ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- Creators: Xue, Guoliang
guessed by different sources. The values of different properties can be obtained from
various sources. These will lead to the disagreement in sources. An important task
is to obtain the truth from these sometimes contradictory sources. In the extension
of computing the truth, the reliability of sources needs to be computed. There are
models which compute the precision values. In those earlier models Banerjee et al.
(2005) Dong and Naumann (2009) Kasneci et al. (2011) Li et al. (2012) Marian and
Wu (2011) Zhao and Han (2012) Zhao et al. (2012), multiple properties are modeled
individually. In one of the existing works, the heterogeneous properties are modeled in
a joined way. In that work, the framework i.e. Conflict Resolution on Heterogeneous
Data (CRH) framework is based on the single objective optimization. Due to the
single objective optimization and non-convex optimization problem, only one local
optimal solution is found. As this is a non-convex optimization problem, the optimal
point depends upon the initial point. This single objective optimization problem is
converted into a multi-objective optimization problem. Due to the multi-objective
optimization problem, the Pareto optimal points are computed. In an extension of
that, the single objective optimization problem is solved with numerous initial points.
The above two approaches are used for finding the solution better than the solution
obtained in the CRH with median as the initial point for the continuous variables and
majority voting as the initial point for the categorical variables. In the experiments,
the solution, coming from the CRH, lies in the Pareto optimal points of the multiobjective
optimization and the solution coming from the CRH is the optimum solution
in these experiments.