Big data architecture for pervasive healthcare: a literature review

Pervasive healthcare aims to deliver deinstitutionalised healthcare services to patients anytime and anywhere. Pervasive healthcare involves remote data collection through mobile devices and sensor network which the data is usually in large volume, varied formats and high frequency. The nature of big data such as volume, variety, velocity and veracity, together with its analytical capabilities com-plements the delivery of pervasive healthcare. However, there is limited research in intertwining these two domains. Most research focus mainly on the technical context of big data application in the healthcare sector. Little attention has been paid to a strategic role of big data which impacts the quality of healthcare services provision at the organisational level. Therefore, this paper delivers a conceptual view of big data architecture for pervasive healthcare via an intensive literature review to address the aforementioned research problems. This paper provides three major contributions: 1) identifies the research themes of big data and pervasive healthcare, 2) establishes the relationship between research themes, which later composes the big data architecture for pervasive healthcare, and 3) sheds a light on future research, such as semiosis and sense-making, and enables practitioners to implement big data in the pervasive healthcare through the proposed architecture.

Fast adaptive real-time classification for data streams with concept drift

An important application of Big Data Analytics is the real-time analysis of streaming data. Streaming data imposes unique challenges to data mining algorithms, such as concept drifts, the need to analyse the data on the fly due to unbounded data streams and scalable algorithms due to potentially high throughput of data. Real-time classification algorithms that are adaptive to concept drifts and fast exist, however, most approaches are not naturally parallel and are thus limited in their scalability. This paper presents work on the Micro-Cluster Nearest Neighbour (MC-NN) classifier. MC-NN is based on an adaptive statistical data summary based on Micro-Clusters. MC-NN is very fast and adaptive to concept drift whilst maintaining the parallel properties of the base KNN classifier. Also MC-NN is competitive compared with existing data stream classifiers in terms of accuracy and speed.

Big data: a normal accident waiting to happen?

Widespread commercial use of the internet has significantly increased the volume and scope of data being collected by organisations. ‘Big data’ has emerged as a term to encapsulate both the technical and commercial aspects of this growing data collection activity. To date, much of the discussion of big data has centred upon its transformational potential for innovation and efficiency, yet there has been less reflection on its wider implications beyond commercial value creation. This paper builds upon normal accident theory (NAT) to analyse the broader ethical implications of big data. It argues that the strategies behind big data require organisational systems that leave them vulnerable to normal accidents, that is to say some form of accident or disaster that is both unanticipated and inevitable. Whilst NAT has previously focused on the consequences of physical accidents, this paper suggests a new form of system accident that we label data accidents. These have distinct, less tangible and more complex characteristics and raise significant questions over the role of individual privacy in a ‘data society’. The paper concludes by considering the ways in which the risks of such data accidents might be managed or mitigated.

Big Data and ecosystem research programmes

The size and complexity of data sets generated within ecosystem-level programmes merits their capture, curation, storage and analysis, synthesis and visualisation using Big Data approaches. This review looks at previous attempts to organise and analyse such data through the International Biological Programme and draws on the mistakes made and the lessons learned for effective Big Data approaches to current Research Councils United Kingdom (RCUK) ecosystem-level programmes, using Biodiversity and Ecosystem Service Sustainability (BESS) and Environmental Virtual Observatory Pilot (EVOp) as exemplars. The challenges raised by such data are identified, explored and suggestions are made for the two major issues of extending analyses across different spatio-temporal scales and for the effective integration of quantitative and qualitative data.

Estratégias de Compras Governamentais no Brasil - Teoria dos Leilões e "Big Data"

O estudo da FGV Projetos, coordenado pelo economista Fernando Blumenschein, desenvolve um quadro metodológico específico para compras governamentais com base em pesquisa realizada para o Fundo Nacional de Desenvolvimento da Educação (FNDE). Este estudo destaca o potencial de uso dos conceitos da teoria dos leilões, juntamente com métodos de análise de "Big Data" na formação de sessões públicas.

Privacidade e inovação na era do Big Data

A coleta e o armazenamento de dados em larga escala, combinados à capacidade de processamento de dados que não necessariamente tenham relação entre si de forma a gerar novos dados e informações, é uma tecnologia amplamente usada na atualidade, conhecida de forma geral como Big Data. Ao mesmo tempo em que possibilita a criação de novos produtos e serviços inovadores, os quais atendem a demandas e solucionam problemas de diversos setores da sociedade, o Big Data levanta uma série de questionamentos relacionados aos direitos à privacidade e à proteção dos dados pessoais. Esse artigo visa proporcionar um debate sobre o alcance da atual proteção jurídica aos direitos à privacidade e aos dados pessoais nesse contexto, e consequentemente fomentar novos estudos sobre a compatibilização dos mesmos com a liberdade de inovação. Para tanto, abordará, em um primeiro momento, pontos positivos e negativos do Big Data, identificando como o mesmo afeta a sociedade e a economia de forma ampla, incluindo, mas não se limitando, a questões de consumo, saúde, organização social, administração governamental, etc. Em seguida, serão identificados os efeitos dessa tecnologia sobre os direitos à privacidade e à proteção dos dados pessoais, tendo em vista que o Big Data gera grandes mudanças no que diz respeito ao armazenamento e tratamento de dados. Por fim, será feito um mapeamento do atual quadro regulatório brasileiro de proteção a tais direitos, observando se o mesmo realmente responde aos desafios atuais de compatibilização entre inovação e privacidade.

