Download or read book Big Data Analytics in Cognitive Social Media and Literary Texts written by Sanjiv Sharma and published by Springer Nature. This book was released on 2021-10-10 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive overview of the theory and praxis of Big Data Analytics and how these are used to extract cognition-related information from social media and literary texts. It presents analytics that transcends the borders of discipline-specific academic research and focuses on knowledge extraction, prediction, and decision-making in the context of individual, social, and national development. The content is divided into three main sections: the first of which discusses various approaches associated with Big Data Analytics, while the second addresses the security and privacy of big data in social media, and the last focuses on the literary text as the literary data in Big Data Analytics. Sharing valuable insights into the etiology behind human cognition and its reflection in social media and literary texts, the book benefits all those interested in analytics that can be applied to literature, history, philosophy, linguistics, literary theory, media & communication studies and computational/digital humanities.

Download or read book Data Science in Theory and Practice written by Maria Cristina Mariani and published by John Wiley & Sons. This book was released on 2021-10-12 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: DATA SCIENCE IN THEORY AND PRACTICE EXPLORE THE FOUNDATIONS OF DATA SCIENCE WITH THIS INSIGHTFUL NEW RESOURCE Data Science in Theory and Practice delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling. The book offers readers a multitude of topics all relevant to the analysis of complex data sets. Along with a robust exploration of the theory underpinning data science, it contains numerous applications to specific and practical problems. The book also provides examples of code algorithms in R and Python and provides pseudo-algorithms to port the code to any other language. Ideal for students and practitioners without a strong background in data science, readers will also learn from topics like: Analyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis A comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity Introductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages An exploration of algorithms, including how to write one and how to perform an asymptotic analysis A comprehensive discussion of several techniques for analyzing and predicting complex data sets Perfect for advanced undergraduate and graduate students in Data Science, Business Analytics, and Statistics programs, Data Science in Theory and Practice will also earn a place in the libraries of practicing data scientists, data and business analysts, and statisticians in the private sector, government, and academia.

Download or read book Big Data and Learning Analytics in Higher Education written by Ben Kei Daniel and published by Springer. This book was released on 2016-08-27 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: ​This book focuses on the uses of big data in the context of higher education. The book describes a wide range of administrative and operational data gathering processes aimed at assessing institutional performance and progress in order to predict future performance, and identifies potential issues related to academic programming, research, teaching and learning​. Big data refers to data which is fundamentally too big and complex and moves too fast for the processing capacity of conventional database systems. The value of big data is the ability to identify useful data and turn it into useable information by identifying patterns and deviations from patterns​.

Download or read book Big Data in the Arts and Humanities written by Giovanni Schiuma and published by CRC Press. This book was released on 2018-04-27 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: As digital technologies occupy a more central role in working and everyday human life, individual and social realities are increasingly constructed and communicated through digital objects, which are progressively replacing and representing physical objects. They are even shaping new forms of virtual reality. This growing digital transformation coupled with technological evolution and the development of computer computation is shaping a cyber society whose working mechanisms are grounded upon the production, deployment, and exploitation of big data. In the arts and humanities, however, the notion of big data is still in its embryonic stage, and only in the last few years, have arts and cultural organizations and institutions, artists, and humanists started to investigate, explore, and experiment with the deployment and exploitation of big data as well as understand the possible forms of collaborations based on it. Big Data in the Arts and Humanities: Theory and Practice explores the meaning, properties, and applications of big data. This book examines therelevance of big data to the arts and humanities, digital humanities, and management of big data with and for the arts and humanities. It explores the reasons and opportunities for the arts and humanities to embrace the big data revolution. The book also delineates managerial implications to successfully shape a mutually beneficial partnership between the arts and humanities and the big data- and computational digital-based sciences. Big data and arts and humanities can be likened to the rational and emotional aspects of the human mind. This book attempts to integrate these two aspects of human thought to advance decision-making and to enhance the expression of the best of human life.

