Related papers: "Big Data" and its Origins
Currently, experts from IT industry are closely monitoring the soaring total volume of digital data. Moreover the problem is not purely technical, it directly affects human civilization as a whole. The growth rate of the all increasing and…
Bioinformatics research is characterized by voluminous and incremental datasets and complex data analytics methods. The machine learning methods used in bioinformatics are iterative and parallel. These methods can be scaled to handle big…
The so-called great divergence in the income per capita is described in the Unified Growth Theory as the mind-boggling and unresolved mystery about the growth process. This mystery has now been solved: the great divergence never happened.…
We have witnessed an exponential growth in commercial data services, which has lead to the 'big data era'. Machine learning, as one of the most promising artificial intelligence tools of analyzing the deluge of data, has been invoked in…
The research on and application of artificial intelligence (AI) has triggered a comprehensive scientific, economic, social and political discussion. Here we argue that statistics, as an interdisciplinary scientific field, plays a…
Turing's (1936) paper on computable numbers has played its role in underpinning different perspectives on the world of information. On the one hand, it encourages a digital ontology, with a perceived flatness of computational structure…
Humans are expert in the amount of sensory data they deal with each moment. Human brain not only analyses these data but also starts synthesizing new information from the existing data. The current age Big-data systems are needed not just…
Brain signal data are inherently big: massive in amount, complex in structure, and high in dimensions. These characteristics impose great challenges for statistical inference and learning. Here we review several key challenges, discuss…
Synthetic data is often positioned as a solution to replace sensitive fixed-size datasets with a source of unlimited matching data, freed from privacy concerns. There has been much progress in synthetic data generation over the last decade,…
Advances in data analytics bring with them civil rights implications. Data-driven and algorithmic decision making increasingly determine how businesses target advertisements to consumers, how police departments monitor individuals or…
Massive data from software repositories and collaboration tools are widely used to study social aspects in software development. One question that several recent works have addressed is how a software project's size and structure influence…
The humble spreadsheet is the most widely used data storage, manipulation and modelling tool. Its ubiquity over the past 30 years has seen its successful application in every area of life. Surprisingly the spreadsheet has remained…
The use of statistical software in academia and enterprises has been evolving over the last years. More often than not, students, professors, workers, and users, in general, have all had, at some point, exposure to statistical software.…
Analytics corresponds to a relevant and challenging phase of Big Data. The generation of knowledge from extensive data sets (petabyte era) of varying types, occurring at a speed able to serve decision makers, is practiced using multiple…
Large-scale databases of human activity in social media have captured scientific and policy attention, producing a flood of research and discussion. This paper considers methodological and conceptual challenges for this emergent field, with…
While manufacturers have been generating highly distributed data from various systems, devices and applications, a number of challenges in both data management and data analysis require new approaches to support the big data era. These…
The last two decades witnessed tremendous advances in the Information and Communications Technologies. Beside improvements in computational power and storage capacity, communication networks carry nowadays an amount of data which was not…
Synthetic Data is not new, but recent advances in Generative AI have raised interest in expanding the research toolbox, creating new opportunities and risks. This article provides a taxonomy of the full breadth of the Synthetic Data domain.…
Event data are prevalent in diverse domains such as financial trading, business workflows and industrial IoT nowadays. An event is often characterized by several attributes denoting the meaning associated with the corresponding occurrence…
Data science is not a science. It is a research paradigm. Its power, scope, and scale will surpass science, our most powerful research paradigm, to enable knowledge discovery and change our world. We have yet to understand and define it,…