After Theory Before Big Data
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After Theory Before Big Data
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Author : Friedrich Kratochwil
language : en
Publisher: Routledge
Release Date : 2021-07-13
After Theory Before Big Data written by Friedrich Kratochwil and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-13 with Political Science categories.
This book’s key purpose is to contribute to the ongoing "theoretical" discussion in the field of international relations (IR) concerning the status of grand theories. However, it also has a wider, critical mission: to challenge mainstream social science and its dominant methodology, as well as the unfettered optimism that the problem of social order can be solved by the "application" of scientific knowledge to our practical problems. The author uses rigorous philosophical analysis to focus on the unexamined assumptions that form the bedrock of many contemporary scholars in IR and demonstrates the unavailability of a universal "scientific" procedure for finding the facts, when we face practical choices and issues of social reproduction. This book will be of interest to upper-level students of IR, sociology, history, and philosophy of science; it will also speak to students of security, foreign policy making, migration, and political economy, in addressing the basis of their attitudes in thinking about the world and the role of scholarship.
Mathematical Foundations Of Big Data Analytics
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Author : Vladimir Shikhman
language : en
Publisher: Springer Nature
Release Date : 2021-02-11
Mathematical Foundations Of Big Data Analytics written by Vladimir Shikhman and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-11 with Computers categories.
In this textbook, basic mathematical models used in Big Data Analytics are presented and application-oriented references to relevant practical issues are made. Necessary mathematical tools are examined and applied to current problems of data analysis, such as brand loyalty, portfolio selection, credit investigation, quality control, product clustering, asset pricing etc. – mainly in an economic context. In addition, we discuss interdisciplinary applications to biology, linguistics, sociology, electrical engineering, computer science and artificial intelligence. For the models, we make use of a wide range of mathematics – from basic disciplines of numerical linear algebra, statistics and optimization to more specialized game, graph and even complexity theories. By doing so, we cover all relevant techniques commonly used in Big Data Analytics.Each chapter starts with a concrete practical problem whose primary aim is to motivate the study of a particular Big Data Analytics technique. Next, mathematical results follow – including important definitions, auxiliary statements and conclusions arising. Case-studies help to deepen the acquired knowledge by applying it in an interdisciplinary context. Exercises serve to improve understanding of the underlying theory. Complete solutions for exercises can be consulted by the interested reader at the end of the textbook; for some which have to be solved numerically, we provide descriptions of algorithms in Python code as supplementary material.This textbook has been recommended and developed for university courses in Germany, Austria and Switzerland.
Deep Learning Convergence To Big Data Analytics
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Author : Murad Khan
language : en
Publisher: Springer
Release Date : 2018-12-30
Deep Learning Convergence To Big Data Analytics written by Murad Khan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-30 with Computers categories.
This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various challenges, such as fast, accurate and efficient processing of big data in real-time. In addition, the Internet of Things is progressively increasing in various fields, like smart cities, smart homes, and e-health. As the enormous number of connected devices generate huge amounts of data every day, we need sophisticated algorithms to deal, organize, and classify this data in less processing time and space. Similarly, existing techniques and algorithms for deep learning in big data field have several advantages thanks to the two main branches of the deep learning, i.e. convolution and deep belief networks. This book offers insights into these techniques and applications based on these two types of deep learning. Further, it helps students, researchers, and newcomers understand big data analytics based on deep learning approaches. It also discusses various machine learning techniques in concatenation with the deep learning paradigm to support high-end data processing, data classifications, and real-time data processing issues. The classification and presentation are kept quite simple to help the readers and students grasp the basics concepts of various deep learning paradigms and frameworks. It mainly focuses on theory rather than the mathematical background of the deep learning concepts. The book consists of 5 chapters, beginning with an introductory explanation of big data and deep learning techniques, followed by integration of big data and deep learning techniques and lastly the future directions.
Big Data Analytics In Supply Chain Management
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Author : Iman Rahimi
language : en
Publisher: CRC Press
Release Date : 2020-12-20
Big Data Analytics In Supply Chain Management written by Iman Rahimi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-20 with Computers categories.
