Thesis (M.S.) -- Mississippi State University. Department of Electrical and Computer Engineering. / Title from title screen. Includes bibliographical references.

Thesis (Dr. rer. nat.)--Technischen Fakultät der Christian-Albrechts-Universität zu Kiel, 2008. / Reproduced from PDF. Includes bibliographical references (p. 237-250).

Thesis (Ph.D.)--University of Western Sydney, 2007. / A thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy to the College of Health & Science, School of Computing and Mathematics, University of Western Sydney. Includes bibliography.

Thesis (Ph. D.)--Ohio State University, 2009. / Title from first page of PDF file. Includes vita. Includes bibliographical references (p. 115-119).

The purpose of this empirical study is to examine the usage of Enterprise Resource Planning Systems (ERPS) in Higher Education Institutes (HEIs). Recently, rapid growth in information technology services compels developing countries to emerge as an information-based society. This emergence corresponds with the calls of researchers to address ERPS (Abbas, 2011). However, there is a scarcity of efforts by researchers to identify the factors contributing to the usage of ERPS at the organisational, departmental and end-user layer in HEIs. To fill this gap, this research develops a Multi-Layer Usage Model (MLUM) to determine the factors of ERPS usage across the organisational, departmental and individual levels of HEIs. The theoretical foundation of this study is adapted from unified theory of acceptance and use of technology developed by Venkatesh et al (2003). The study is unique in many respects. Firstly, it offers a newly developed multi-level conceptual model that is tested empirically using three distinct questionnaires; one for each layer. A large primary dataset, 1317 responses, is collected through three questionnaire from 18 higher education institutions in Pakistan; 86 responses from the organisational layer, 143 from the departmental layer and 1088 from the end-user layer. Structural equation modelling is used to analyse the effect of factors at three layers contributing to the usage of ERPS. Furthermore, the models are refined by applying extensions of structural equation modelling. Results suggest that at the organisational layer human resource availability, tolerance for risks and conflicts, collegial support and collaboration and decision making and control are significant and contributed towards ERPS usage while at the end-user layer behavioural intentions and motivation were insignificant and were therefore, removed from the model. This study contributes to theory development regarding usage of innovations in the under-researched context of HEIs. It also provides indigenous manifestations of ERPS usage that may be used by policy-makers.

Early this century "S-shaped" curves, sigmoids, gained popularity among demographers. However, by 1940, the approach had "fallen out of favour", being criticised for giving poor results and having no theoretical validity. It was also considered that models of total population were of little practical interest, the main forecasting procedure currently adopted being the bottom-up "cohort-component" method. In the light of poor forecasting performance from component methods, a re-assessment is given in this thesis of the use of simple trend models. A suitable means of fitting these models to census data is developed, using a non-linear least squares algorithm based on minimisation of a proportionately weighted residual sum of squares. It is demonstrated that useful models can be obtained from which, by using a top-down methodology, component populations and vital components can be derived. When these models are recast in a recursive parameterisation, it is shown that forecasts can be obtained which, it is argued, are superior to existing official projections. Regarding theoretical validity, it is argued that sigmoid models relate closely to Malthusian theory and give a mathematical statement of the demographic transition. In order to judge the suitability of extrapolating from sigmoid models, a framework using Catastrophe Theory is developed. It is found that such a framework allows one qualitatively to model population changes resulting from subtle changes in influencing variables. The use of Catastrophe Theory has advantages over conventional demographic models as it allows a more holistic approach to population modelling.

