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An Investigation of the Cost and Accuracy Tradeoffs of Supplanting AFDs with Bayes Network in Query Processing in the Presence of Incompleteness in Autonomous Databases


Abstract As the information available to lay users through autonomous data sources continues to increase, mediators become important to ensure that the wealth of information available is tapped effectively. A key challenge that these information mediators need to handle is the varying levels of incompleteness in the underlying databases in terms of missing attribute values. Existing approaches such as Query Processing over Incomplete Autonomous Databases (QPIAD) aim to mine and use Approximate Functional Dependencies (AFDs) to predict and retrieve relevant incomplete tuples. These approaches make independence assumptions about missing values--which critically hobbles their performance when there are tuples containing missing values for multiple corr... (more)
Created Date 2011
Contributor Raghunathan, Rohit (Author) / Kambhampati, Subbarao (Advisor) / Liu, Huan (Committee member) / Lee, Joohyung (Committee member) / Arizona State University (Publisher)
Subject Computer science / Autonomous Databases / Bayes Networks / Incompleteness / Uncertainty
Type Masters Thesis
Extent 44 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note M.S. Computer Science 2011
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis