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• Intro
• Preface
• Acknowledgments
• Contents
• About the Author
• Chapter 1: Introduction
• 1.1 Background
• 1.2 Symbols and Notations (Table 1.1)
• 1.3 Book Organization
• References
• Chapter 2: Basis of Latent Feature Learning
• 2.1 Overview
• 2.2 Preliminaries
• 2.3 Latent Feature Learning
• 2.3.1 A Basic LFL Model
• 2.3.2 A Biased LFL Model
• 2.3.3 Algorithms Design
• 2.4 Performance Analysis
• 2.4.1 Evaluation Protocol
• 2.4.2 Discussion
• 2.5 Summary
• References
• Chapter 3: Robust Latent Feature Learning based on Smooth L1-norm
• 3.1 Overview

• 3.2 Related Work
• 3.3 A Smooth L1-Norm Based Latent Feature Model
• 3.3.1 Objective Formulation
• 3.3.2 Model Optimization
• 3.3.3 Incorporating Linear Biases into SL-LF
• 3.4 Performance Analysis
• 3.4.1 General Settings
• 3.4.2 Performance Comparison
• 3.4.2.1 Comparison of Prediction Accuracy
• 3.4.2.2 Comparison of Computational Efficiency
• 3.4.3 Outlier Data Sensitivity Tests
• 3.4.4 The Impact of Hyper-Parameter
• 3.5 Summary
• References
• Chapter 4: Improving Robustness of Latent Feature Learning Using L1-Norm
• 4.1 Overview
• 4.2 Related Work

• 4.3 An L1-and-L2-Norm-Oriented Latent Feature Model
• 4.3.1 Objective Formulation
• 4.3.2 Model Optimization
• 4.3.3 Self-Adaptive Aggregation
• 4.4 Performance Analysis
• 4.4.1 General Settings
• 4.4.2 L3Fś Aggregation Effects
• 4.4.3 Comparison Between L3F and Baselines
• 4.4.3.1 Comparison of Rating Prediction Accuracy
• 4.4.3.2 Comparison of Computational Efficiency
• 4.4.4 L3Fś Robustness to Outlier Data
• 4.5 Summary
• References
• Chapter 5: Improve Robustness of Latent Feature Learning Using Double-Space
• 5.1 Overview
• 5.2 Related Work

• 5.3 A Double-Space and Double-Norm Ensembled Latent Feature Model
• 5.3.1 Predictor Based on Inner Product Space (D2E-LF-1)
• 5.3.2 Predictor on Euclidean Distance Space (D2E-LF-2)
• 5.3.3 Ensemble of D2E-LF-1 and D2E-LF-2
• 5.3.4 Algorithm Design and Analysis
• 5.4 Performance Analysis
• 5.4.1 General Settings
• 5.4.2 Performance Comparison
• 5.5 Summary
• References
• Chapter 6: Data-characteristic-aware Latent Feature Learning
• 6.1 Overview
• 6.2 Related Work
• 6.2.1 Related LFL-Based Models
• 6.2.2 DPClust Algorithm
• 6.3 A Data-Characteristic-Aware Latent Feature Model

• 6.3.1 Model Structure
• 6.3.2 Step 1: Latent Feature Extraction
• 6.3.3 Step 2: Neighborhood and Outlier Detection
• 6.3.4 Step 3: Prediction
• 6.4 Performance Analysis
• 6.4.1 Prediction Rule Selection
• 6.4.2 Performance Comparison
• 6.5 Summary
• References
• Chapter 7: Posterior-neighborhood-regularized Latent Feature Learning
• 7.1 Overview
• 7.2 Related Work
• 7.3 A Posterior-Neighborhood-Regularized Latent Feature Model
• 7.3.1 Primal Latent Feature Extraction
• 7.3.2 Posterior-Neighborhood Construction
• 7.3.3 Posterior-Neighborhood-Regularized LFL

