Predictive analytics applied to Alzheimer’s disease : a data visualisation framework for understanding current research and future challenges

Dissertation as a partial requirement for obtaining a master’s degree in information management, with a specialisation in Business Intelligence and Knowledge Management.Big Data is, nowadays, regarded as a tool for improving the healthcare sector in many areas, such as in its economic side, by trying to search for operational efficiency gaps, and in personalised treatment, by selecting the best drug for the patient, for instance. Data science can play a key role in identifying diseases in an early stage, or even when there are no signs of it, track its progress, quickly identify the efficacy of treatments and suggest alternative ones. Therefore, the prevention side of healthcare can be enhanced with the usage of state-of-the-art predictive big data analytics and machine learning methods, integrating the available, complex, heterogeneous, yet sparse, data from multiple sources, towards a better disease and pathology patterns identification. It can be applied for the diagnostic challenging neurodegenerative disorders; the identification of the patterns that trigger those disorders can make possible to identify more risk factors, biomarkers, in every human being. With that, we can improve the effectiveness of the medical interventions, helping people to stay healthy and active for a longer period. In this work, a review of the state of science about predictive big data analytics is done, concerning its application to Alzheimer’s Disease early diagnosis. It is done by searching and summarising the scientific articles published in respectable online sources, putting together all the information that is spread out in the world wide web, with the goal of enhancing knowledge management and collaboration practices about the topic. Furthermore, an interactive data visualisation tool to better manage and identify the scientific articles is develop, delivering, in this way, a holistic visual overview of the developments done in the important field of Alzheimer’s Disease diagnosis.Big Data é hoje considerada uma ferramenta para melhorar o sector da saúde em muitas áreas, tais como na sua vertente mais económica, tentando encontrar lacunas de eficiência operacional, e no tratamento personalizado, selecionando o melhor medicamento para o paciente, por exemplo. A ciência de dados pode desempenhar um papel fundamental na identificação de doenças em um estágio inicial, ou mesmo quando não há sinais dela, acompanhar o seu progresso, identificar rapidamente a eficácia dos tratamentos indicados ao paciente e sugerir alternativas. Portanto, o lado preventivo dos cuidados de saúde pode ser bastante melhorado com o uso de métodos avançados de análise preditiva com big data e de machine learning, integrando os dados disponíveis, geralmente complexos, heterogéneos e esparsos provenientes de múltiplas fontes, para uma melhor identificação de padrões patológicos e da doença. Estes métodos podem ser aplicados nas doenças neurodegenerativas que ainda são um grande desafio no seu diagnóstico; a identificação dos padrões que desencadeiam esses distúrbios pode possibilitar a identificação de mais fatores de risco, biomarcadores, em todo e qualquer ser humano. Com isso, podemos melhorar a eficácia das intervenções médicas, ajudando as pessoas a permanecerem saudáveis e ativas por um período mais longo. Neste trabalho, é feita uma revisão do estado da arte sobre a análise preditiva com big data, no que diz respeito à sua aplicação ao diagnóstico precoce da Doença de Alzheimer. Isto foi realizado através da pesquisa exaustiva e resumo de um grande número de artigos científicos publicados em fontes online de referência na área, reunindo a informação que está amplamente espalhada na world wide web, com o objetivo de aprimorar a gestão do conhecimento e as práticas de colaboração sobre o tema. Além disso, uma ferramenta interativa de visualização de dados para melhor gerir e identificar os artigos científicos foi desenvolvida, fornecendo, desta forma, uma visão holística dos avanços científico feitos no importante campo do diagnóstico da Doença de Alzheimer

A business model framework for the Internet of Things

The Internet of Things (IoT) is an emerging technology with research interests transcending disciplines of computer sciences and computer engineering to agriculture, business management, civil engineering, architecture, medical sciences, social science etc. This is because of the potential expanding range of its application areas of wind mill operation and irrigation control, supply chain and logistics, manufacturing, home and office environment, healthcare, social care, etc. As it is usually the case with emerging technologies, IoT is faced with the challenge of bridging the gap between the technology development and corresponding business model design. Without a workable business model, the IoT paradigm may end up in research labs and subsequently fade away. A business model should show how lucrative it is to be in the IoT business by adding value to the customer and generating revenue for the business firm. This research is a contribution towards the goal of developing a business model for IoT, with customer/user value potential as the focal point. The comprehensive literature review carried out during this research (i) outlines the concept of business models; (ii) investigates through desk research, existing digital technology business models with focus on two (2) established digital technology firms and identified five generic components of their business models including but not limited to subscription, training, price, satisfaction, and trust, which were used for the primary investigation; (iii) investigates the IoT state-of-the-arts by elaborating on the IoT space and precursor technologies that are part of its ecosystem with the aim of describing, illustrating and developing application prototypes for three IoT scenarios of health monitoring, the use of the library and borrowing of books (a novel idea), and home environment; (iv) evaluates business model framework representation maps in current use, and specifically modified the general structure, content, and performance framework map to design an adoption framework map called a customer-focused business model framework map for IoT (CBMF4IoT). The unique approach to business model research involved conducting a user-led experiment to investigate the likelihood of IoT adoption of existing digital technology business models, as the customer value potential aspect of a business model design was the focal point of this research. Specifically, the experiment was aimed at determining if there was any significant differences in user inclinations towards the five generic components of existing digital technology business models based on smartphone context and IoT products context in a within-subjects design, with sample population drawn from University of Sussex community. The experimental design relied on participants' past experiences with smartphone for them to indicate their pre-purchase inclinations towards the five generics components. For the IoT products context, descriptions and diagrammatic illustration of the three IoT scenarios with their corresponding Just-in-Mind clickable prototypes served as educational tools to enable participants to be acquainted with IoT in order for them to indicate their potential pre-purchase inclinations towards the five generic components. A unique procedure for business model adoption likelihood was designed using the Sign test for high, low, and medium likelihood of adoption. The results of this test indicate medium likelihood of adoption for three of the generic components and low likelihood of adoption for two of the generic components. The results of this test was then fed to the CBMF4IoT. This thesis demonstrates that reusability of successful digital technology business models could potentially result in market success for an emerging digital technology in a B2C context, as users opinion formed the bases for the conclusions, instead of the conventional opinion gathering from only experts, business owners, and practitioners for a BM research

Video Vortex reader : responses to Youtube

The Video Vortex Reader is the first collection of critical texts to deal with the rapidly emerging world of online video – from its explosive rise in 2005 with YouTube, to its future as a significant form of personal media. After years of talk about digital convergence and crossmedia platforms we now witness the merger of the Internet and television at a pace no-one predicted. These contributions from scholars, artists and curators evolved from the first two Video Vortex conferences in Brussels and Amsterdam in 2007 which focused on responses to YouTube, and address key issues around independent production and distribution of online video content. What does this new distribution platform mean for artists and activists? What are the alternatives