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Dissertation/Thesis Abstract

Uncertain data integration with probabilities
by Tallur, Gayatri, M.S., The University of North Carolina at Greensboro, 2013, 57; 1551297
Abstract (Summary)

Real world applications that deal with information extraction, such as business intelligence software or sensor data management, must often process data provided with varying degrees of uncertainty. Uncertainty can result from multiple or inconsistent sources, as well as approximate schema mappings. Modeling, managing and integrating uncertain data from multiple sources has been an active area of research in recent years. In particular, data integration systems free the user from the tedious tasks of finding relevant data sources, interacting with each source in isolation using its corresponding interface and combining data from multiple sources by providing a uniform query interface to gain access to the integrated information.

Previous work has integrated uncertain data using representation models such as the possible worlds and probabilistic relations. We extend this work by determining the probabilities of possible worlds of an extended probabilistic relation. We also present an algorithm to determine when a given extended probabilistic relation can be obtained by the integration of two probabilistic relations and give the decomposed pairs of probabilistic relations.

Indexing (document details)
Advisor: Sadri, Fereidoon
Commitee: Deng, Jing, Green, Nancy
School: The University of North Carolina at Greensboro
Department: College of Arts and Sciences: Computer Science
School Location: United States -- North Carolina
Source: MAI 52/04M(E), Masters Abstracts International
Subjects: Computer science
Keywords: Database, Integration, Possible world, Probabilistic relation, Probability, Uncertain
Publication Number: 1551297
ISBN: 978-1-303-68577-4
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