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Resumoste estudo trata da análise da produção científica brasileira sobre tecnologias de construção e edificações por meio de indicadores bibliométricos. A área foi escolhida em razão de sua relevância para o desenvolvimento econômico do país. A bibliometria é um método de análise quantitativa para a pesquisa científica. Os dados elaborados por meio dos estudos bibliométricos mensuram a contribuição do conhecimento científico derivado das publicações em determinadas áreas. Os indicadores de produção são úteis para o planejamento e a execução de políticas públicas, e para o conhecimento da comunidade científica sobre o sistema em que está inserida. O objetivo deste trabalho é estudar a produção científica brasileira na área da construção e tecnologia da construção civil a partir da análise de artigos científicos publicados em periódicos e anais de congressos indexados na base de dados Web of Science (WoS). Os dados foram coletados utilizando como critério de seleção a categoria "Construction & Building Technology", sendo analisados dados de 910 artigos de autores brasileiros no período de 1982 a 2014. A interpretação dos dados permite confirmar o expressivo crescimento da produção científica brasileira e da grande contribuição da região Sudeste para essa evolução.Palavras-chaves:Tecnologias de construção e edificação. Construção e tecnologia da construção. Bibliometria.Ciência.Tecnologia e inovação. Abstract This study consists in the analysis of the Brazilian scientific production on Building and Construction Technologies through bibliometric indicators. This field of study was chosen because of its relevance to the country's economic development. Bibliometry is a quantitative analysis method for scientific research. The data elaborated through bibliometric studies measures the contribution of scientific knowledge obtained from publications in specific areas. Production indicators are useful for the planning and execution of public policies and to improve the knowledge of the scientific community about the system that it is a part of. The aim of this study is to assess the Brazilian scientific production in Building and

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Ubiquitous Knowledge Discovery is a new research area at the intersection of machine learning and data mining with mobile and distributed systems. In this paper the main characteristics of the objects of study are defined and a high-level framework for analyzing ubiquitous knowledge discovery systems is introduced. Next, a number of examples from a broad range of application areas are reviewed and analyzed in terms of this framework. Based on this material, important characteristics of this field are identified and a number of research challenges are discussed. Ubiquitous Knowledge DiscoveryKnowledge Discovery in ubiquitous environments (KDUbiq) is an emerging area of research at the intersection of the two major challenges of highly distributed and mobile systems and advanced knowledge discovery systems.Today, in many subfields of computer science and engineering, being intelligent and adaptive marks the difference between a system that works in a complex and changing environment and a system that does not work. Hence, projects across many areas, ranging from Web 2.0 to ubiquitous computing and robotics, aim to create systems which are "smart", "intelligent", "adaptive" etc., allowing to solve problems that could not be solved before. A central assumption of KDUbiq is that what seems to be a bewildering array of different methodologies and approaches for building smart, adaptive, intelligent systems, can be cast into a coherent, integrated set of key ideas centered on the notion of learning from experience.Focusing on these key ideas, KDUbiq provides a unifying framework for systematically investigating the mutual dependencies of otherwise quite unrelated technologies employed in building next-generation intelligent systems: machine learning, data mining, sensor networks, grids, P2P, data stream mining, activity recognition, Web 2.0, privacy, user modeling and others. Machine learning and data mining emerge as basic methodologies and indispensable building blocks for some of the most difficult computer science and engineering challenges of the next decade.From a high-level perspective, key characteristics of an ubiquitous knowledge discovery application are:C1. Time and space. The objects of analysis exist in time and space. Often they are able to move.C2. Dynamic environment. These objects might not be stable over the life-time of an application. Instead they might appear or disappear. They exist in a dynamic and unstable environment, evolving incrementally over time.C3. Information processing capability. The objects are endowed with information processing capabilities.C4. Locality. The objects never see the global picture, knowing only their local spatio-temporal environment.C5. Real-Time. Because they typically have to take decisions or even act upon their environment, analysis and inference has to be done in real-time, and not only on historic data; the models have to evolve incrementally in correspondence with the evolving environment.C6. Distributed. In many cases the object will be able to excha...

In its 30 years of existence, there are still many improvement possibilities in studies performing the life cycle assessment (LCA) of wastewater treatment plants (WWTPs). Hence, this paper aims to start a guideline development for LCA of urban WWTPs based on the information available in the scientific literature on the topic. The authors used the ProKnow-C systematic review methodology for paper selection and 111 studies were analyzed. The most significant points that can be improved are caused by missing essential information (e.g. functional unity and input data). Other important methodological aspects are covered: allocation process, functional unit choice, sensitivity analysis, and important fluxes to be considered. Many opportunities within the LCA of WWTPs were identified, such as optimization of WWTP operational aspects and resource recovery. Furthermore, LCA should be combined with other methodologies such as big data, data envelopment analysis, life cycle cost assessment, and social life cycle assessment. To achieve this potential, it is clear that the scientific and technical community needs to converge on a new protocol to ensure that LCA application becomes more reliable and transparent.