Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2007. / Includes bibliographical references (leaves 98-113). Also available online.

Thesis (M.Sc.)--University of Alberta, 2009. / Title from PDF file main screen (viewed on July 30, 2009). "A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science, Department of Computing Science, University of Alberta." Includes bibliographical references.

Thesis (M.S.)--Brigham Young University. Dept. of Computer Science, 2008. / Includes bibliographical references (p. 101-107).

Thesis (M.S.)--University of Missouri-Columbia, 2005. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (December 12, 2006) Includes bibliographical references.

Thesis (M.S.)--State University of New York at Binghamton, Watson School of Engineering and Applied Science (Computer Science), 2005. / Includes bibliographical references.

Thesis (M.S.) -- Worcester Polytechnic Institute. / Keywords: implicit feedback; explicit feedback; document relevance; implicit indicators; search engine; voluntary feedback; mandatory feedback. Includes bibliographical references (leaves 75-77).

The papers of this thesis all look at different aspects of organisational governance and strategy. In particular these papers look at organisations that seem to be behaving in counterintuitive ways. For instance all around the world governments often spend disproportionately large amounts of money in the few months at the end of the fiscal year and in the private sector firms often advertise against their rivals even though by doing so they may face greater competition from these rival firms. In these papers I look into whether these behaviours are as a result of a strategy or perhaps reflect some form of a problem in organisational governance. I try to analyse the effects on market efficiency and what steps a government or regulator might take to improve the outcome of the market. The approach is generally theoretical but in the case of the first paper on government spending I calibrate a theoretical model to Northern Ireland spending data. In the rest of this document see non-technical abstracts for my three papers. Note that in order to avoid maths I had to simplify papers considerably so these nontechnical abstracts should not be cited.

abstract: Although there are many forms of organization on the Web, one of the most prominent ways to organize web content and websites are tags. Tags are keywords or terms that are assigned to a specific piece of content in order to help users understand the common relationships between pieces of content. Tags can either be assigned by an algorithm, the author, or the community. These tags can also be organized into tag clouds, which are visual representations of the structure and organization contained implicitly within these tags. Importantly, little is known on how we use these different tagging structures to understand the content and structure of a given site. This project examines 2 different characteristics of tagging structures: font size and spatial orientation. In order to examine how these different characteristics might interact with individual differences in attentional control, a measure of working memory capacity (WMC) was included. The results showed that spatial relationships affect how well users understand the structure of a website. WMC was not shown to have any significant effect; neither was varying the font size. These results should better inform how tags and tag clouds are used on the Web, and also provide an estimation of what properties to include when designing and implementing a tag cloud on a website. / Dissertation/Thesis / M.S. Applied Psychology 2011

This thesis addresses the need for a scalable distributed solution for k-nearest-neighbor (kNN) search, a fundamental data mining task. This unsupervised method poses particular challenges on shared-nothing distributed architectures, where global information about the dataset is not available to individual machines. The distance to search for neighbors is not known a priori, and therefore a dynamic data partitioning strategy is required to guarantee that exact kNN can be found autonomously on each machine. Pivot-based partitioning has been shown to facilitate bounding of partitions, however state-of-the-art methods suffer from prohibitive data duplication (upwards of 20x the size of the dataset). In this work an innovative method for solving exact distributed kNN search called PkNN is presented. The key idea is to perform computation over several rounds, leveraging pivot-based data partitioning at each stage. Aggressive data-driven bounds limit communication costs, and a number of optimizations are designed for efficient computation. Experimental study on large real-world data (over 1 billion points) compares PkNN to the state-of-the-art distributed solution, demonstrating that the benefits of additional stages of computation in the PkNN method heavily outweigh the added I/O overhead. PkNN achieves a data duplication rate close to 1, significant speedup over previous solutions, and scales effectively in data cardinality and dimension. PkNN can facilitate distributed solutions to other unsupervised learning methods which rely on kNN search as a critical building block. As one example, a distributed framework for the Local Outlier Factor (LOF) algorithm is given. Testing on large real-world and synthetic data with varying characteristics measures the scalability of PkNN and the distributed LOF framework in data size and dimensionality.

This thesis addresses the need for a scalable distributed solution for k-nearest-neighbor (kNN) search, a fundamental data mining task. This unsupervised method poses particular challenges on shared-nothing distributed architectures, where global information about the dataset is not available to individual machines. The distance to search for neighbors is not known a priori, and therefore a dynamic data partitioning strategy is required to guarantee that exact kNN can be found autonomously on each machine. Pivot-based partitioning has been shown to facilitate bounding of partitions, however state-of-the-art methods suffer from prohibitive data duplication (upwards of 20x the size of the dataset). In this work an innovative method for solving exact distributed kNN search called PkNN is presented. The key idea is to perform computation over several rounds, leveraging pivot-based data partitioning at each stage. Aggressive data-driven bounds limit communication costs, and a number of optimizations are designed for efficient computation. Experimental study on large real-world data (over 1 billion points) compares PkNN to the state-of-the-art distributed solution, demonstrating that the benefits of additional stages of computation in the PkNN method heavily outweigh the added I/O overhead. PkNN achieves a data duplication rate close to 1, significant speedup over previous solutions, and scales effectively in data cardinality and dimension. PkNN can facilitate distributed solutions to other unsupervised learning methods which rely on kNN search as a critical building block. As one example, a distributed framework for the Local Outlier Factor (LOF) algorithm is given. Testing on large real-world and synthetic data with varying characteristics measures the scalability of PkNN and the distributed LOF framework in data size and dimensionality.