Handling dirty databases: From user warning to data cleaning-towards an interactive approach

Pivert, O; Prade, H

Handling dirty databases: From user warning to data cleaning-towards an interactive approach

Pivert, O Prade, H

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 6379 LNAI pp. 292 - 305
Issue Date:: 2010-10-25

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Field	Value	Language
dc.contributor.author	Pivert, O	en_US
dc.contributor.author	Prade, H https://orcid.org/0000-0003-4586-8527	en_US
dc.date.issued	2010-10-25	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 6379 LNAI pp. 292 - 305	en_US
dc.identifier.isbn	3642159508	en_US
dc.identifier.isbn	9783642159503	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32860
dc.description.abstract	One can conceive many reasonable ways of characterizing how dirty a database is with respect to a set of integrity constraints (e.g., functional dependencies). However, dirtiness measures, as good as they can be, are difficult to interpret for an end-user and do not give the database administrator much hint about how to clean the base. This paper discusses these aspects and proposes some methods aimed at either helping the user or the administrator overcome the limitations of dirtiness measures when it comes to handling dirty databases. © 2010 Springer-Verlag Berlin Heidelberg.	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.relation.isbasedon	10.1007/978-3-642-15951-0_28	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Handling dirty databases: From user warning to data cleaning-towards an interactive approach	en_US
dc.type	Conference Proceeding
utslib.citation.volume	6379 LNAI	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0804 Data Format	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	6379 LNAI	en_US

Abstract:

One can conceive many reasonable ways of characterizing how dirty a database is with respect to a set of integrity constraints (e.g., functional dependencies). However, dirtiness measures, as good as they can be, are difficult to interpret for an end-user and do not give the database administrator much hint about how to clean the base. This paper discusses these aspects and proposes some methods aimed at either helping the user or the administrator overcome the limitations of dirtiness measures when it comes to handling dirty databases. © 2010 Springer-Verlag Berlin Heidelberg.

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http://hdl.handle.net/10453/32860