Download or read book Literature Theory and Big Data written by Ana Gallego Cuiñas and published by de Gruyter. This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: By analyzing the citation flows that shape the current geopolitics of knowledge, this book offers to map the production of academic knowledge in Europe and America, taking in to account the specific role of Ibero-American academic thought. Combining the quantitative tools of Big Data and qualitative, critical, humanist and decolonial analysis, it aims at overcoming what some theorists have criticized as "dataism" in the Digital Humanities.

Download or read book Small Wars Big Data written by Eli Berman and published by Princeton University Press. This book was released on 2018-06-12 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: How a new understanding of warfare can help the military fight today’s conflicts more effectively The way wars are fought has changed starkly over the past sixty years. International military campaigns used to play out between large armies at central fronts. Today's conflicts find major powers facing rebel insurgencies that deploy elusive methods, from improvised explosives to terrorist attacks. Small Wars, Big Data presents a transformative understanding of these contemporary confrontations and how they should be fought. The authors show that a revolution in the study of conflict--enabled by vast data, rich qualitative evidence, and modern methods—yields new insights into terrorism, civil wars, and foreign interventions. Modern warfare is not about struggles over territory but over people; civilians—and the information they might choose to provide—can turn the tide at critical junctures. The authors draw practical lessons from the past two decades of conflict in locations ranging from Latin America and the Middle East to Central and Southeast Asia. Building an information-centric understanding of insurgencies, the authors examine the relationships between rebels, the government, and civilians. This approach serves as a springboard for exploring other aspects of modern conflict, including the suppression of rebel activity, the role of mobile communications networks, the links between aid and violence, and why conventional military methods might provide short-term success but undermine lasting peace. Ultimately the authors show how the stronger side can almost always win the villages, but why that does not guarantee winning the war. Small Wars, Big Data provides groundbreaking perspectives for how small wars can be better strategized and favorably won to the benefit of the local population.

Download or read book Applications of Big Data in Healthcare written by Ashish Khanna and published by Elsevier. This book was released on 2021-03-12 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applications of Big Data in Healthcare: Theory and Practice begins with the basics of Big Data analysis and introduces the tools, processes and procedures associated with Big Data analytics. The book unites healthcare with Big Data analysis and uses the advantages of the latter to solve the problems faced by the former. The authors present the challenges faced by the healthcare industry, including capturing, storing, searching, sharing and analyzing data. This book illustrates the challenges in the applications of Big Data and suggests ways to overcome them, with a primary emphasis on data repositories, challenges, and concepts for data scientists, engineers and clinicians. The applications of Big Data have grown tremendously within the past few years and its growth can not only be attributed to its competence to handle large data streams but also to its abilities to find insights from complex, noisy, heterogeneous, longitudinal and voluminous data. The main objectives of Big Data in the healthcare sector is to come up with ways to provide personalized healthcare to patients by taking into account the enormous amounts of already existing data. Provides case studies that illustrate the business processes underlying the use of big data and deep learning health analytics to improve health care delivery Supplies readers with a foundation for further specialized study in clinical analysis and data management Includes links to websites, videos, articles and other online content to expand and support the primary learning objectives for each major section of the book

Download or read book Big Data Analytics for Internet of Things written by Tausifa Jan Saleem and published by John Wiley & Sons. This book was released on 2021-04-20 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: BIG DATA ANALYTICS FOR INTERNET OF THINGS Discover the latest developments in IoT Big Data with a new resource from established and emerging leaders in the field Big Data Analytics for Internet of Things delivers a comprehensive overview of all aspects of big data analytics in Internet of Things (IoT) systems. The book includes discussions of the enabling technologies of IoT data analytics, types of IoT data analytics, challenges in IoT data analytics, demand for IoT data analytics, computing platforms, analytical tools, privacy, and security. The distinguished editors have included resources that address key techniques in the analysis of IoT data. The book demonstrates how to select the appropriate techniques to unearth valuable insights from IoT data and offers novel designs for IoT systems. With an abiding focus on practical strategies with concrete applications for data analysts and IoT professionals, Big Data Analytics for Internet of Things also offers readers: A thorough introduction to the Internet of Things, including IoT architectures, enabling technologies, and applications An exploration of the intersection between the Internet of Things and Big Data, including IoT as a source of Big Data, the unique characteristics of IoT data, etc. A discussion of the IoT data analytics, including the data analytical requirements of IoT data and the types of IoT analytics, including predictive, descriptive, and prescriptive analytics A treatment of machine learning techniques for IoT data analytics Perfect for professionals, industry practitioners, and researchers engaged in big data analytics related to IoT systems, Big Data Analytics for Internet of Things will also earn a place in the libraries of IoT designers and manufacturers interested in facilitating the efficient implementation of data analytics strategies.

