Finding Influential Users in Social Media Using Association Rule Learning

Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods

PRESEE: An MDL/MML Algorithm to Time-Series Stream Segmenting

Time-series stream is one of the most common data types in data mining field. It is prevalent in fields such as stock market, ecology, and medical care. Segmentation is a key step to accelerate the processing speed of time-series stream mining. Previous algorithms for segmentingmainly focused on the issue of ameliorating precision instead of payingmuch attention to the efficiency. Moreover, the performance of these algorithms depends heavily on parameters, which are hard for the users to set. In this paper, we propose PRESEE (parameter-free, real-time, and scalable time-series stream segmenting algorithm), which greatly improves the efficiency of time-series stream segmenting. PRESEE is based on both MDL (minimum description length) and MML (minimum message length) methods, which could segment the data automatically. To evaluate the performance of PRESEE, we conduct several experiments on time-series streams of different types and compare it with the state-of-art algorithm. The empirical results show that PRESEE is very efficient for real-time stream datasets by improving segmenting speed nearly ten times. The novelty of this algorithm is further demonstrated by the application of PRESEE in segmenting real-time stream datasets from ChinaFLUX sensor networks data stream

Hot Topic Discovery in Online Community using Topic Labels and Hot Features

With huge volumes of information on Internet, how to extract user-concerned hot topics quickly and effectively has become a fundamental task for information processing on Internet. Generally, hot topic detection includes two tasks, the first one is topic discovery and the other is its hotness evaluation. In this paper, we propose a hot topic detection method. For topic discovery, topics are identified by clustering based on extracted topic labels. For hotness evaluation, the proposed model has fully considered the internal and external dual features and combined them together. The experimental results over TianYa BBS demonstrate the efficiency of the proposed method: compared with topic discovery based on latent semantic indexing, the improved vector space model based on topic labels gets better results and the identified topics are more accurate. Moreover, the proposed hotness features could reflect the popularity of a topic, and hence have obtained better hot topic results finally

Model-Driven Automatic Question Generation for a Gamified Clinical Guideline Training System

Clinical practice guidelines (CPGs) are a cornerstone of modern medical practice since they summarize the vast medical literature and provide care recommendations based on the current best evidence. However, there are barriers to CPG utilization such as lack of awareness and lack of familiarity of the CPGs by clinicians due to ineffective CPG dissemination and implementation. This calls for research into effective and scalable CPG dissemination strategies that will improve CPG awareness and familiarity. We describe a model-driven approach to design and develop a gamified e-learning system for clinical guidelines where the training questions are generated automatically. We also present the prototype developed using this approach. We use models for different aspects of the system, an entity model for the clinical domain, a workflow model for the clinical processes and a game engine to generate and manage the training sessions. We employ gamification to increase user motivation and engagement in the training of guideline content. We conducted a limited formative evaluation of the prototype system and the users agreed that the system would be a useful addition to their training. Our proposed approach is flexible and adaptive as it allows for easy updates of the guidelines, integration with different device interfaces and representation of any guideline.acceptedVersio

A Hessenberg-type algorithm for computing PageRank Problems

PageRank is a widespread model for analysing the relative relevance of nodes within large graphs arising in several applications. In the current paper, we present a cost-effective Hessenberg-type method built upon the Hessenberg process for the solution of difficult PageRank problems. The new method is very competitive with other popular algorithms in this field, such as Arnoldi-type methods, especially when the damping factor is close to 1 and the dimension of the search subspace is large. The convergence and the complexity of the proposed algorithm are investigated. Numerical experiments are reported to show the efficiency of the new solver for practical PageRank computations

