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Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World
by Bruce SchneierYou are under surveillance right now. Your cell phone provider tracks your location and knows who's with you. Your online and in-store purchasing patterns are recorded, and reveal if you're unemployed, sick, or pregnant. Your e-mails and texts expose your intimate and casual friends. Google knows what you're thinking because it saves your private searches. Facebook can determine your sexual orientation without you ever mentioning it. The powers that surveil us do more than simply store this information. Corporations use surveillance to manipulate not only the news articles and advertisements we each see, but also the prices we're offered. Governments use surveillance to discriminate, censor, chill free speech, and put people in danger worldwide. And both sides share this information with each other or, even worse, lose it to cybercriminals in huge data breaches. Much of this is voluntary: we cooperate with corporate surveillance because it promises us convenience, and we submit to government surveillance because it promises us protection. The result is a mass surveillance society of our own making. But have we given up more than we've gained? In Data and Goliath, security expert Bruce Schneier offers another path, one that values both security and privacy. He shows us exactly what we can do to reform our government surveillance programs and shake up surveillance-based business models, while also providing tips for you to protect your privacy every day. You'll never look at your phone, your computer, your credit cards, or even your car in the same way again.
Data Classification and Incremental Clustering in Data Mining and Machine Learning (EAI/Springer Innovations in Communication and Computing)
by Sanjay Chakraborty Sk Hafizul Islam Debabrata SamantaThis book is a comprehensive, hands-on guide to the basics of data mining and machine learning with a special emphasis on supervised and unsupervised learning methods. The book lays stress on the new ways of thinking needed to master in machine learning based on the Python, R, and Java programming platforms. This book first provides an understanding of data mining, machine learning and their applications, giving special attention to classification and clustering techniques. The authors offer a discussion on data mining and machine learning techniques with case studies and examples. The book also describes the hands-on coding examples of some well-known supervised and unsupervised learning techniques using three different and popular coding platforms: R, Python, and Java. This book explains some of the most popular classification techniques (K-NN, Naïve Bayes, Decision tree, Random forest, Support vector machine etc,) along with the basic description of artificial neural network and deep neural network. The book is useful for professionals, students studying data mining and machine learning, and researchers in supervised and unsupervised learning techniques.
Data Communication and Networks: Proceedings of GUCON 2019 (Advances in Intelligent Systems and Computing #1049)
by Lakhmi C. Jain George A. Tsihrintzis Valentina E. Balas Dilip Kumar SharmaThis book gathers selected high-quality papers presented at the International Conference on Computing, Power and Communication Technologies 2019 (GUCON 2019), organized by Galgotias University, India, in September 2019. The content is divided into three sections – data mining and big data analysis, communication technologies, and cloud computing and computer networks. In-depth discussions of various issues within these broad areas provide an intriguing and insightful reference guide for researchers, engineers and students alike.
Data-Driven Public Relations Research: 21st Century Practices and Applications
by Jim Eggensperger Natalie RedcrossThe public relations industry is undergoing a revolution in using data to define promotional programs, to measure influence and to address the needs of clients with more precision than ever. Applying tools that range from online surveys to social-media listening to applying big data with sophisticated algorithms, today’s PR professionals are data-driven in virtually everything they do. Data-Driven Public Relations Research is the first book for PR students and practitioners to offer an overview of these new practices as well as a glimpse into the future of these new applications, including "big data" and some of the applications from real-world PR campaigns and strategic planning. It includes contemporary cases involving brand name companies who are blazing new trails in the use of metrics in public relations. This book presents a practical, accessible approach that requires no prior training or experience, with easy to follow, step-by-step measurement examples from existing campaigns. Using Excel, the book enables readers to export lessons from the classroom to the office, where use of statistical packages is rare and can give PR practitioners the advantage over competitors. This pragmatic approach helps readers apply metrics to PR problems such as: Finding the best target audiences Understanding audience communication needs and preferences How best to present research outcomes How to manage major projects with specialized research firms. Accompanying electronic resources for the book include sample answers to the book’s discussion questions, PowerPoint lecture slides for instructors and sample research exercises using Excel.