Download or read book Big Data Little Data No Data written by Christine L. Borgman and published by MIT Press. This book was released on 2015-01-02 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: An examination of the uses of data within a changing knowledge infrastructure, offering analysis and case studies from the sciences, social sciences, and humanities. “Big Data” is on the covers of Science, Nature, the Economist, and Wired magazines, on the front pages of the Wall Street Journal and the New York Times. But despite the media hyperbole, as Christine Borgman points out in this examination of data and scholarly research, having the right data is usually better than having more data; little data can be just as valuable as big data. In many cases, there are no data—because relevant data don't exist, cannot be found, or are not available. Moreover, data sharing is difficult, incentives to do so are minimal, and data practices vary widely across disciplines. Borgman, an often-cited authority on scholarly communication, argues that data have no value or meaning in isolation; they exist within a knowledge infrastructure—an ecology of people, practices, technologies, institutions, material objects, and relationships. After laying out the premises of her investigation—six “provocations” meant to inspire discussion about the uses of data in scholarship—Borgman offers case studies of data practices in the sciences, the social sciences, and the humanities, and then considers the implications of her findings for scholarly practice and research policy. To manage and exploit data over the long term, Borgman argues, requires massive investment in knowledge infrastructures; at stake is the future of scholarship.

Download or read book Macroanalysis written by Matthew L. Jockers and published by University of Illinois Press. This book was released on 2013-04-15 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this volume, Matthew L. Jockers introduces readers to large-scale literary computing and the revolutionary potential of macroanalysis--a new approach to the study of the literary record designed for probing the digital-textual world as it exists today, in digital form and in large quantities. Using computational analysis to retrieve key words, phrases, and linguistic patterns across thousands of texts in digital libraries, researchers can draw conclusions based on quantifiable evidence regarding how literary trends are employed over time, across periods, within regions, or within demographic groups, as well as how cultural, historical, and societal linkages may bind individual authors, texts, and genres into an aggregate literary culture. Moving beyond the limitations of literary interpretation based on the "close-reading" of individual works, Jockers describes how this new method of studying large collections of digital material can help us to better understand and contextualize the individual works within those collections.

Download or read book Data Science written by Qurban A Memon and published by CRC Press. This book was released on 2019-09-26 with total page 403 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this book is to provide an internationally respected collection of scientific research methods, technologies and applications in the area of data science. This book can prove useful to the researchers, professors, research students and practitioners as it reports novel research work on challenging topics in the area surrounding data science. In this book, some of the chapters are written in tutorial style concerning machine learning algorithms, data analysis, information design, infographics, relevant applications, etc. The book is structured as follows: • Part I: Data Science: Theory, Concepts, and Algorithms This part comprises five chapters on data Science theory, concepts, techniques and algorithms. • Part II: Data Design and Analysis This part comprises five chapters on data design and analysis. • Part III: Applications and New Trends in Data Science This part comprises four chapters on applications and new trends in data science.