Peer-to-Peer (P2P) overlays were initially proposed for use with wired networks. However, the very rapid proliferation of wireless communication technology has prompted a need for adoption of P2P systems in mobile networks too. There are many common characteristics between P2P overlay networks and Mobile Ad-hoc Networks (MANET). Self-organization, decentralization, a dynamic nature and changing topology are the most commonly shared features. Furthermore, when used together, the two approaches complement each other. P2P overlays provide data storage/retrieval functionality and MANET provides wireless connectivity between clients without depending on any pre-existing infrastructure. P2P overlay networks can be deployed over MANET to address content discovery issues. However, previous research has shown that deploying P2P systems straight over MANET does not exhibit satisfactory performance. Bandwidth limitation, limited resources and node mobility are some of the key constraints. This thesis proposes a novel approach, OneHopOverlay4MANET, to exploit the synergies between MANET and P2P overlays through cross-layering. It combines Distributed Hash Table (DHT) based structured P2P overlays with MANET underlay routing protocols to achieve one logical hop between any pair of overlay nodes. OneHopOverlay4MANET constructs a cross-layer channel to permit direct exchange of routing information between the Application layer, where the overlay operates, and the MANET underlay layer. Consequently, underlay routing information can be shared and used by the overlay. Thus, OneHopOverlay4MANET reduces the typical management traffic when deploying traditional P2P systems over MANET. Moreover, as a result of building one hop overlay, OneHopOverlay4MANET can eliminate the mismatching issue between overlay and underlay and hence resolve key lookups in a short time, enhancing the performance of the overlay. v In this thesis, we present OneHopOverlay4MANET and evaluate its performance when combined with different underlay routing protocols. OneHopOverlay4MANET has been combined with two proactive underlays (OLSR and BATMAN) and with three reactive underlay routing protocols (DSR, AODV and DYMO). In addition, the performance of the proposed system over OLSR has been compared to two recent structured P2P over MANET systems (MA-SP2P and E-SP2P) that adopted OLSR as the routing protocol. The results show that better performance can be achieved using OneHopOverlay4MANET.

Orientadores: Ricardo da Silva Torres, Anderson de Rezende Rocha / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-25T19:48:27Z (GMT). No. of bitstreams: 1 Werneck_RafaeldeOliveira_M.pdf: 2395829 bytes, checksum: 8f190aeb6dbafb841d0c03f7d7099041 (MD5) Previous issue date: 2014 / Resumo: Devido ao grande crescimento do uso de tecnologias para a aquisição de dados, temos que lidar com grandes e complexos conjuntos de dados a fim de extrair conhecimento que possa auxiliar o processo de tomada de decisão em diversos domínios de aplicação. Uma solução típica para abordar esta questão se baseia na utilização de métodos de aprendizado de máquina, que são métodos computacionais que extraem conhecimento útil a partir de experiências para melhorar o desempenho de aplicações-alvo. Existem diversas bibliotecas e arcabouços na literatura que oferecem apoio à execução de experimentos de aprendizado de máquina, no entanto, alguns não são flexíveis o suficiente para poderem ser estendidos com novos métodos, além de não oferecerem mecanismos que permitam o reuso de soluções de sucesso concebidos em experimentos anteriores na ferramenta. Neste trabalho, propomos um arcabouço para automatizar experimentos de aprendizado de máquina, oferecendo um ambiente padronizado baseado em workflow, tornando mais fácil a tarefa de avaliar diferentes descritores de características, classificadores e abordagens de fusão em uma ampla gama de tarefas. Também propomos o uso de medidas de similaridade e métodos de learning-to-rank em um cenário de recomendação, para que usuários possam ter acesso a soluções alternativas envolvendo experimentos de aprendizado de máquina. Nós realizamos experimentos com quatro medidas de similaridade (Jaccard, Sorensen, Jaro-Winkler e baseada em TF-IDF) e um método de learning-to-rank (LRAR) na tarefa de recomendar workflows modelados como uma sequência de atividades. Os resultados dos experimentos mostram que a medida Jaro-Winkler obteve o melhor desempenho, com resultados comparáveis aos observados para o método LRAR. Em ambos os casos, as recomendações realizadas são promissoras, e podem ajudar usuários reais em diferentes tarefas de aprendizado de máquina / Abstract: Due to the large growth of the use of technologies for data acquisition, we have to handle large and complex data sets in order to extract knowledge that can support the decision-making process in several domains. A typical solution for addressing this issue relies on the use of machine learning methods, which are computational methods that extract useful knowledge from experience to improve performance of target applications. There are several libraries and frameworks in the literature that support the execution of machine learning experiments. However, some of them are not flexible enough for being extended with novel methods and they do not support reusing of successful solutions devised in previous experiments made in the framework. In this work, we propose a framework for automating machine learning experiments that provides a workflow-based standardized environment and makes it easy to evaluate different feature descriptors, classifiers, and fusion approaches in a wide range of tasks. We also propose the use of similarity measures and learning-to-rank methods in a recommendation scenario, in which users may have access to alternative machine learning experiments. We performed experiments with four similarity measures (Jaccard, Sorensen, Jaro-Winkler, and a TF-IDF-based measure) and one learning-to-rank method (LRAR) in the task of recommending workflows modeled as a sequence of activities. Experimental results show that Jaro-Winkler yields the highest effectiveness performance with comparable results to those observed for LRAR. In both cases, the recommendations performed are very promising and might help real-world users in different daily machine learning tasks / Mestrado / Ciência da Computação / Mestre em Ciência da Computação