Data-Driven Wireless Networks: A Compressive Spectrum Approach (SpringerBriefs in Electrical and Computer Engineering)
by Yue Gao Zhijin QinThis SpringerBrief discusses the applications of spare representation in wireless communications, with a particular focus on the most recent developed compressive sensing (CS) enabled approaches. With the help of sparsity property, sub-Nyquist sampling can be achieved in wideband cognitive radio networks by adopting compressive sensing, which is illustrated in this brief, and it starts with a comprehensive overview of compressive sensing principles. Subsequently, the authors present a complete framework for data-driven compressive spectrum sensing in cognitive radio networks, which guarantees robustness, low-complexity, and security. Particularly, robust compressive spectrum sensing, low-complexity compressive spectrum sensing, and secure compressive sensing based malicious user detection are proposed to address the various issues in wideband cognitive radio networks. Correspondingly, the real-world signals and data collected by experiments carried out during TV white space pilot trial enables data-driven compressive spectrum sensing. The collected data are analysed and used to verify our designs and provide significant insights on the potential of applying compressive sensing to wideband spectrum sensing. This SpringerBrief provides readers a clear picture on how to exploit the compressive sensing to process wireless signals in wideband cognitive radio networks. Students, professors, researchers, scientists, practitioners, and engineers working in the fields of compressive sensing in wireless communications will find this SpringerBrief very useful as a short reference or study guide book. Industry managers, and government research agency employees also working in the fields of compressive sensing in wireless communications will find this SpringerBrief useful as well.
Data Engineering and Communication Technology: Proceedings of 3rd ICDECT-2K19 (Advances in Intelligent Systems and Computing #1079)
by K. Srujan Raju Roman Senkerik Satya Prasad Lanka V. RajagopalThis book includes selected papers presented at the 3rd International Conference on Data Engineering and Communication Technology (ICDECT-2K19), held at Stanley College of Engineering and Technology for Women, Hyderabad, from 15 to 16 March 2019. It features advanced, multidisciplinary research towards the design of smart computing, information systems, and electronic systems. It also focuses on various innovation paradigms in system knowledge, intelligence, and sustainability which can be applied to provide viable solutions to diverse problems related to society, the environment, and industry.
Data Engineering and Communication Technology: Proceedings of ICDECT 2020 (Lecture Notes on Data Engineering and Communications Technologies #63)
by K. Ashoka Reddy B. Rama Devi Boby George K. Srujan RajuThis book includes selected papers presented at the 4th International Conference on Data Engineering and Communication Technology (ICDECT 2020), held at Kakatiya Institute of Technology & Science, Warangal, India, during 25–26 September 2020. It features advanced, multidisciplinary research towards the design of smart computing, information systems and electronic systems. It also focuses on various innovation paradigms in system knowledge, intelligence and sustainability which can be applied to provide viable solutions to diverse problems related to society, the environment and industry.
Data for Journalism: Between Transparency and Accountability (Disruptions)
by Jingrong TongConsidering the interactions between developments in open data and data journalism, Data for Journalism: Between Transparency and Accountability offers an interdisciplinary account of this complex and uncertain relationship in a context of tightening the control over data and weighing transparency against privacy. As data has brought both promise and disruptive changes to societies, the relationship between transparency and accountability has become complicated, and data journalism is practised alongside the contradictory needs of opening up and protecting data. In addition to exploring the benefits of data for journalism, this book addresses the uncertain nature of data and the obstacles preventing data from being fluently accessed and properly used for data reporting. Because of these obstacles, it argues individual data journalists play a decisive role in using data for journalism and facilitating the circulation of data. Frictions in data access, newsrooms’ resources and cultures and data journalists’ skill and data literacy levels determine the degree to which journalism can benefit from data, and these factors potentially exacerbate digital inequalities between newsrooms in different countries and with different resources. As such, the author takes an international perspective, drawing on empirical research and cases from around the world, including countries such as the UK, the US, Germany, Sweden, Australia, India, China and Japan. Introducing a new dimension to the study of developments in journalism and the role of journalism in society, Data for Journalism will be of interest to academics and researchers in the fields of journalism and the sociology of (big and open) data.
