Big Data Mining for Climate Change

Big Data Mining for Climate Change PDF Author: Zhihua Zhang
Publisher: Elsevier
ISBN: 0128187034
Category : Science
Languages : en
Pages : 344

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Book Description
Climate change mechanisms, impacts, risks, mitigation, adaption, and governance are widely recognized as the biggest, most interconnected problem facing humanity. Big Data Mining for Climate Change addresses one of the fundamental issues facing scientists of climate or the environment: how to manage the vast amount of information available and analyse it. The resulting integrated and interdisciplinary big data mining approaches are emerging, partially with the help of the United Nation's big data climate challenge, some of which are recommended widely as new approaches for climate change research. Big Data Mining for Climate Change delivers a rich understanding of climate-related big data techniques and highlights how to navigate huge amount of climate data and resources available using big data applications. It guides future directions and will boom big-data-driven researches on modeling, diagnosing and predicting climate change and mitigating related impacts. This book mainly focuses on climate network models, deep learning techniques for climate dynamics, automated feature extraction of climate variability, and sparsification of big climate data. It also includes a revelatory exploration of big-data-driven low-carbon economy and management. Its content provides cutting-edge knowledge for scientists and advanced students studying climate change from various disciplines, including atmospheric, oceanic and environmental sciences; geography, ecology, energy, economics, management, engineering, and public policy.

Big Data Mining for Climate Change

Big Data Mining for Climate Change PDF Author: Zhihua Zhang
Publisher: Elsevier
ISBN: 0128187034
Category : Science
Languages : en
Pages : 344

Get Book Here

Book Description
Climate change mechanisms, impacts, risks, mitigation, adaption, and governance are widely recognized as the biggest, most interconnected problem facing humanity. Big Data Mining for Climate Change addresses one of the fundamental issues facing scientists of climate or the environment: how to manage the vast amount of information available and analyse it. The resulting integrated and interdisciplinary big data mining approaches are emerging, partially with the help of the United Nation's big data climate challenge, some of which are recommended widely as new approaches for climate change research. Big Data Mining for Climate Change delivers a rich understanding of climate-related big data techniques and highlights how to navigate huge amount of climate data and resources available using big data applications. It guides future directions and will boom big-data-driven researches on modeling, diagnosing and predicting climate change and mitigating related impacts. This book mainly focuses on climate network models, deep learning techniques for climate dynamics, automated feature extraction of climate variability, and sparsification of big climate data. It also includes a revelatory exploration of big-data-driven low-carbon economy and management. Its content provides cutting-edge knowledge for scientists and advanced students studying climate change from various disciplines, including atmospheric, oceanic and environmental sciences; geography, ecology, energy, economics, management, engineering, and public policy.

Big Data Mining for Climate Change

Big Data Mining for Climate Change PDF Author: Zhihua Zhang
Publisher: Elsevier
ISBN: 0128187042
Category : Science
Languages : en
Pages : 346

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Book Description
Climate change mechanisms, impacts, risks, mitigation, adaption, and governance are widely recognized as the biggest, most interconnected problem facing humanity. Big Data Mining for Climate Change addresses one of the fundamental issues facing scientists of climate or the environment: how to manage the vast amount of information available and analyse it. The resulting integrated and interdisciplinary big data mining approaches are emerging, partially with the help of the United Nation's big data climate challenge, some of which are recommended widely as new approaches for climate change research. Big Data Mining for Climate Change delivers a rich understanding of climate-related big data techniques and highlights how to navigate huge amount of climate data and resources available using big data applications. It guides future directions and will boom big-data-driven researches on modeling, diagnosing and predicting climate change and mitigating related impacts. This book mainly focuses on climate network models, deep learning techniques for climate dynamics, automated feature extraction of climate variability, and sparsification of big climate data. It also includes a revelatory exploration of big-data-driven low-carbon economy and management. Its content provides cutting-edge knowledge for scientists and advanced students studying climate change from various disciplines, including atmospheric, oceanic and environmental sciences; geography, ecology, energy, economics, management, engineering, and public policy. - Provides a step-by-step guide for applying big data mining tools to climate and environmental research - Presents a comprehensive review of theory and algorithms of big data mining for climate change - Includes current research in climate and environmental science as it relates to using big data algorithms

Data Science Applied to Sustainability Analysis

Data Science Applied to Sustainability Analysis PDF Author: Jennifer Dunn
Publisher: Elsevier
ISBN: 0128179775
Category : Science
Languages : en
Pages : 312

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Book Description
Data Science Applied to Sustainability Analysis focuses on the methodological considerations associated with applying this tool in analysis techniques such as lifecycle assessment and materials flow analysis. As sustainability analysts need examples of applications of big data techniques that are defensible and practical in sustainability analyses and that yield actionable results that can inform policy development, corporate supply chain management strategy, or non-governmental organization positions, this book helps answer underlying questions. In addition, it addresses the need of data science experts looking for routes to apply their skills and knowledge to domain areas. - Presents data sources that are available for application in sustainability analyses, such as market information, environmental monitoring data, social media data and satellite imagery - Includes considerations sustainability analysts must evaluate when applying big data - Features case studies illustrating the application of data science in sustainability analyses

Big Data, Data Mining, and Machine Learning

Big Data, Data Mining, and Machine Learning PDF Author: Jared Dean
Publisher: John Wiley & Sons
ISBN: 1118920708
Category : Computers
Languages : en
Pages : 293

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Book Description
With big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency. With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders. This book provides a view into the topic that executives, managers, and practitioners require, and includes: A complete overview of big data and its notable characteristics Details on high performance computing architectures for analytics, massively parallel processing (MPP), and in-memory databases Comprehensive coverage of data mining, text analytics, and machine learning algorithms A discussion of explanatory and predictive modeling, and how they can be applied to decision-making processes Big Data, Data Mining, and Machine Learning provides technology and marketing executives with the complete resource that has been notably absent from the veritable libraries of published books on the topic. Take control of your organization's big data analytics to produce real results with a resource that is comprehensive in scope and light on hyperbole.