Download or read book Big Data Management and Processing written by Kuan-Ching Li and published by CRC Press. This book was released on 2017-05-19 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the Foreword: "Big Data Management and Processing is [a] state-of-the-art book that deals with a wide range of topical themes in the field of Big Data. The book, which probes many issues related to this exciting and rapidly growing field, covers processing, management, analytics, and applications... [It] is a very valuable addition to the literature. It will serve as a source of up-to-date research in this continuously developing area. The book also provides an opportunity for researchers to explore the use of advanced computing technologies and their impact on enhancing our capabilities to conduct more sophisticated studies." ---Sartaj Sahni, University of Florida, USA "Big Data Management and Processing covers the latest Big Data research results in processing, analytics, management and applications. Both fundamental insights and representative applications are provided. This book is a timely and valuable resource for students, researchers and seasoned practitioners in Big Data fields. --Hai Jin, Huazhong University of Science and Technology, China Big Data Management and Processing explores a range of big data related issues and their impact on the design of new computing systems. The twenty-one chapters were carefully selected and feature contributions from several outstanding researchers. The book endeavors to strike a balance between theoretical and practical coverage of innovative problem solving techniques for a range of platforms. It serves as a repository of paradigms, technologies, and applications that target different facets of big data computing systems. The first part of the book explores energy and resource management issues, as well as legal compliance and quality management for Big Data. It covers In-Memory computing and In-Memory data grids, as well as co-scheduling for high performance computing applications. The second part of the book includes comprehensive coverage of Hadoop and Spark, along with security, privacy, and trust challenges and solutions. The latter part of the book covers mining and clustering in Big Data, and includes applications in genomics, hospital big data processing, and vehicular cloud computing. The book also analyzes funding for Big Data projects.

Download or read book Big Data written by Rajkumar Buyya and published by Morgan Kaufmann. This book was released on 2016-06-07 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data: Principles and Paradigms captures the state-of-the-art research on the architectural aspects, technologies, and applications of Big Data. The book identifies potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications. To help realize Big Data's full potential, the book addresses numerous challenges, offering the conceptual and technological solutions for tackling them. These challenges include life-cycle data management, large-scale storage, flexible processing infrastructure, data modeling, scalable machine learning, data analysis algorithms, sampling techniques, and privacy and ethical issues. - Covers computational platforms supporting Big Data applications - Addresses key principles underlying Big Data computing - Examines key developments supporting next generation Big Data platforms - Explores the challenges in Big Data computing and ways to overcome them - Contains expert contributors from both academia and industry

Download or read book The Big Data Agenda written by Annika Richterich and published by University of Westminster Press. This book was released on 2018-04-13 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights that the capacity for gathering, analysing, and utilising vast amounts of digital (user) data raises significant ethical issues. Annika Richterich provides a systematic contemporary overview of the field of critical data studies that reflects on practices of digital data collection and analysis. The book assesses in detail one big data research area: biomedical studies, focused on epidemiological surveillance. Specific case studies explore how big data have been used in academic work. The Big Data Agenda concludes that the use of big data in research urgently needs to be considered from the vantage point of ethics and social justice. Drawing upon discourse ethics and critical data studies, Richterich argues that entanglements between big data research and technology/ internet corporations have emerged. In consequence, more opportunities for discussing and negotiating emerging research practices and their implications for societal values are needed.

Download or read book Data Theory written by Simon Lindgren and published by John Wiley & Sons. This book was released on 2020-09-01 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: The datafication of our world offers huge challenges and opportunities for social science. The ‘data-drivenness’ of computational research can occur at the expense of theoretical reflection and interpretation. Additionally, it can be difficult to reconcile the ‘quantitative’ dimensions of big data with the ‘qualitative’ sensibilities needed for its understanding. At the same time, this opens up possibilities for reimagining key principles of social inquiry. In this experimental and provocative book, Simon Lindgren argues that a hybrid approach to data and theory must be developed in order to make sense of today's ambivalent, turbulent, and media-saturated political landscape. He pushes for the development of a critical science of data, joining the interpretive theoretical and ethical sensibilities of social science with the predictive and prognostic powers of data science and computational methods. In order for theories and research methods to be more useful and relevant, they must be dismantled and put together in new, alternative, and unexpected ways. Data Theory is essential reading for social scientists and data scientists, as well as students taking courses in social theory and data, digital methods, big data, and data and society.

Download or read book Big Data written by Viktor Mayer-Schönberger and published by Houghton Mifflin Harcourt. This book was released on 2013 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: A exploration of the latest trend in technology and the impact it will have on the economy, science, and society at large.

Download or read book Law and Policy for the Quantum Age written by Chris Jay Hoofnagle and published by Cambridge University Press. This book was released on 2022-01-06 with total page 601 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Quantum Age cuts through the hype to demystify quantum technologies, their development paths, and the policy issues they raise.