Data for Journalists: A Practical Guide for Computer-Assisted Reporting
by Brant HoustonThis straightforward and effective how-to guide provides the basics for any reporter or journalism student beginning to use data for news stories. It has step-by-step instructions on how to do basic data analysis in journalism while addressing why these digital tools should be an integral part of reporting in the 21st century. In an ideal core text for courses on data-driven journalism or computer-assisted reporting, Houston emphasizes that journalists are accountable for the accuracy and relevance of the data they acquire and share. With a refreshed design, this updated new edition includes expanded coverage on social media, scraping data from the web, and text-mining, and provides journalists with the tips and tools they need for working with data.
Data-intensive Systems: Principles And Fundamentals Using Hadoop And Spark (Advanced Information and Knowledge Processing)
by Tomasz WiktorskiData-intensive systems are a technological building block supporting Big Data and Data Science applications.This book familiarizes readers with core concepts that they should be aware of before continuing with independent work and the more advanced technical reference literature that dominates the current landscape. The material in the book is structured following a problem-based approach. This means that the content in the chapters is focused on developing solutions to simplified, but still realistic problems using data-intensive technologies and approaches. The reader follows one reference scenario through the whole book, that uses an open Apache dataset. The origins of this volume are in lectures from a master’s course in Data-intensive Systems, given at the University of Stavanger. Some chapters were also a base for guest lectures at Purdue University and Lodz University of Technology.
Data + Journalism: A Story-Driven Approach to Learning Data Reporting
by Mike Reilley Samantha SunneTaking a hands-on and holistic approach to data, Data + Journalism provides a complete guide to reporting data-driven stories. This book offers insights into data journalism from a global perspective, including datasets and interviews with data journalists from countries around the world. Emphasized by examples drawn from frequently updated sets of open data posted by authoritative sources like the FBI, Eurostat and the US Census Bureau, the authors take a deep dive into data journalism’s "heavy lifting" – searching for, scraping and cleaning data. Combined with exercises, video training supplements and lists of tools and resources at the end of each chapter, readers will learn not just how to crunch numbers but also how to put a human face to data, resulting in compelling, story-driven news stories based on solid analysis. Written by two experienced journalists and data journalism teachers, Data + Journalism is essential reading for students, instructors and early career professionals seeking a comprehensive introduction to data journalism skills.
Data Journalism and the COVID-19 Disruption (Routledge Research in Journalism)
by Jingrong TongData Journalism and the COVID‑19 Disruption offers an international, multidisciplinary account of how and to what extent the COVID‑19 pandemic has been a blessing for data journalism.Bringing together insights into current developments in data journalism during (and since the onset of) the COVID‑19 pandemic from world‑leading data journalism practitioners and academics, this book draws on case studies and examples from different countries to critically reflect on emerging data journalism practices during the pandemic and their sustainability and implications for journalism and newsroom work in the post‑pandemic era. The chapters document changes in the practice and integration of data journalism into newsrooms and the 24/7 news cycle after the unexpected onset of the pandemic and explore how newsrooms and journalists are coping with the sudden and immense demand for data journalism and related challenges. This book also scrutinises the implications for understanding the roles played by newsroom structure and operation, the uncertain nature of data, and the relationship between journalism and other social entities such as audiences and the state in journalism’s development through times of crisis.Offering a timely contribution to the discussions on how data journalism evolved during a time of crisis, this volume will appeal to scholars and students of data journalism, journalism practice, media and communication studies, and media industry studies.