This diploma thesis results from the need for Competitive Intelligence as a system for gathering, analyzing and communicating information about competitors to obtain a competitive advantage. The work attempts to analyze current knowledge gathering processes as well as an application of that information for strategic decision making inside the XXX corporation. After definition of basic terms several different methods and approaches to Competitive Intelligence are described. These methods are reviewed and those that suit XXX requirements best are picked up afterwards. In this thesis there are also identified some of the crucial information sources, to begin with public and commercial databases and catalogues, business publications, online sources, personal knowledge, but also data-mining and other sophisticated methods. The main contribution of this work is the proposal of Competitive Intelligence system itself, empathising an effective functionality that solves identified issues, and is based on our theoretical resources and on actual competence of the company.

This dissertation studies economic issues in the digital economy with a specific focus on the economic aspects of how firms acquire and use consumer data. Chapter 1 empirically studies the drivers of digital attention in the space of social media applications. In order to do so I conduct an experiment where I comprehensively monitor how participants spend their time on digital services and use parental control software to shut off access to either their Instagram or YouTube. I characterize how participants substitute their time during and after the restrictions. I provide an interpretation of the substitution during the restriction period that allows me to conclude that relevant market definitions may be broader than those currently considered by regulatory authorities, but that the substantial diversion towards non-digital activities indicates significant market power from the perspective of consumers for Instagram and YouTube. I then use the results on substitution after the restriction period to motivate a discrete choice model of time usage with inertia and, using the estimates from this model, conduct merger assessments between social media applications. I find that the inertia channel is important for justifying blocking mergers, which I use to argue that currently debated policies aimed at curbing digital addiction are important not only just in their own right but also from an antitrust perspective and, in particular, as a potential policy tool for promoting competition in these markets. More broadly, my paper highlights the utility of product unavailability experiments for demand and merger analysis of digital goods. I thank Maayan Malter for working together with me on collecting the data for this paper. Chapter 2 then studies the next step in consumer data collection process – the extent to which a firm can collect a consumer’s data depends on privacy preferences and the set of available privacy tools. This chapter studies the impact of the General Data Protection Regulation on the ability of a data-intensive intermediary to collect and use consumer data. We find that the opt-in requirement of GDPR resulted in 12.5% drop in the intermediary-observed consumers, but the remaining consumers are trackable for a longer period of time. These findings are consistent with privacy-conscious consumers substituting away from less efficient privacy protection (e.g, cookie deletion) to explicit opt out—a process that would make opt-in consumers more predictable. Consistent with this hypothesis, the average value of the remaining consumers to advertisers has increased, offsetting some of the losses from consumer opt-outs. This chapter is jointly authored with Yeon-Koo Che and Tobias Salz. Chapter 3 and Chapter 4 make up the third portion of the dissertation that studies one of the most prominent uses of consumer data in the digital economy – recommendation systems. This chapter is a combination of several papers studying the economic impact of these systems. The first paper is a joint paper with Duarte Gonçalves which studies a model of strategic interaction between producers and a monopolist platform that employs a recommendation system. We characterize the consumer welfare implications of the platform’s entry into the production market. The platform’s entry induces the platform to bias recommendations to steer consumers towards its own goods, which leads to equilibrium investment adjustments by the producers and lower consumer welfare. Further, we find that a policy separating recommendation and production is not always welfare improving. Our results highlight the ability of integrated recommender systems to foreclose competition on online platforms. The second paper turns towards understanding how such systems impact consumer choices and is joint with Duarte Gonçalves and Shan Sikdar. In this paper we study a model of user decision-making in the context of recommender systems via numerical simulation. Our model provides an explanation for the findings of Nguyen et. al (2014), where, in environments where recommender systems are typically deployed, users consume increasingly similar items over time even without recommendation. We find that recommendation alleviates these natural filter-bubble effects, but that it also leads to an increase in homogeneity across users, resulting in a trade-off between homogenizing across-user consumption and diversifying within-user consumption. Finally, we discuss how our model highlights the importance of collecting data on user beliefs and their evolution over time both to design better recommendations and to further understand their impact.