New concepts integration on e-learning platforms

The learning experience has evolved into the virtual world of the Internet, where learners have the possibility to shift from face-to-face learning environments to virtual learning environments supported by technologies. This concept, called e-learning, emerged in the early 1960s where a group of researchers from the Stanford University, USA began experimenting different ways to publish and assign learning content using a computer. These experiments were the beginning that led to the creation of countless learning platforms, initially constructed in standalone environments and later ported to the Internet as Webbased learning platforms. As initial objectives, these learning platforms include a collection of features to support instructors and learners in the learning process. However, some of these platforms continued to be based on an old instructor-centered learning model and created a collection of outdated technologies that, given the current need to a learner-center learning model and the existence of Web 2.0 technologies, become inadequate. As a solution to address and overcome these challenges, a friendly user interface and a correct root incorporation of Web 2.0 services a platform designed to focus the learning experience and environment personalization into the learner is needed to propose. In an operating system (OS) context the graphic user interface (GUI) is guided by a collection of approaches that details how human beings should interact with computers. These are the key ideas to customize, install, and organize virtual desktops. The combination of desktop concepts into a learning platform can be an asset to reduce the learning curve necessary to know how to use the system and also to create a group of flexible learning services. However, due to limitations in hypertext transfer protocol-hypertext markup language (HTTP-HTML) traditional solutions, to shift traditional technologies to a collection of rich Internet application (RIA) technologies and personal learning environments (PLEs) concepts is needed, in order to construct a desktop-like learning platform. RIA technologies will allow the design of powerful Web solutions containing many of the characteristics of desktop-like applications. Additionally, personal learning environments (PLEs) will help learners to manage learning contents. In this dissertation the personal learning environment box (PLEBOX) is presented. The PLEBOX platform is a customizable, desktop-like platform similar to the available operating systems, based on personal learning environments concepts and rich Internet applications technologies that provide a better learning environment for users. PLEBOX developers have a set of tools that allow the creation of learning and management modules that can be installed on the platform. These tools are management learning components and interfaces built as APIs, services, and objects of the software development kit (SDK). A group of prototype modules were build for evaluation of learning and management services, APIs, and SDKs. Furthermore, three case studies were created in order to evaluate and demonstrate the learning service usage in external environments. The PLEBOX deployment and corresponding features confirms that this platform can be seen as a very promising e-learning platform. Exhaustive experiments were driven with success and it is ready for use.A experiência de aprendizagem baseada em tecnologias evoluiu para o mundo virtual da Internet, onde os alunos têm a possibilidade de mudar uma aprendizagem presencial em sala de aula para uma aprendizagem baseada em ambientes virtuais de aprendizagem suportados por tecnologias. O conceito de e-learning surgiu nos anos sessenta (1960) quando um grupo de investigadores da Universidade de Standford, nos Estados Unidos, começaram a experimentar diferentes formas de publicar e atribuir conteúdos de aprendizagem através do computador. Estas experiências marcaram o começo que levou à criação de inúmeras plataformas de aprendizagem, inicialmente construídas em ambientes isolados e depois migradas para a Internet como plataformas de aprendizagem baseadas na Web. Como objectivos inicias, estas plataformas de aprendizagem incluem um conjunto de recursos para apoiar professores e alunos no processo de aprendizagem. No entanto, algumas destas plataformas continuam a ser baseadas em velhos modelos de aprendizagem centrados no professor, criadas com base em tecnologias ultrapassadas que, dadas as necessidades actuais de um modelo de aprendizagem centrado no aluno e da existência de tecnologias baseadas na Web 2.0, se tornaram inadequadas. Como abordagem para enfrentar e superar estes desafios propõem-se uma plataforma focada na personalização do ambiente de aprendizagem do aluno, composta por uma interface amigável e uma correcta incorporação de raiz de serviços da Web 2.0. No contexto dos sistemas operativos (SOs) o graphic user interface (GUI) é desenhado tendo em conta um conjunto de abordagens que detalha como as pessoas devem interagir com os computadores. Estas são as ideias chave para personalizar, instalar e organizar áreas de trabalho virtuais. A combinação do conceito desktop com uma plataforma de aprendizagem pode ser um trunfo para reduzir a curva de aprendizagem necessária para saber como utilizar o sistema e também para criar um grupo de serviços flexíveis de aprendizagem. No entanto, devido as limitações em soluções tradicionais hypertext transfer protocol - hypertext markup language (HTTP - HTML), é necessário migrar estas tecnologias para um grupo de tecnologias rich Internet application (RIA) e conceitos presentes em ambientes personalizados de aprendizagem (personal learning environment - PLE) para construir uma plataforma baseada em ambientes de trabalho virtuais de aprendizagem. As tecnologias RIA irão permitir a criação de soluções Web poderosas que contêm muitas das características disponíveis em aplicações desktop. Adicionalmente, o conceitos de PLE irá ajudar os alunos a gerir os seus próprios conteúdos de aprendizagem. Nesta dissertação, com base nas características apresentadas anteriormente, é apresentada a personal learning environment box (PLEBOX). A plataforma PLEBOX é uma solução de aprendizagem parametrizável com um ambiente de trabalho semelhante aos sistemas operativos actuais, baseando-se em personal learning environments e tecnologias RIA que fornecem um melhor ambiente de aprendizagem para os seus utilizadores. Os programadores da PLEBOX têm ao seu dispor um conjunto de ferramentas que permitem a criação de módulos de aprendizagem e administração que podem ser instalados na plataforma. Estas ferramentas são componentes de aprendizagem e interfaces construídos como APIs, serviços e objectos do software development kit (SDK). Foi construído um conjunto de módulos com o objectivo de avaliar e demonstrar os serviços de aprendizagem, os serviços de gestão, APIs e SDKs. Para além disso, foram criados três casos de estudo para avaliar e demonstrar a utilização dos serviços de aprendizagem em ambientes externos. O desenvolvimento efectuado até ao momento na PLEBOX e respectivos recursos confirma que esta plataforma pode ser vista com uma promissora plataforma de aprendizagem (e-learning), totalmente modular e adaptativa. Realizaram-se experiências exaustivas para testar a plataforma e estas foram realizadas com sucesso num ambiente real, estando assim a plataforma pronta para exploração real

