Grande parte do sucesso do Google provêm do algoritmo Page Rank, que avalia quantitativamente a importância de cada página na web. Esta ordenação é obtida através do vetor estacionário de uma matriz estocástica específica, utilizando o Método das Potências. A velocidade de convergência deste método será avaliada em detalhe, já que se trata de uma resposta imediata da pesquisa do usuário. Afim de entender as diferentes situações que o modelo pode enfrentar, diversas simulações são apresentadas neste trabalho. Em particular, estamos interessados nos fatores que influenciam a velocidade de convergência. Para tanto, o número de páginas total e de cada conjunto fechado, bem como o número de conjuntos fechados e de nós pendentes foram estudados. / Great part of Google\'s success comes from the Page Rank algorithm, wich quantitatively evaluates the importance of each page on the web. This sort is achieved through a specific stochastic matrix stationary vector, using the Power Method. The convergency speed of this method will be evaluated in details, since this is a imediate response for the user search. In order to understand the diferent situations the model can confront, several simulations are shown in this work. In particular, we are interested in the factors which influences the convergency speed. For that, the total and inside each closed set number of pages and also the closed sets and dangling nodes numbers were studied.

Grande parte do sucesso do Google provêm do algoritmo Page Rank, que avalia quantitativamente a importância de cada página na web. Esta ordenação é obtida através do vetor estacionário de uma matriz estocástica específica, utilizando o Método das Potências. A velocidade de convergência deste método será avaliada em detalhe, já que se trata de uma resposta imediata da pesquisa do usuário. Afim de entender as diferentes situações que o modelo pode enfrentar, diversas simulações são apresentadas neste trabalho. Em particular, estamos interessados nos fatores que influenciam a velocidade de convergência. Para tanto, o número de páginas total e de cada conjunto fechado, bem como o número de conjuntos fechados e de nós pendentes foram estudados. / Great part of Google\'s success comes from the Page Rank algorithm, wich quantitatively evaluates the importance of each page on the web. This sort is achieved through a specific stochastic matrix stationary vector, using the Power Method. The convergency speed of this method will be evaluated in details, since this is a imediate response for the user search. In order to understand the diferent situations the model can confront, several simulations are shown in this work. In particular, we are interested in the factors which influences the convergency speed. For that, the total and inside each closed set number of pages and also the closed sets and dangling nodes numbers were studied.

Using page rank and semantic information are frequently used techniques in next page prediction systems. In our work, we extend the use of Page Rank algorithm for next page prediction with several navigational attributes, which are size of the page, duration of the page visit and duration of transition (two page visits sequentially), frequency of page and transition. In our model, we define popularity of transitions and pages by using duration information, use it in a relation with page size, and visit frequency factors. By using the popularity value of pages, we bias conventional Page Rank algorithm and model a next page prediction system that produces page recommendations under given top-n value. Moreover, we extract semantic terms from web URLs in order to tag pages semantically. The extracted terms are mapped into web URLs with different level of details in order to find semantically similar pages for next page recommendations. With this tagging, we model another next page prediction method, which uses Semantic Tagging (ST) similarity and exploits PPR values as a supportive method. Moreover, we model a Hybrid Page Rank (HPR) algorithm that uses both Semantic Tagging based approach and Popularity Based Page Rank values of pages together in order to investigate the effect of PPR and ST with equal weights. In addition, we investigate the effect of local (a synopsis of directed web graph) and global (whole directed web graph) modeling on next page prediction accuracy.

Influence has been studied across many different domains including sociology, statistics, marketing, network theory, psychology, social media, politics, and web search. In each of these domains, being able to measure and rank various degrees of influence has useful applications. For example, measuring influence in web search allows internet users to discover useful content more quickly. However, many of these algorithms measure influence across networks and graphs that are mathematically static. This project explores influence measurement within the context of linear time invariant (LTI) systems. While dynamical networks do have mathematical models for quantifying influence on a node-to-node basis, to the best of our knowledge, there are no proposed mathematical formulations that measure aggregate level influence across an entire dynamical network. The dynamics associated with each link, which can differ from one link to another, add additional complexity to the problem. Because of this complexity, many of the static-graph approaches used in web search do not achieve the desired outcome for dynamical networks. In this work we build upon concepts from PageRank and systems theory introduce two new methods for measuring influence within dynamical networks: 1) Dynamical Responsive Page Rank (DRPR) and 2) Aggregated Targeted Reachability (ATR). We then compare and analyze and compare results with these new methods.

Electronic commerce (e-commerce) markets provide benefits for both buyers and sellers; however, because of cyber security risks consumers are reluctant to transact online. Trust in e-commerce is paramount for adoption. Trust as a subject for research has been a term considered in depth by numerous researchers in various fields of study, including psychology and information technology. Various models have been developed in e-commerce to alleviate consumer fears, thus promoting trust in online environments. Third-party web seals and online scanning tools are some of the existing models used in e-commerce environments, but they have some deficiencies, e.g. failure to incorporate compliance, which need to be addressed. This research proposes an e-commerce assurance model for safe online shopping. The machine learning model is called the Page ranking analytical hierarchy process (PRAHP). PRAHP builds complementary strengths of the analytical hierarchy process (AHP) and Page ranking (PR) techniques to evaluate the trustworthiness of web attributes. The attributes that are assessed are Adaptive legislation, Adaptive International Organisation for Standardisation Standards, Availability, Policy and Advanced Security login. The attributes were selected based on the literature reviewed from accredited journals and some of the reputable e-commerce websites. PRAHP’s paradigms were evaluated extensively through detailed experiments on business-to-business, business-to-consumer, cloud-based and general e-commerce websites. The results of the assessments were validated by customer inputs regarding the website. The reliability and robustness of PRAHP was tested by varying the damping factor and the inbound links. In all the experiments, the results revealed that the model provides reliable results to guide customers in making informed purchasing decisions. The research also reveals hidden e-commerce topics that have not received attention, which generates knowledge and opens research questions for future researchers. These ultimately made significant contributions in e-commerce assurance, in areas such as security and compliance through the fusing of AHP and PR, integrated into a decision table for alleviating trustworthiness anxiety in various e-commerce transacting partners, e-commerce platforms and markets. / College of Engineering, Science and Technology / D. Phil. Information Systems

The aim of the thesis was to design, implement and evaluate the results of automatic tests for the JavaScript Restrictor project, which is being developed as a web browser extension. The tests are divided into three levels - unit, integration, and system. The Unit Tests verify the behavior of individual features, the Integration Tests verify the correct wrapping of browser API endpoints, and the System Tests check that the extension does not suppress the desired functionality of web pages. The System Tests are implemented for parallel execution in a distributed environment which has succeeded in achieving an almost directly proportional reduction in time with respect to the number of the tested nodes. The benefit of this work is detection of previously unknown errors in the JavaScript Restrictor extension and provision of the necessary information that allowed to fix some of the detected bugs.