Suchergebnisse

One of the biggest questions in the digital age pertains to the relevance of democracy in the era of big data. Unquestioningly, the digital revolution is growing at a rapid pace and many are being caught unaware by its impact in various avenues. The amount of data available has been doubling year on year, and the conditions of usage have been evolving at a pace faster than the policies are being provided to ensure proper usage. Many have started looking for ways of turning big data into big money, an aspect that is coming at the expense of the democratic values countries have upheld for generations. Instead, it is becoming a huge problem as the history of humankind is becoming more documented now and messages are easier to send compared to a century ago. Understanding the impact of big data on democracy can help accentuate the best way of improving democratic institutions and their ability to overcome the pressure coming from evolving technology.

"Finding patterns in massive event streams can be difficult, but learning how to find them doesn't have to be. This unique hands-on guide shows you how to solve this and many other problems in large-scale data processing with simple, fun, and elegant tools that leverage Apache Hadoop. You'll gain a practical, actionable view of big data by working with real data and real problems. Perfect for beginners, this book's approach will also appeal to experienced practitioners who want to brush up on their skills. Part I explains how Hadoop and MapReduce work, while Part II covers many analytic patterns you can use to process any data. As you work through several exercises, you'll also learn how to use Apache Pig to process data"--

Source criticism is an epistemological practice in social and cultural studies that is crucial for specifying the range and scope of the findings, or in other words their validity and reliability. In the context of big data, source criticism is not yet established in the fashion as it is known in other areas of social and cultural research. Currently emerging discussions in historical research emphasize the relevance of source criticism of digital objects or data. In the context of these discussions, this contribution suggests exploring the potentials of source criticism for platform logics. We focus on big data sourced from the internet. Nevertheless our results aim to be transferrable to other sources of big data. The inclusion of source criticism into big data analysis may in turn foster the integration of data-driven analyses into social and cultural studies research approaches. For an integration of source criticism, the paper proposes source critical analyses of information systems, in particular internet platforms, in big data analysis with regard to a) types of big data platforms, b) researchers as data makers, and c) mixed realities of platform usage practices. In analogy to source repertoires (Quellentypen) it suggests to classify internet platforms as providers of particular types of big data sources depending on their infrastructural materiality and ontologies for tracing the key issues of (external) source criticism: provenance, authenticity, and integrity.

Our world is being transformed by big data. The growth of the Internet and the rapid expansion of mobile communications and related technologies have created a massive flow of data--both structured and unstructured. The availability and use of that data has enormous implications for businesses and for the wider society. Used effectively, big data can drive businesses in the direction of more accurate analyses of vital information, leading ultimately to greater operational efficiencies, cost reductions, reduced risk, speedier innovations, and increased and new revenue. In this book, you'll find detailed instruction in big data strategy development and implementation, supported by numerous real-world business cases in ten distinct industries. You will learn what big data is and how to wield it--from calculating ROI and making a business case to developing overall and project-specific strategies that actually work. Each chapter answers key questions and will give you the skills you need to make your big data projects succeed

AbstractThe introduction of Big Data sets in the healthcare domain has presented opportunities to engage in analytics of very large sets containing both structured and unstructured data. With advances in information technology (IT), these data sets have become available from diverse sources at greatly increased rates. The availability of Big Data sets has introduced complexities that we must address, not only in terms of semantics and analytics but also in terms of data management, storage, and distribution. Currently, the capabilities to ingest, analyze, and manage multipetabyte data sets have underscored the limitations of our analytics capabilities supported by relational database management systems. This essay argues that an ontology‐based approach to data analytics provides a practical framework to address the semantic challenges presented by Big Data sets. No ontological framework can address the operational and management requirements introduced by the availability of Big Data sets, however. There are also a number of IT architectural factors that must be considered in implementing such a framework.

This paper has reviewed the field of Data Science and how Data Science techniques can be applied to building energy management. Specifically, we have focused on building operation, energy load prediction, and identification of consumption patterns. Our experiments show that Big Data technologies can solve the computational problems that appear when processing of large amounts of data, which are likely to have an increasing relevance with the advent of the Internet of Things –with smart meters and appliances fully connected to the Internet. However, the applications to real-world scenarios are still scarce. In our experience, one of the most important aspects to improve is achieving a greater involvement of the building managers in the data analysis process. To do this, future research work should explore two complementary directions, namely, showing the potential of Data Science to building managers, and developing more user-friendly algorithms and tools. In this way, we expect that new approaches will be less opaque, easier to use, more customizable, and above all other features, more engaging. ; Tesis Univ. Granada. ; Spanish Ministries of Science, Innovation and Universities (TIN2017-91223-EXP) ; Economy and Competitiveness (TIN2015-64776-C3-1-R) ; Energy IN TIME project from the EU 7o Framework Programme (grant agreement No. 608981) ; COPKIT project from the EU 8 Framework Programme (H2020) research ; Innovation programme (grant agreement No 786687) ; University of Granada (Programa de Proyectos para la incorporación de jóvenes doctores a nuevas líneas de investigación) ; Spanish Government (TIN2012-30939 project)

What's wrong with targeted advertising in political campaigns? Are echo chambers a matter of genuine concern? How does data collection impact on trust in society? As decision-making becomes increasingly automated, how can decision-makers be held to account? This collection consider potential solutions to these challenges.