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: Davulcu, Hasan
This research investigates the problem of preference aware skyline processing which consists of inferring the preferences of users and computing a skyline specific to that user, taking into account his preferences. This research proposes a model that transforms the data from a given space to a user preferential space where each attribute represents the preference of the user. This study proposes two techniques "Preferential Skyline Processing" and "Latent Skyline Processing" to efficiently compute preference aware skylines in the user preferential space. Finally, through extensive experiments and performance analysis the correctness of the recommendations and the algorithm's ability to outperform the naïve ones is confirmed.
In this research, given a mixed set of senators/blogs debating on a set of political issues from opposing camps, I use signed bipartite graphs for modeling debates, and I propose an algorithm for partitioning both the opinion holders (senators or blogs) and the issues (bills or topics) comprising the debate into binary opposing camps. Simultaneously, my algorithm scales the entities on a univariate scale. Using this scale, a researcher can identify moderate and extreme senators/blogs within each camp, and polarizing versus unifying issues. Through performance evaluations I show that my proposed algorithm provides an effective solution to the problem, and performs much better than existing baseline algorithms adapted to solve this new problem. In my experiments, I used both real data from political blogosphere and US Congress records, as well as synthetic data which were obtained by varying polarization and degree distribution of the vertices of the graph to show the robustness of my algorithm.
I also applied my algorithm on all the terms of the US Senate to the date for longitudinal analysis and developed a web based interactive user interface www.PartisanScale.com to visualize the analysis.
US politics is most often polarized with respect to the left/right alignment of the entities. However, certain issues do not reflect the polarization due to political parties, but observe a split correlating to the demographics of the senators, or simply receive consensus. I propose a hierarchical clustering algorithm that identifies groups of bills that share the same polarization characteristics. I developed a web based interactive user interface www.ControversyAnalysis.com to visualize the clusters while providing a synopsis through distribution charts, word clouds, and heat maps.
Among the various aspects of network studies, network connectivity plays an important role in a myriad of applications. The diversified application areas have spurred numerous connectivity measures, each designed for some specific tasks. Although effective in their own fields, none of the connectivity measures is generally applicable to all the tasks. Moreover, existing connectivity measures are predominantly based on single-layered networks, with few attempts made on multi-layered networks.
Most connectivity analyzing methods assume that the input network is static and accurate, which is not realistic in many applications. As real-world networks are evolving, their connectivity scores would vary by time as well, making it imperative to keep track of those changing parameters in a timely manner. Furthermore, as the observed links in the input network may be inaccurate due to noise and incomplete data sources, it is crucial to infer a more accurate network structure to better approximate its connectivity scores.
The ultimate goal of connectivity studies is to optimize the connectivity scores via manipulating the network structures. For most complex measures, the hardness of the optimization problem still remains unknown. Meanwhile, current optimization methods are mainly ad-hoc solutions for specific types of connectivity measures on single-layered networks. No optimization framework has ever been proposed to tackle a wider range of connectivity measures on complex networks.
In this thesis, an in-depth study of connectivity measures, inference, and optimization problems will be proposed. Specifically, a unified connectivity measure model will be introduced to unveil the commonality among existing connectivity measures. For the connectivity inference aspect, an effective network inference method and connectivity tracking framework will be described. Last, a generalized optimization framework will be built to address the connectivity minimization/maximization problems on both single-layered and multi-layered networks.
The ubiquity of social media, especially Twitter, in financial market has been overly resonant in the past couple of years. With the growth of its (Twitter) usage by news channels, financial experts and pandits, the global economy does seem to hinge on 140 characters. By analyzing the number of tweets hash tagged to a stock, a strong relation can be established between the number of people talking about it, to the trading volume of the stock.
In my work, I overt this relation and find a state of the breakout when the volume goes beyond a characterized support or resistance level.