An integrated mobile content recommendation system

Many features have been added to mobile devices to assist the user's information consumption. However, there are limitations due to information overload on the devices, hardware usability and capacity. As a result, content filtering in a mobile recommendation system plays a vital role in the solution to this problem. A system that utilises content filtering can recommend content which matches a user's needs based on user preferences with a higher accuracy rate. However, mobile content recommendation systems have problems and limitations related to cold start and sparsity. The problems can be viewed as first time connection and first content rating for non-interactive recommendation systems where information is insufficient to predict mobile content which will match with a user's needs. In addition, how to find relevant items for the content recommendation system which are related to a user's profile is also a concern. An integrated model that combines the user group identification and mobile content filtering for mobile content recommendation was proposed in this study in order to address the current limitations of the mobile content recommendation system. The model enhances the system by finding the relevant content items that match with a user's needs based on the user's profile. A prototype of the client-side user profile modelling is also developed to demonstrate the concept. The integrated model applies clustering techniques to determine groups of users. The content filtering implemented classification techniques to predict the top content items. After that, an adaptive association rules technique was performed to find relevant content items. These approaches can help to build the integrated model. Experimental results have demonstrated that the proposed integrated model performs better than the comparable techniques such as association rules and collaborative filtering. These techniques have been used in several recommendation systems. The integrated model performed better in terms of finding relevant content items which obtained higher accuracy rate of content prediction and predicted successful recommended relevant content measured by recommendation metrics. The model also performed better in terms of rules generation and content recommendation generation. Verification of the proposed model was based on real world practical data. A prototype mobile content recommendation system with client-side user profile has been developed to handle the revisiting user issue. In addition, context information, such as time-of-day and time-of-week, could also be used to enhance the system by recommending the related content to users during different time periods. Finally, it was shown that the proposed method implemented fewer rules to generate recommendation for mobile content users and it took less processing time. This seems to overcome the problems of first time connection and first content rating for non-interactive recommendation systems

Automated Realistic Test Input Generation and Cost Reduction in Service-centric System Testing

Service-centric System Testing (ScST) is more challenging than testing traditional software due to the complexity of service technologies and the limitations that are imposed by the SOA environment. One of the most important problems in ScST is the problem of realistic test data generation. Realistic test data is often generated manually or using an existing source, thus it is hard to automate and laborious to generate. One of the limitations that makes ScST challenging is the cost associated with invoking services during testing process. This thesis aims to provide solutions to the aforementioned problems, automated realistic input generation and cost reduction in ScST. To address automation in realistic test data generation, the concept of Service-centric Test Data Generation (ScTDG) is presented, in which existing services used as realistic data sources. ScTDG minimises the need for tester input and dependence on existing data sources by automatically generating service compositions that can generate the required test data. In experimental analysis, our approach achieved between 93% and 100% success rates in generating realistic data while state-of-the-art automated test data generation achieved only between 2% and 34%. The thesis addresses cost concerns at test data generation level by enabling data source selection in ScTDG. Source selection in ScTDG has many dimensions such as cost, reliability and availability. This thesis formulates this problem as an optimisation problem and presents a multi-objective characterisation of service selection in ScTDG, aiming to reduce the cost of test data generation. A cost-aware pareto optimal test suite minimisation approach addressing testing cost concerns during test execution is also presented. The approach adapts traditional multi-objective minimisation approaches to ScST domain by formulating ScST concerns, such as invocation cost and test case reliability. In experimental analysis, the approach achieved reductions between 69% and 98.6% in monetary cost of service invocations during testin