Logical foundations of possibilistic keys

Koehler, H; Leck, U; Link, S; Prade, H

Logical foundations of possibilistic keys

Koehler, H Leck, U Link, S Prade, H

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Publication Type:: Journal Article
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, 8761 pp. 181 - 195
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Koehler, H	en_US
dc.contributor.author	Leck, U	en_US
dc.contributor.author	Link, S	en_US
dc.contributor.author	Prade, H https://orcid.org/0000-0003-4586-8527	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, 8761 pp. 181 - 195	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/115723
dc.description.abstract	© Springer International Publishing Switzerland 2014. Possibility theory is applied to introduce and reason about the fundamental notion of a key for uncertain data. Uncertainty is modeled qualitatively by assigning to tuples of data a degree of possibility with which they occur in a relation, and assigning to keys a degree of certainty which says to which tuples the key applies. The associated implication problem is characterized axiomatically and algorithmically. It is shown how sets of possibilistic keys can be visualized as possibilistic Armstrong relations, and how they can be discovered from given possibilistic relations. It is also shown how possibilistic keys can be used to clean dirty data by revising the belief in possibility degrees of tuples.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Logical foundations of possibilistic keys	en_US
dc.type	Journal Article
utslib.citation.volume	8761	en_US
utslib.for	0804 Data Format	en_US
utslib.for	0803 Computer Software	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	8761	en_US

Abstract:

© Springer International Publishing Switzerland 2014. Possibility theory is applied to introduce and reason about the fundamental notion of a key for uncertain data. Uncertainty is modeled qualitatively by assigning to tuples of data a degree of possibility with which they occur in a relation, and assigning to keys a degree of certainty which says to which tuples the key applies. The associated implication problem is characterized axiomatically and algorithmically. It is shown how sets of possibilistic keys can be visualized as possibilistic Armstrong relations, and how they can be discovered from given possibilistic relations. It is also shown how possibilistic keys can be used to clean dirty data by revising the belief in possibility degrees of tuples.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/115723