Incomplete big data are frequently encountered in many industrial applications, such as recommender systems, the Internet of Things, intelligent transportation, cloud computing, and so on. It is of great significance to analyze them for mining rich and valuable knowledge and patterns. Latent feature analysis (LFA) is one of the most popular representation learning methods tailored for incomplete big data due to its high accuracy, computational efficiency, and ease of scalability. The crux of analyzing incomplete big data lies in addressing the uncertainty problem caused by their incomplete characteristics. However, existing LFA methods do not fully consider such uncertainty. In this book, the author introduces several robust latent feature learning methods to address such uncertainty for effectively and efficiently analyzing incomplete big data, including robust latent feature learning based on smooth L1-norm, improving robustness of latent feature learning using L1-norm, improving robustness of latent feature learning using double-space, data-characteristic-aware latent feature learning, posterior-neighborhood-regularized latent feature learning, and generalized deep latent feature learning. Readers can obtain an overview of the challenges of analyzing incomplete big data and how to employ latent feature learning to build a robust model to analyze incomplete big data. In addition, this book provides several algorithms and real application cases, which can help students, researchers, and professionals easily build their models to analyze incomplete big data.
(source: Nielsen Book Data)

• Part I: Ecosystem Elements of Big Data Value.- The European Big Data Value Ecosystem.- Stakeholder Analysis of Data Ecosystems.- A Roadmap to Drive Adoption of Data Ecosystems.- Achievements and Impact of the Big Data Value Public-Private Partnership: The Story so Far.- Part II: Research and Innovation Elements of Big Data Value.- Technical Research Priorities for Big Data.- A Reference Model for Big Data Technologies.- Data Protection in the Era of Artificial Intelligence: Trends, Existing Solutions and Recommendations for Privacy-Preserving Technologies.- A Best Practice Framework for Centres of Excellence in Big Data and Artificial Intelligence.- Data Innovation Spaces.- Part III: Business, Policy, and Societal Elements of Big Data Value.- Big Data Value Creation by Example.- Business Models and Ecosystem for Big Data.- Innovation in Times of Big Data and AI: Introducing the Data-Driven Innovation (DDI) Framework.- The Road to Big Data Standardisation.- The Role of Data Regulation in Shaping AI: An Overview of Challenges and Recommendations for SMEs.- Part IV: Emerging Elements of Big Data Value.- Data Economy 2
• .0: From Big Data Value to AI Value and a European Data Space.
• (source: Nielsen Book Data)

This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: * Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. * Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. * Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. * Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation. .
(source: Nielsen Book Data)

• 1. Big Data: What, Why and How?
• 2. Big Data and AI
• 3. What Big Data is not?
• 4. How Data Analytics works?
• 5. Big Data: The Applications
• 6. Why Big Data Matters?
• 7. The challenges with Big Data
• 8. Big Data: the Key Questions
• 9. Big Data: Is there a question mark on ethics?
• 10. The Future of Big Data.
• (source: Nielsen Book Data)

Big Data is everywhere. It shapes our lives in more ways than we know and understand. This comprehensive introduction unravels the complex terabytes that will continue to shape our lives in ways imagined and unimagined. Drawing on case studies like Amazon, Facebook, the FIFA World Cup and the Aadhaar scheme, this book looks at how Big Data is changing the way we behave, consume and respond to situations in the digital age. It looks at how Big Data has the potential to transform disaster management and healthcare, as well as prove to be authoritarian and exploitative in the wrong hands. The latest offering from the authors of Artificial Intelligence: Evolution, Ethics and Public Policy, this accessibly written volume is essential for the researcher in science and technology studies, media and culture studies, public policy and digital humanities, as well as being a beacon for the general reader to make sense of the digital age.
(source: Nielsen Book Data)

• 1. Big Data: What, Why and How?
• 2. Big Data and AI
• 3. What Big Data is not?
• 4. How Data Analytics works?
• 5. Big Data: The Applications
• 6. Why Big Data Matters?
• 7. The challenges with Big Data
• 8. Big Data: the Key Questions
• 9. Big Data: Is there a question mark on ethics?
• 10. The Future of Big Data.
• (source: Nielsen Book Data)

Big Data is everywhere. It shapes our lives in more ways than we know and understand. This comprehensive introduction unravels the complex terabytes that will continue to shape our lives in ways imagined and unimagined. Drawing on case studies like Amazon, Facebook, the FIFA World Cup and the Aadhaar scheme, this book looks at how Big Data is changing the way we behave, consume and respond to situations in the digital age. It looks at how Big Data has the potential to transform disaster management and healthcare, as well as prove to be authoritarian and exploitative in the wrong hands. The latest offering from the authors of Artificial Intelligence: Evolution, Ethics and Public Policy, this accessibly written volume is essential for the researcher in science and technology studies, media and culture studies, public policy and digital humanities, as well as being a beacon for the general reader to make sense of the digital age.
(source: Nielsen Book Data)

• Cluster Analysis in Big Data Mining.- Deep Neural Networks and Hybrid GMDH
• Neuro-fuzzy Networks in Big Data Analysis.- Pattern Recognition in Big Data Analysis.- Intellectual analysis of systemic world conflicts and global forecast for the 21st century.

