This book provides an overview of crowdsourced data management. Covering all aspects including the workflow, algorithms and research potential, it particularly focuses on the latest techniques and recent advances. The authors identify three key aspects in determining the performance of crowdsourced data management: quality control, cost control and latency control. By surveying and synthesizing a wide spectrum of studies on crowdsourced data management, the book outlines important factors that need to be considered to improve crowdsourced data management. It also introduces a practical crowdsourced-database-system design and presents a number of crowdsourced operators. Self-contained and covering theory, algorithms, techniques and applications, it is a valuable reference resource for researchers and students new to crowdsourced data management with a basic knowledge of data structures and databases.

This book provides an overview of crowdsourced data management. Covering all aspects including the workflow, algorithms and research potential, it particularly focuses on the latest techniques and recent advances. The authors identify three key aspects in determining the performance of crowdsourced data management: quality control, cost control and latency control. By surveying and synthesizing a wide spectrum of studies on crowdsourced data management, the book outlines important factors that need to be considered to improve crowdsourced data management. It also introduces a practical crowdsourced-database-system design and presents a number of crowdsourced operators. Self-contained and covering theory, algorithms, techniques and applications, it is a valuable reference resource for researchers and students new to crowdsourced data management with a basic knowledge of data structures and databases.

This book provides an overview of crowdsourced data management. Covering all aspects including the workflow, algorithms and research potential, it particularly focuses on the latest techniques and recent advances. The authors identify three key aspects in determining the performance of crowdsourced data management: quality control, cost control and latency control. By surveying and synthesizing a wide spectrum of studies on crowdsourced data management, the book outlines important factors that need to be considered to improve crowdsourced data management. It also introduces a practical crowdsourced-database-system design and presents a number of crowdsourced operators. Self-contained and covering theory, algorithms, techniques and applications, it is a valuable reference resource for researchers and students new to crowdsourced data management with a basic knowledge of data structures and databases.

Crowdsourced Data Management: Industry and Academic Perspectives aims to narrow the gap between academics and practitioners in this burgeoning field. It simultaneously introduces academics to real problems that practitioners encounter every day, and provides a survey of the state of the art for practitioners to incorporate into their designs.

This book collects selected high-quality papers published in 2018–2020 to inaugurate the “Natural Hazards” Section of the Geosciences journal. The topics encompass: trends in publications at international level in the field of natural hazards research; the role of Big Data in natural disaster management; assessment of seismic risk through the understanding and quantification of its different components; climatic/hydro-meteorological hazards; and finally, the scientific analysis and disaster forensics of recent natural hazard events. The target audience includes not only specialists, but also graduate students who wish to approach the challenging, but also fascinating

The evolution of knowledge management theory and the special emphasis on human and social capital sets new challenges for knowledge-driven and technology-enabled innovation. Emerging technologies including big data and analytics have significant implications for sustainability, policy making, and competitiveness. This edited volume promotes scientific research into the potential contributions knowledge management can make to the new era of innovation and social inclusive economic growth. We are grateful to all the contributors of this edition for their intellectual work. The organization of the relevant debate is aligned around three pillars: SECTION A. DATA, KNOWLEDGE, HUMAN AND SOCIAL CAPITAL FOR INNOVATION We elaborate on the new era of knowledge types and the emerging forms of social capital and their impact on technology-driven innovation. Topics include: · Social Networks · Smart Education · Social Capital · Corporate Innovation · Disruptive Innovation · Knowledge integration · Enhanced Decision-Making. SECTION B. KNOWLEDGE MANAGEMENT & BIG DATA ENABLED INNOVATION In this section, knowledge management and big data applications and systems are presented. Selective topic include: · Crowdsourcing Analysis · Natural Language Processing · Data Governance · Knowledge Extraction · Ontology Design Semantic Modeling SECTION C. SUSTAINABLE DEVELOPMENT In the section, the debate on the impact of knowledge management and big data research to sustainability is promoted with integrative discussion of complementary social and technological factors including: · Big Social Networks on Sustainable Economic Development · Business Intelligence

Moving away from the strong body of critique of pervasive ?bad data? practices by both governments and private actors in the globalized digital economy, this book aims to paint an alternative, more optimistic but still pragmatic picture of the datafied future. The authors examine and propose ?good data? practices, values and principles from an interdisciplinary, international perspective. From ideas of data sovereignty and justice, to manifestos for change and calls for activism, this collection opens a multifaceted conversation on the kinds of futures we want to see, and presents concrete steps on how we can start realizing good data in practice.