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Unsupervised Bayesian Data Cleaning Techniques for Structured Data

abstract: Recent efforts in data cleaning have focused mostly on problems like data deduplication, record matching, and data standardization; few of these focus on fixing incorrect attribute values in tuples. Correcting values in tuples is typically performed by a minimum cost repair of tuples that violate static constraints like CFDs (which have to be provided by domain experts, or learned from a clean sample of the database). In this thesis, I provide a method for correcting individual attribute values in a structured database using a Bayesian generative model and a statistical error model learned from the noisy database directly. I thus avoid the necessity for a domain expert or master data. I also show how to efficiently perform consistent query answering using this model over a dirty database, in case write permissions to the database are unavailable. A Map-Reduce architecture to perform this computation in a distributed manner is also shown. I evaluate these methods over both synthetic and real data. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2014

Identiferoai:union.ndltd.org:asu.edu/item:25942
Date January 2014
ContributorsDe, Sushovan (Author), Kambhampati, Subbarao (Advisor), Chen, Yi (Committee member), Candan, K. Selçuk (Committee member), Liu, Huan (Committee member), Arizona State University (Publisher)
Source SetsArizona State University
LanguageEnglish
Detected LanguageEnglish
TypeDoctoral Dissertation
Format99 pages
Rightshttp://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved

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