The Data Journalism Handbook
by Jonathan Gray Lucy Chambers Liliana Bounegru<p>When you combine the sheer scale and range of digital information now available with a journalist’s "nose for news" and her ability to tell a compelling story, a new world of possibility opens up. With <i>The Data Journalism Handbook</i>, you’ll explore the potential, limits, and applied uses of this new and fascinating field.</p>
Data Journalism in the Global South (Palgrave Studies in Journalism and the Global South)
by Saba Bebawi Bruce Mutsvairo Eddy Borges-ReyThis volume seeks to analyse the emerging wave of data journalism in the Global South. It does so by examining trends, developments and opportunities for data journalism in the aforementioned contexts. Whilst studies in this specific form of journalism are increasing in numbers and significance, there remains a dearth of literature on data journalism in less developed regions of the world. By demonstrating an interest in data journalism across countries including Chile, Argentina, the Philippines, South Africa and Iran, among others, this volume contributes to multifaceted transnational debates on journalism, and is a crucial reference text for anyone interested in data journalism in the ‘developing’ world. Drawing on a range of voices from different fields and nations, sharing empirical and theoretical experiences, the volume aims to initiate a global dialogue among journalism practitioners, researchers and students.
Data Literacy: A User′s Guide
by David L. HerzogA practical, skill-based introduction to data analysis and literacy We are swimming in a world of data, and this handy guide will keep you afloat while you learn to make sense of it all. In Data Literacy: A User′s Guide, David Herzog, a journalist with a decade of experience using data analysis to transform information into captivating storytelling, introduces students and professionals to the fundamentals of data literacy, a key skill in today’s world. Assuming the reader has no advanced knowledge of data analysis or statistics, this book shows how to create insight from publicly-available data through exercises using simple Excel functions. Extensively illustrated, step-by-step instructions within a concise, yet comprehensive, reference will help readers identify, obtain, evaluate, clean, analyze and visualize data. A concluding chapter introduces more sophisticated data analysis methods and tools including database managers such as Microsoft Access and MySQL and standalone statistical programs such as SPSS, SAS and R.
Data Literacy: A User′s Guide
by David L. HerzogA practical, skill-based introduction to data analysis and literacy We are swimming in a world of data, and this handy guide will keep you afloat while you learn to make sense of it all. In Data Literacy: A User′s Guide, David Herzog, a journalist with a decade of experience using data analysis to transform information into captivating storytelling, introduces students and professionals to the fundamentals of data literacy, a key skill in today’s world. Assuming the reader has no advanced knowledge of data analysis or statistics, this book shows how to create insight from publicly-available data through exercises using simple Excel functions. Extensively illustrated, step-by-step instructions within a concise, yet comprehensive, reference will help readers identify, obtain, evaluate, clean, analyze and visualize data. A concluding chapter introduces more sophisticated data analysis methods and tools including database managers such as Microsoft Access and MySQL and standalone statistical programs such as SPSS, SAS and R.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2019, Volume 2 (Advances in Intelligent Systems and Computing #1016)
by Valentina Emilia Balas Amlan Chakrabarti Neha SharmaThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2018, Volume 1 (Advances in Intelligent Systems and Computing #808)
by Valentina Emilia Balas Neha Sharma Amlan ChakrabartiThe book presents the latest, high-quality, technical contributions and research findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. It discusses state-of-the-art topics as well as the challenges and solutions for future development. It includes original and previously unpublished international research work highlighting research domains from different perspectives. This book is mainly intended for researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings Of Icdmai 2018, Volume 2 (Advances In Intelligent Systems and Computing #839)
by Amlan Chakrabarti Neha Sharma Valentina Emilia BalasThe volume on Data Management, Analytics and Innovations presents the latest high-quality technical contributions and research results in the areas of data management and smart computing, big data management, artificial intelligence and data analytics along with advances in network technologies. It deals with the state-of-the-art topics and provides challenges and solutions for future development. Original, unpublished research work highlighting specific research domains from all viewpoints are contributed from scientists throughout the globe. This volume is mainly designed for professional audience, composed of researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2019, Volume 1 (Advances in Intelligent Systems and Computing #1042)