In a world of soaring digitization, social media, financial transactions, and production and logistics processes constantly produce massive data. Employing analytical tools to extract insights and foresights from data improves the quality, speed, and reliability of solutions to highly intertwined issues faced in supply chain operations. From procurement in Industry 4.0 to sustainable consumption behavior to curriculum development for data scientists, this book offers a wide array of techniques and theories of Big Data Analytics applied to Supply Chain Management. It offers a comprehensive overview and forms a new synthesis by bringing together seemingly divergent fields of research. Intended for Engineering and Business students, scholars, and professionals, this book is a collection of state-of-the-art research and best practices to spur discussion about and extend the cumulant knowledge of emerging supply chain problems.
Big Data Concepts Theories And Applications
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Author : Shui Yu
language : en
Publisher: Springer
Release Date : 2016-03-03
Big Data Concepts Theories And Applications written by Shui Yu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-03 with Computers categories.
This book covers three major parts of Big Data: concepts, theories and applications. Written by world-renowned leaders in Big Data, this book explores the problems, possible solutions and directions for Big Data in research and practice. It also focuses on high level concepts such as definitions of Big Data from different angles; surveys in research and applications; and existing tools, mechanisms, and systems in practice. Each chapter is independent from the other chapters, allowing users to read any chapter directly. After examining the practical side of Big Data, this book presents theoretical perspectives. The theoretical research ranges from Big Data representation, modeling and topology to distribution and dimension reducing. Chapters also investigate the many disciplines that involve Big Data, such as statistics, data mining, machine learning, networking, algorithms, security and differential geometry. The last section of this book introduces Big Data applications from different communities, such as business, engineering and science. Big Data Concepts, Theories and Applications is designed as a reference for researchers and advanced level students in computer science, electrical engineering and mathematics. Practitioners who focus on information systems, big data, data mining, business analysis and other related fields will also find this material valuable.
Understanding China Through Big Data
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Author : Yunsong Chen
language : en
Publisher: Routledge
Release Date : 2021-07-15
Understanding China Through Big Data written by Yunsong Chen and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-15 with Social Science categories.
Chen, He and Yan present a range of applications of multiple-source big data to core areas of contemporary sociology, demonstrating how a theory-guided approach to macrosociology can help to understand social change in China, especially where traditional approaches are limited by constrained and biased data. In each chapter of the book, the authors highlight an application of theory-guided macrosociology that has the potential to reinvigorate an ambitious, open-minded and bold approach to sociological research. These include social stratification, social networks, medical care, and online behaviours among many others. This research approach focuses on macro-level social process and phenomena by using quantitative models to statistically test for associations and causalities suggested by a clearly hypothesised social theory. By deploying theory-oriented macrosociology where it can best assure macro-level robustness and reliability, big data applications can be more relevant to and guided by social theory. An essential read for sociologists with an interest in quantitative and macro-scale research methods, which also provides fascinating insights into Chinese society as a demonstration of the utility of its methodology.
Power Relations And Comparative Regionalism
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Author : Min-hyung Kim
language : en
Publisher: Routledge
Release Date : 2021-07-29
Power Relations And Comparative Regionalism written by Min-hyung Kim and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-29 with Political Science categories.
Three trends have dominated the political economy of integration during the last two decades: globalization, economic nationalism, and regionalization. This book explores comparative regional integration, focusing on both intra regional integration and relations among regions in the context of power. The most common focus of integration studies has been on the logic of cooperation, but there is another logic of integration: power. The relevance of power today is represented by the relations within the Eurozone, especially between creditors and debtors. By the same line of reasoning, integration in Asia cannot ignore the respective roles of China, Japan, and Korea, nor the unresolved disputes about Taiwan, Hong Kong, and the islands in the South China Sea. This edited volume addresses the role of power in regional integration in three contexts: (1) the role of hegemonic external actors (the US and China) in regional integration; (2) the role of core states within regions (Germany, China , Japan, and Brazil); and (3) the role of noncore states- smaller and middle range powers (Italy and Greece in Europe; South Korea and Malaysia in Asia; and Argentina, Colombia, Uruguay, and Paraguay in Latin America). This book will benefit students and scholars of international relations and comparative political economy, especially those with an interest in integration studies and comparative regionalism.