Livro analisa relações emergentes entre big data e metodologias qualitativas

As tecnologias digitais tornaram-se uma importante infraestrutura para as nossas vidas nas mais diversas dimensões – cultural, social, politicas e econômicas. As formas de mediação digital têm alterado meios tradicionais e convencionais de organização de tempo e espaço. No entanto, contextualizar e situar narrativas e práticas de produção e análise de dados de rede – gerados em grande volume e a uma grande velocidade – têm sido grandes desafios para pesquisadores de todo o mundo. Lançado no fim de 2014, o livro Big data? Qualitative approaches to digital research (Emerald Books, 2014) oferece uma visão crítica sobre análises qualitativas de novos tipos de dados, plataformas e meios de comunicação, suas implicações para o futuro e como melhorar ativamente a pesquisa em relação a estes. Com autores especialistas das áreas de sociologia, ciência politica, cultura, comunicação, metodologia e administração, os organizadores do livro, Martin Hand e Sam Hillyard, denominam de pesquisa social digital todas as perspectivas qualitativas de diversas disciplinas, conceitos e orientações metodológicas e empíricas que atestem a integração e a diversificação das tecnologias digitais e de dados na vida social de hoje.

O projeto Media Cloud Brasil: uma análise do tratamento de informações em ambientes de big data

EMAp - Escola de Matemática Aplicada

Big Data e gestão processual

Apresentamos um projeto inovador na intersecção da tecnologia da informação, gestão, e direito, com o intuito de oferecer otimização de resultados, redução de custos e de tempo. A equipe proponente foi formada no projeto Big Data e Gestão Processual, do qual participam três escolas da Fundação Getulio Vargas que são referência em todo Brasil: as escolas de Direito, de Administração de Empresas e de Matemática Aplicada, todas do Rio de Janeiro.

Big data and open data as sustainability tools: A working paper prepared by the Economic Commission for Latin America and the Caribbean

The two main forces affecting economic development are the ongoing technological revolution and the challenge of sustainability. Technological change is altering patterns of production, consumption and behaviour in societies; at the same time, it is becoming increasingly difficult to ensure the sustainability of these new patterns because of the constraints resulting from the negative externalities generated by economic growth and, in many cases, by technical progress itself. Reorienting innovation towards reducing or, if possible, reversing the effects of these externalities could create the conditions for synergies between the two processes. Views on the subject vary widely: while some maintain that these synergies can easily be created if growth follows an environmentally friendly model, summarized in the concept of green growth, others argue that production and consumption patterns are changing too slowly and that any technological fix will come too late. These considerations apply to hard technologies, essentially those used in production. The present document explores the opportunities being opened up by new ones, basically information and communication technologies, in terms of increasing the effectiveness (outcomes) and efficiency (relative costs) of soft technologies that can improve the way environmental issues are handled in business management and in public policy formulation and implementation.

An assessment of big data for official statistics in the Caribbean: Challenges and opportunities

The data revolution for sustainable development has triggered interest in the use of big data for official statistics such that theUnited Nations Economic and Social Council considers it to be almost an obligation for statistical organizations to explore big data. Big data has been promoted as a more timely and cheaper alternative to traditional sources of official data, and one that offers great potential for monitoring the sustainable development goals. However, privacy concerns, technology and capacity remain significant obstacles to the use of big data. This study makes a case for incorporating big data in official statitics in the Caribbean by highlight the opportunities that big data provides for the subregion, while suggesting ways to manage the challenges. It serves as a starting point for further discussions on the many facets of big data and provides an initial platform upon which a Caribbean big data strategy could be built.

Inteligência cibernética e uso de recursos semânticos na detecção de perfis falsos no contexto do Big Data

Pós-graduação em Ciência da Informação - FFC

Gerenciamento de Big Data em plantas industriais

Nowadays the companies generate great amount of data from different sources, however some of them produce more data than they can analyze. Big Data is a set of data that grows very fast, collected several times during a short period of time. This work focus on the importance of the correct management of Big Data in an industrial plant. Through a case study based on a company that belongs to the pulp and paper area, the problems resolutions are going to be presented with the usage of appropriate data management. In the final chapters, the results achieved by the company are discussed, showing how the correct choice of data to be monitored and analyzed brought benefits to the company, also best practices will be recommended for the Big Data management

Inteligência cibernética e uso de recursos semânticos na detecção de perfis falsos no contexto do Big Data

Pós-graduação em Ciência da Informação - FFC