and more attention is placed on data quality, dependencies holding on data reflect the quality of data: the more dependencies that the data satisfies, the higher quality the data is of. Motivated by the importance of dependency discovery in knowledge discovery and data quality assessment, in this paper, we review the methods for functional dependency and inclusion dependency discovery in relational and XML databases in the literature.

Publication type

Journal article

Research centre

Swinburne University of Technology. Faculty of Information and Communication Technologies

Source

IEEE Transactions on Knowledge and Data Engineering, Vol. 24, no. 2 (Feb 2012), pp. 251-264

Publication year

2012

FOR Code(s)

08 Information and Computing Sciences

Keyword(s)

Conditional functional dependencies;  Data quality;  eXtensible Markup Language;  Functional dependencies;  Inclusion dependencies;  Integrity constraint;  Knowledge discovery;  XML

Publisher

IEEE

ISSN

1041-4347

Publisher URL

http://dx.doi.org/10.1109/TKDE.2010.197

Copyright

Copyright © 2011 IEEE. The accepted manuscript is reproduced here in accordance with the copyright policy of the publisher. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Full text

Peer reviewed