Big Data A New Medium
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Author : Natasha Lushetich
language : en
Publisher: Routledge
Release Date : 2020-11-26
Big Data A New Medium written by Natasha Lushetich and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-26 with Social Science categories.
Drawing on a range of methods from across science and technology studies, digital humanities and digital arts, this book presents a comprehensive view of the big data phenomenon. Big data architectures are increasingly transforming political questions into technical management by determining classificatory systems in the social, educational, and healthcare realms. Data, and their multiple arborisations, have become new epistemic landscapes. They have also become new existential terrains. The fundamental question is: can big data be seen as a new medium in the way photography or film were when they first appeared? No new medium is ever truly new. It’s always remediation of older media. What is new is the medium’s re-articulation of the difference between here and there, before and after, yours and mine, knowable and unknowable, possible and impossible. This transdisciplinary volume, incorporating cultural and media theory, art, philosophy, history, and political philosophy is a key resource for readers interested in digital humanities, cultural, and media studies.
Big Data Analytics And Cloud Computing
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Author : Marcello Trovati
language : en
Publisher: Springer
Release Date : 2016-01-12
Big Data Analytics And Cloud Computing written by Marcello Trovati and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-12 with Computers categories.
This book reviews the theoretical concepts, leading-edge techniques and practical tools involved in the latest multi-disciplinary approaches addressing the challenges of big data. Illuminating perspectives from both academia and industry are presented by an international selection of experts in big data science. Topics and features: describes the innovative advances in theoretical aspects of big data, predictive analytics and cloud-based architectures; examines the applications and implementations that utilize big data in cloud architectures; surveys the state of the art in architectural approaches to the provision of cloud-based big data analytics functions; identifies potential research directions and technologies to facilitate the realization of emerging business models through big data approaches; provides relevant theoretical frameworks, empirical research findings, and numerous case studies; discusses real-world applications of algorithms and techniques to address the challenges of big datasets.
Big Data Science In Finance
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Author : Irene Aldridge
language : en
Publisher: John Wiley & Sons
Release Date : 2020-12-31
Big Data Science In Finance written by Irene Aldridge and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-31 with Computers categories.
Explains the mathematics, theory, and methods of Big Data as applied to finance and investing Data science has fundamentally changed Wall Street—applied mathematics and software code are increasingly driving finance and investment-decision tools. Big Data Science in Finance examines the mathematics, theory, and practical use of the revolutionary techniques that are transforming the industry. Designed for mathematically-advanced students and discerning financial practitioners alike, this energizing book presents new, cutting-edge content based on world-class research taught in the leading Financial Mathematics and Engineering programs in the world. Marco Avellaneda, a leader in quantitative finance, and quantitative methodology author Irene Aldridge help readers harness the power of Big Data. Comprehensive in scope, this book offers in-depth instruction on how to separate signal from noise, how to deal with missing data values, and how to utilize Big Data techniques in decision-making. Key topics include data clustering, data storage optimization, Big Data dynamics, Monte Carlo methods and their applications in Big Data analysis, and more. This valuable book: Provides a complete account of Big Data that includes proofs, step-by-step applications, and code samples Explains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) Covers vital topics in the field in a clear, straightforward manner Compares, contrasts, and discusses Big Data and Small Data Includes Cornell University-tested educational materials such as lesson plans, end-of-chapter questions, and downloadable lecture slides Big Data Science in Finance: Mathematics and Applications is an important, up-to-date resource for students in economics, econometrics, finance, applied mathematics, industrial engineering, and business courses, and for investment managers, quantitative traders, risk and portfolio managers, and other financial practitioners.