The book is devoted to the analysis of big data in order to extract from these data hidden patterns necessary for making decisions about the rational behavior of complex systems with the different nature that generate this data. To solve these problems, a group of new methods and tools is used, based on the self-organization of computational processes, the use of crisp and fuzzy cluster analysis methods, hybrid neural-fuzzy networks, and others. The book solves various practical problems. In particular, for the tasks of 3D image recognition and automatic speech recognition large-scale neural networks with applications for Deep Learning systems were used. Application of hybrid neuro-fuzzy networks for analyzing stock markets was presented. The analysis of big historical, economic and physical data revealed the hidden Fibonacci pattern about the course of systemic world conflicts and their connection with the Kondratieff big economic cycles and the Schwabe-Wolf solar activity cycles. The book is useful for system analysts and practitioners working with complex systems in various spheres of human activity.

• Foreword ix
• Acknowledgements xiii
• Introduction xv
• Chapter 1. From Data to Decision-Making: A Major Pathway 1
• 1.1. Background on economic intelligence 2
• 1.2. Strategic economic intelligence revisited 3
• 1.2.1. The three major steps for decision support 3
• 1.2.2. Modeling the concept of strategic business intelligence 4
• 1.3. Conclusion 9
• Chapter 2. Data: An Indispensable Platform for Companies 11
• 2.1. The key figures of digital technology 14
• 2.1.1. Figures on social networks 20
• 2.1.2. Numbers: Big Data 22
• 2.1.3. Key figures: the Internet of Things 24
• 2.2. The power of data: a major challenge 28
• 2.3. The Big Data revolution, “Mega Data” 30
• 2.3.1. Understanding the world of Big Data 31
• 2.3.2. Open data: a new challenge 41
• 2.4. Developing the culture of data sharing 55
• 2.5. Storage of data in databases 56
• 2.6. The appearance of buzzwords: Big, Open, Viz, etc. 58
• 2.7. Conclusion 59
• Chapter 3. From Data to Information: Essential Transformations 63
• 3.1. Value creation from data processing 63
• 3.2. Value creation and analysis of open databases 69
• 3.3. From data to information: the “DataViz” or data visualization 73
• 3.4. From data to information: statistical processing 74
• 3.4.1. Phases of data processing 75
• 3.4.2. Processing the data 75
• 3.5. Turning mass data into an opportunity for innovation 81
• 3.6. Development of company assets in the web of data 87
• 3.7. Conclusion 91
• Chapter 4. Information: Contextualized and Materialized Data 93
• 4.1. What is information? 94
• 4.1.1. How can we define information? 94
• 4.2. Internal and external information 100
• 4.2.1. Internal information 100
• 4.2.2. External information 100
• 4.3. Formal and informal information 100
• 4.3.1. Formal information 100
• 4.3.2. Informal information 101
• 4.4. Importance of information 101
• 4.4.1. White information 101
• 4.4.2. Gray information 101
• 4.4.3. Black information 101
• 4.5. Décodex set up by Le Monde 102
• 4.6. Conclusion 103
• Chapter 5. From Information to Knowledge: Valuing and Innovating 105
• 5.1. Innovation as a driving force of growth 105
• 5.1.1. Innovation and the intangible economy 106
• 5.2. Knowledge: the key to innovation 108
• 5.3. Building knowledge: economic intelligence 109
• 5.3.1. The EI process and the transition from information to knowledge 110
• 5.3.2. Managing the data warehouse to extract knowledge and insight 111
• 5.4. Data mining, Statistica and Tibco 114
• 5.5. Information an economic good? 115
• 5.5.1. Innovation as a driving force of growth 115
• 5.5.2. Strategic business intelligence 116
• 5.6. What is data science? 118
• 5.7. Conclusion 119
• Chapter 6. From Knowledge to Strategic Business Intelligence: Decision-Making 121
• 6.1. Data valuation mechanisms 121
• 6.2. How do you value data? 122
• 6.3. Data governance: a key factor in valuation 132
• 6.4. EI: protection and enhancement of digital heritage 138
• 6.5. Data analysis techniques: data mining/text mining 143
• 6.6. Conclusion 148
• Conclusion 151
• Glossary 157
• References 159
• Index 173.

