From human mobility to renewable energies

Abstract

Abstract.We address and discuss recent trends in the analysis of big data sets, with the emphasis on studying multiscale phenomena. Applications of big data analysis in different scientific fields are described and two particular examples of multiscale phenomena are explored in more detail. The first one deals with wind power production at the scale of single wind turbines, the scale of entire wind farms and also at the scale of a whole country. Using open source data we show that the wind power production has an intermittent character at all those three scales, with implications for defining adequate strategies for stable energy production. The second example concerns the dynamics underlying human mobility, which presents different features at different scales.For that end, we analyze 12-month data of the Eduroam database within Portuguese universities, and find that, at the smallest scales, typically within a set of a few adjacent buildings, the characteristic exponents of average displacements are different from the ones found at the scale of one country or one continent. Big data: the emergence of a new paradigmIn some sense, the notion of Information Age is slowly fading from the perception of our society. For decades, national and international research has been creating impressive amounts of data, and often from various unrelated measurement sources [1]. Computational methods have been serving the needs of the scientific community and the need for higher computational power has driven the invention of more efficient computational codes and triggered the development of new hardware. Simultaneously, the internet has been instrumental in fostering international research collaborations and divulging scientific knowledge.Today, obtaining data is no longer a problem. Information is there, available for everyone at any time. Given the computational power of today's computers and clusters, even single research groups can create and store large data volumes. The challenge in today's research is more often what to do with the data we have. How to manage all the information we have in such large data sources? Which phenomena can we now study? The recent technological progress together with the new challenges naturally lead to a new paradigm in computational sciences, which is partially described by the term big data.In this paper we discuss the utility of big data for approaching the empirical study of phenomena up to now unreachable. We discuss the usage of big data in the study of two specific phenomena, that up to now were unreachable, namely wind energy production and human

Big Data Opportunities for Accounting and Finance Practice and Research

“…The need for higher computational power has also driven the invention of more efficient computational codes and triggered the development of new hardware (Raischel et al. ). Predictive modelling, including classification, clustering and association rules, has evolved from retrospective analytical techniques to techniques that allow organisations to predict outcomes based on historical data, for example, detecting possible credit card fraud at the point of sale based on features of the transaction.…”

Human Mobility Patterns at the Smallest Scales

“…As recently proposed [19], since the Eduroam data base covers the whole of Europe, a possible next step would be to consider the joint data base of several European universities. By properly matching the mask IDs and similar quantities at different universities, one would be able to keep track of individuals throughout Europe and apply the framework described above to data sets comprising larger and larger areas.…”

Smartphones in Higher Education Classrooms: Motivations, Rules, and Consequences

“…Even though eduroam has been implemented in many Brazilian universities, we have noted, after consulting CAPES/MEC periodicals portal, that there is little research about the network. Most studies we have found were conducted in other countries, such as Spain (CABALLERO et al, 2016;SÁNCHEZ et al, 2009) and regards technical matters, like the network authentication system (LÓPEZ et al, 2008;MURUGANANTAM et al, 2005;PÉREZ-MÉNDEZ et al, 2012) and mobility (RAISCHEL et al, 2014;CRUZ et al, 2013). The context of the research, which considers the implementation of eduroam, is addressed in the following topic, as well as the research methodology.…”