In the investigation Exploring the Boundaries of Big Data The Netherlands Scientific Council for Government Policy (WRR) offers building blocks for developing a regulatory approach to Big Data.

In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. The book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe. This compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment.

Over the past decade, since the publication of the first edition, there have been new advances in solving complex geoinformatics problems. Advancements in computing power, computing platforms, mathematical models, statistical models, geospatial algorithms, and the availability of data in various domains, among other things, have aided in the automation of complex real-world tasks and decision-making that inherently rely on geospatial data. Of the many fields benefiting from these latest advancements, machine learning, particularly deep learning, virtual reality, and game engine, have increasingly gained the interest of many researchers and practitioners. This revised new edition provides up-to-date knowledge on the latest developments related to these three fields for solving geoinformatics problems. FEATURES Contains a comprehensive collection of advanced big data approaches, techniques, and technologies for geoinformatics problems Provides seven new chapters on deep learning models, algorithms, and structures, including a new chapter on how spatial metaverse is used to build immersive realistic virtual experiences Presents information on how deep learning is used for solving real-world geoinformatics problems This book is intended for researchers, academics, professionals, and students in such fields as computing and information, civil and environmental engineering, environmental sciences, geosciences, geology, geography, and urban studies.

Big data, surveillance, crisis management. Three largely different and richly researched fields, however, the interplay amongst these three domains is rarely addressed. Through unique international case studies this book examines the links between these three fields. Considering crisis management as an 'umbrella term' that covers a number of crises and ways of managing them, this book explores the collection of ‘big data’ by governmental crisis organisations, as well as the unintended consequences of using such data. In particular, through the lens of surveillance, the contributions investigate how the use and abuse of big data can easily lead to monitoring and controlling the behaviour of people affected by crises. Readers will understand that big data in crisis management must be examined as a political process, involving questions of power and transparency. A highly topical volume, Big Data, Surveillance and Crisis Management will appeal to postgraduate students and postdoctoral researchers interested in fields including Sociology and Surveillance Studies, Disaster and Crisis Management, Media Studies, Governmentality, Organisation Theory and Information Society Studies.

Drawing on the theoretical debates, practical applications, and sectoral approaches in the field, this ground-breaking Handbook unpacks the political and regulatory developments in AI and big data governance. Covering the political implications of big data and AI on international relations, as well as emerging initiatives for legal regulation, it provides an accessible overview of ongoing data science discourses in politics, law and governance. This title contains one or more Open Access chapters.

This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: · Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. · Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. · Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. · Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation.

The concept of a risk-based approach to data protection came to the fore during the overhaul process of the EU's General Data Protection Regulation (GDPR). At its core, it consists of endowing the regulated organizations that process personal data with increased responsibility for complying with data protection mandates. Such increased compliance duties are performed through risk management tools. This book provides a comprehensive analysis of this legal and policy development, which considers a legal, historical, and theoretical perspective. By framing the risk-based approach as a sui generis implementation of a specific regulation model 'known as meta regulation, this book provides a recollection of the policy developments that led to the adoption of the risk-based approach in light of regulation theory and debates. It also discusses a number of salient issues pertaining to the risk-based approach, such as its rationale, scope, and meaning; the role for regulators; and its potential and limits. The book also looks at they way it has been undertaken in major statutes with a focus on key provisions, such as data protection impact assessments or accountability. Finally, the book devotes considerable attention to the notion of risk. It explains key terms such as risk assessment and management. It discusses in-depth the role of harms in data protection, the meaning of a data protection risk, and the difference between risks and harms. It also critically analyses prevalent data protection risk management methodologies and explains the most important caveats for managing data protection risks.

This book contains a range of invited and submitted papers presented at the 11th IFIP WG 9.2, 9.5, 9.6/11.7, 11.4, 11.6/SIG 9.2.2 International Summer School, held in Karlstad, Sweden, in August 2016. The 17 revised full papers and one short paper included in this volume were carefully selected from a total of 42 submissions and were subject to a two-step review process. The papers combine interdisciplinary approaches to bring together a host of perspectives: technical, legal, regulatory, socio-economic, social, societal, political, ethical, anthropological, philosophical, and psychological. The paper 'Big Data Privacy and Anonymization' is published open access under a CC BY 4.0 license at link.springer.com.