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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

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 correlated attributes. In this thesis, I present a principled probabilis- tic alternative that views an incomplete tuple as defining a distribution over the complete tuples that it stands for. I learn this distribution in terms of Bayes networks. My approach involves min- ing/"learning" Bayes networks from a sample of the database, and using it do both imputation (predict a missing value) and query rewriting (retrieve relevant results with incompleteness on the query-constrained attributes, when the data sources are autonomous). I present empirical studies to demonstrate that (i) at higher levels of incompleteness, when multiple attribute values are missing, Bayes networks do provide a significantly higher classification accuracy and (ii) the relevant possible answers retrieved by the queries reformulated using Bayes networks provide higher precision and recall than AFDs while keeping query processing costs manageable.
ContributorsRaghunathan, Rohit (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Lee, Joohyung (Committee member) / Arizona State University (Publisher)
Created2011
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Leonard Hayflick studied the processes by which cells age during the twentieth and twenty-first centuries in the United States. In 1961 at the Wistar Institute in the US, Hayflick researched a phenomenon later called the Hayflick Limit, or the claim that normal human cells can only divide forty to sixty

Leonard Hayflick studied the processes by which cells age during the twentieth and twenty-first centuries in the United States. In 1961 at the Wistar Institute in the US, Hayflick researched a phenomenon later called the Hayflick Limit, or the claim that normal human cells can only divide forty to sixty times before they cannot divide any further. Researchers later found that the cause of the Hayflick Limit is the shortening of telomeres, or portions of DNA at the ends of chromosomes that slowly degrade as cells replicate. Hayflick used his research on normal embryonic cells to develop a vaccine for polio, and from HayflickÕs published directions, scientists developed vaccines for rubella, rabies, adenovirus, measles, chickenpox and shingles.

Created2014-07-20
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Although best known for his work with the fruit fly, for which he earned a Nobel Prize and the title "The Father of Genetics," Thomas Hunt Morgan's contributions to biology reach far beyond genetics. His research explored questions in embryology, regeneration, evolution, and heredity, using a variety of approaches.

Created2007-09-25
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Created1935