by Neha Sharma Amlan Chakrabarti Valentina Emilia BalasThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2020, Volume 1 (Advances in Intelligent Systems and Computing #1174)
by Neha Sharma Amlan Chakrabarti Valentina Emilia Balas Jan MartinovicThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. Gathering peer-reviewed research papers presented at the Fourth International Conference on Data Management, Analytics and Innovation (ICDMAI 2020), held on 17–19 January 2020 at the United Services Institute (USI), New Delhi, India, it addresses cutting-edge topics and discusses challenges and solutions for future development. Featuring original, unpublished contributions by respected experts from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2020, Volume 2 (Advances in Intelligent Systems and Computing #1175)
by Neha Sharma Amlan Chakrabarti Valentina Emilia Balas Jan MartinovicThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. Gathering peer-reviewed research papers presented at the Fourth International Conference on Data Management, Analytics and Innovation (ICDMAI 2020), held on 17–19 January 2020 at the United Services Institute (USI), New Delhi, India, it addresses cutting-edge topics and discusses challenges and solutions for future development. Featuring original, unpublished contributions by respected experts from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.
Data Mining Methods for the Content Analyst: An Introduction to the Computational Analysis of Content (Routledge Communication Series)
by Kalev LeetaruWith continuous advancements and an increase in user popularity, data mining technologies serve as an invaluable resource for researchers across a wide range of disciplines in the humanities and social sciences. In this comprehensive guide, author and research scientist Kalev Leetaru introduces the approaches, strategies, and methodologies of current data mining techniques, offering insights for new and experienced users alike. Designed as an instructive reference to computer-based analysis approaches, each chapter of this resource explains a set of core concepts and analytical data mining strategies, along with detailed examples and steps relating to current data mining practices. Every technique is considered with regard to context, theory of operation and methodological concerns, and focuses on the capabilities and strengths relating to these technologies. In addressing critical methodologies and approaches to automated analytical techniques, this work provides an essential overview to a broad innovative field.
Data Science and Artificial Intelligence for Digital Healthcare: Communications Technologies for Epidemic Models (Signals and Communication Technology)
by Fionn Murtagh Marcello Trovati Mohammed Atiquzzaman Pradeep Kumar Singh Mohsen FaridThis book explores current research and development in the area of digital healthcare using recent technologies such as data science and artificial intelligence. The authors discuss how data science, AI, and mobile technologies provide the fundamental backbone to digital healthcare, presenting each technology separately as well covering integrated solutions. The book also focuses on the integration of different multi-disciplinary approaches along with examples and case studies. In order to identify the challenges with security and privacy issues, relevant block chain technologies are identified and discussed. Social aspects related to digital solutions and platforms for healthcare are also discussed and analyzed. The book aims to present high quality, technical contributions in the field of mobile digital healthcare using technologies such as AI, deep learning, IoT and distributed cloud computing.
Data Science and Communication: Proceedings of ICTDsC 2023 (Studies in Autonomic, Data-driven and Industrial Computing)
by João Manuel R. S. Tavares Joel J. P. C. Rodrigues Debajyoti Misra Debasmriti BhattacherjeeThe book presents selected papers from the International Conference on Data Science and Communication (ICTDsC 2023) organized by the Department of Electronics and Communication Engineering and Department of Engineering Science and Humanities (DESH) Siliguri Institute of Technology, India during 23 – 24 March 2023 in Siliguri, India. The book covers state-of-the-art research insights on artificial intelligence, machine learning, big data, data analytics, cyber security and forensic, network and mobile security, advanced computing, cloud computing, quantum computing, electronics system, Internet of Things, robotics and automations, blockchain and software technology, and digital technologies for future.