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Author Fan, Wenfei ♦ Ma, Shuai ♦ Tang, Nan ♦ Yu, Wenyuan
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2014
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Data repairing ♦ Conditional functional dependency ♦ Matching dependency ♦ Record matching
Abstract Central to a data cleaning system are record matching and data repairing. Matching aims to identify tuples that refer to the same real-world object, and repairing is to make a database consistent by fixing errors in the data by using integrity constraints. These are typically treated as separate processes in current data cleaning systems, based on heuristic solutions. This article studies a new problem in connection with data cleaning, namely the interaction between record matching and data repairing. We show that repairing can effectively help us identify matches, and vice versa. To capture the interaction, we provide a uniform framework that seamlessly unifies repairing and matching operations to clean a database based on integrity constraints, matching rules, and master data. We give a full treatment of fundamental problems associated with data cleaning via matching and repairing, including the static analyses of constraints and rules taken together, and the complexity, termination, and determinism analyses of data cleaning. We show that these problems are hard, ranging from NP-complete or coNP-complete, to PSPACE-complete. Nevertheless, we propose efficient algorithms to clean data via both matching and repairing. The algorithms find $\textit{deterministic}$ $\textit{fixes}$ and $\textit{reliable}$ $\textit{fixes}$ based on confidence and entropy analyses, respectively, which are more accurate than fixes generated by heuristics. Heuristic fixes are produced only when deterministic or reliable fixes are unavailable. We experimentally verify that our techniques can significantly improve the accuracy of record matching and data repairing that are taken as separate processes, using real-life and synthetic data.
ISSN 19361955
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2014-05-01
Publisher Place New York
e-ISSN 19361963
Journal Journal of Data and Information Quality (JDIQ)
Volume Number 4
Issue Number 4
Page Count 38
Starting Page 1
Ending Page 38

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Source: ACM Digital Library