2026-05-13T11:02:40Zhttps://keep.lib.asu.edu/oai/requestoai:keep.lib.asu.edu:node-1530032024-12-20T18:25:12Zoai_pmh:alloai_pmh:repo_items153003
https://hdl.handle.net/2286/R.I.25942
http://rightsstatements.org/vocab/InC/1.0/
All Rights Reserved
2014
viii, 90 p. : ill. (some col.)
Doctoral Dissertation
Academic theses
Text
eng
De, Sushovan
Kambhampati, Subbarao
Chen, Yi
Candan, K. Selcuk
Liu, Huan
Arizona State University
Partial requirement for: Ph.D., Arizona State University, 2014
Includes bibliographical references (p. 87-90)
Field of study: Computer science
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.
Computer Science
Consistent Query Answering
Databases
Data Cleaning
Information retrieval
Probabilistic databases
Database management
Structured programming
Information retrieval
Unsupervised Bayesian data cleaning techniques for structured data