How to Avoid a Climate Disaster

How to Avoid a Climate Disaster PDF Author: Bill Gates
Publisher: Vintage
ISBN: 0385546149
Category : Science
Languages : en
Pages : 201

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Book Description
#1 NEW YORK TIMES BEST SELLER • In this urgent, authoritative book, Bill Gates sets out a wide-ranging, practical—and accessible—plan for how the world can get to zero greenhouse gas emissions in time to avoid a climate catastrophe. Bill Gates has spent a decade investigating the causes and effects of climate change. With the help of experts in the fields of physics, chemistry, biology, engineering, political science, and finance, he has focused on what must be done in order to stop the planet's slide to certain environmental disaster. In this book, he not only explains why we need to work toward net-zero emissions of greenhouse gases, but also details what we need to do to achieve this profoundly important goal. He gives us a clear-eyed description of the challenges we face. Drawing on his understanding of innovation and what it takes to get new ideas into the market, he describes the areas in which technology is already helping to reduce emissions, where and how the current technology can be made to function more effectively, where breakthrough technologies are needed, and who is working on these essential innovations. Finally, he lays out a concrete, practical plan for achieving the goal of zero emissions—suggesting not only policies that governments should adopt, but what we as individuals can do to keep our government, our employers, and ourselves accountable in this crucial enterprise. As Bill Gates makes clear, achieving zero emissions will not be simple or easy to do, but if we follow the plan he sets out here, it is a goal firmly within our reach.

Big Data

Big Data PDF Author: Viktor Mayer-Schönberger
Publisher: Houghton Mifflin Harcourt
ISBN: 0544002695
Category : Business & Economics
Languages : en
Pages : 257

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Book Description
A exploration of the latest trend in technology and the impact it will have on the economy, science, and society at large.

The History Manifesto

The History Manifesto PDF Author: Jo Guldi
Publisher: Cambridge University Press
ISBN: 1316165256
Category : Political Science
Languages : en
Pages : 177

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Book Description
How should historians speak truth to power – and why does it matter? Why is five hundred years better than five months or five years as a planning horizon? And why is history – especially long-term history – so essential to understanding the multiple pasts which gave rise to our conflicted present? The History Manifesto is a call to arms to historians and everyone interested in the role of history in contemporary society. Leading historians Jo Guldi and David Armitage identify a recent shift back to longer-term narratives, following many decades of increasing specialisation, which they argue is vital for the future of historical scholarship and how it is communicated. This provocative and thoughtful book makes an important intervention in the debate about the role of history and the humanities in a digital age. It will provoke discussion among policymakers, activists and entrepreneurs as well as ordinary listeners, viewers, readers, students and teachers. This title is also available as Open Access.

Data Mining and Big Data Techniques and Applications

Data Mining and Big Data Techniques and Applications PDF Author: Dr.D.Deepa
Publisher: Leilani Katie Publication
ISBN: 9363481107
Category : Computers
Languages : en
Pages : 203

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Book Description
Dr.D.Deepa, Assistant Professor, Department of Computer Applications, Agurchand Manmull Jain College, Chennai, Tamil Nadu, India. Mr.C.Muruganandam, Assistant Professor, Department of Computer Science, Rajah Serfoji Government College (Autonomous), Thanjavur, Tamil Nadu, India. Mr.P.Ganesh Babu, Guest Lecturer, Department of Computer Applications, Madurai Kamaraj University, Madurai, Tamil Nadu, India.

Predictive Analytics, Data Mining and Big Data

Predictive Analytics, Data Mining and Big Data PDF Author: S. Finlay
Publisher: Springer
ISBN: 1137379286
Category : Business & Economics
Languages : en
Pages : 241

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Book Description
This in-depth guide provides managers with a solid understanding of data and data trends, the opportunities that it can offer to businesses, and the dangers of these technologies. Written in an accessible style, Steven Finlay provides a contextual roadmap for developing solutions that deliver benefits to organizations.

Data Mining and Knowledge Discovery for Big Data

Data Mining and Knowledge Discovery for Big Data PDF Author: Wesley W. Chu
Publisher: Springer Science & Business Media
ISBN: 3642408370
Category : Technology & Engineering
Languages : en
Pages : 314

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Book Description
The field of data mining has made significant and far-reaching advances over the past three decades. Because of its potential power for solving complex problems, data mining has been successfully applied to diverse areas such as business, engineering, social media, and biological science. Many of these applications search for patterns in complex structural information. In biomedicine for example, modeling complex biological systems requires linking knowledge across many levels of science, from genes to disease. Further, the data characteristics of the problems have also grown from static to dynamic and spatiotemporal, complete to incomplete, and centralized to distributed, and grow in their scope and size (this is known as big data). The effective integration of big data for decision-making also requires privacy preservation. The contributions to this monograph summarize the advances of data mining in the respective fields. This volume consists of nine chapters that address subjects ranging from mining data from opinion, spatiotemporal databases, discriminative subgraph patterns, path knowledge discovery, social media, and privacy issues to the subject of computation reduction via binary matrix factorization.