Title
Conditional dependencies : a principled approach to improving data quality
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Publication type
article
Publication
Subject
Computer. Automation
Source (journal)
Lecture notes in computer science
Volume/pages
5588(2009) , p. 8-20
ISSN
0302-9743
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Abstract
Real-life data is often dirty and costs billions of pounds to businesses worldwide each year. This paper presents a promising approach to improving data quality. It effectively detects and fixes inconsistencies in real-life data based on conditional dependencies, an extension of database dependencies by enforcing bindings of semantically related data values. It accurately identifies records from unreliable data sources by leveraging relative candidate keys, an extension of keys for relations by supporting similarity and matching operators across relations. In contrast to traditional dependencies that were developed for improving the quality of schema, the revised constraints are proposed to improve the quality of data. These constraints yield practical techniques for data repairing and record matching in a uniform framework.