Businesses are becoming increasingly aware of the importance of data and information. As such, they are eager to develop ways to Ňmanageň them, to enrich them and take advantage of them. Indeed, the recent explosion of a phenomenal amount of data, and the need to analyze it, brings to the forefront the well-known hierarchical model: 𔟚ta, Information, Knowledgeň. 𔟚taň- this new intangible manna - is produced in real time. It arrives in a continuous stream and comes from a multitude of sources that are generally heterogeneous. This accumulation of data of all kinds is generating new activities designed to analyze these huge amounts of information. It is therefore necessary to adapt and try new approaches, methods, new knowledge and new ways of working. This leads to new properties and new issues as a logical reference must be created and implemented. At the company level, this mass of data is difficult to manage; interpreting it is the predominant challenge.

• 1. Introduction
• 2. Summaries for sets
• 3. Summaries for multisets
• 4. Summaries for ordered data
• 5. Geometric summaries
• 6. Graph summaries
• 7. Vector, matrix and linear algebraic summaries
• 8. Summaries over distributed data
• 9. Other uses of summaries
• 10. Lower bounds for summaries.
• (source: Nielsen Book Data)

The massive volume of data generated in modern applications can overwhelm our ability to conveniently transmit, store, and index it. For many scenarios, building a compact summary of a dataset that is vastly smaller enables flexibility and efficiency in a range of queries over the data, in exchange for some approximation. This comprehensive introduction to data summarization, aimed at practitioners and students, showcases the algorithms, their behavior, and the mathematical underpinnings of their operation. The coverage starts with simple sums and approximate counts, building to more advanced probabilistic structures such as the Bloom Filter, distinct value summaries, sketches, and quantile summaries. Summaries are described for specific types of data, such as geometric data, graphs, and vectors and matrices. The authors offer detailed descriptions of and pseudocode for key algorithms that have been incorporated in systems from companies such as Google, Apple, Microsoft, Netflix and Twitter.
(source: Nielsen Book Data)

This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors - some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors' combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry.
(source: Nielsen Book Data)

• Cover; Half Title Page; Title Page; Copyright Page; Declaration; About the Editor; Table of Contents; List of Contributors; List of Abbreviations; Preface; SECTION I BIG DATA MODELING AND ANALYTICS APPROACHES;
• Chapter 1 Big Data: Survey, Technologies, Opportunities, and Challenges; Abstract; Introduction; Background; Big Data Management; Life Cycle And Management of Data Using Technologies and Terminologies of Big Data; Opportunities, Open Issues, and Challenges; Conclusion; Acknowledgment; References;
• Chapter 2 Data Modeling and Data Analytics: A Survey from a Big Data Perspective; Abstract

• IntroductionData Modeling; Data Analytics; Discussion; Related Work; Conclusions; Acknowledgements; References;
• Chapter 3 Building A Productive Domain-Specific Cloud For Big Data Processing And Analytics Service; Abstract; Introduction; Related Work; Seismic Analytics Cloud Implementation; Experiment And Results; Performance Analysis; Future Work And Conclusion; Acknowledgements; References;
• Chapter 4 Unified Platform For AI and Big Data Analytics; Abstract; Introduction; Idea Extraction Process; Nvidia Artificial Intelligence (AI) Platform

• Configuration of Network Between Host and Slave ServersCreation of Hadoop Cluster on Nvidia AI Platform; Conclusions; Acknowledgements; References;
• Chapter 5 Semantic Recognition of a Data Structure in Big-Data; Abstract; Introduction; Meta-Information; Semantic Data Profiling Process; Conclusions And Contribution; References; SECTION II INFRASTRUCTURE AND SECURITY ISSUES IN BIG DATA ANALYTICS;
• Chapter 6 Cloud Computing And Big Data: A Review Of Current Service Models And Hardware Perspectives; Abstract; Introduction; The User Perspective; The Data Perspective; The Hardware Perspective

• Chapter 9 Development of Multiple Big Data Analytics Platforms With Rapid ResponseAbstract; Introduction; Related Work In Big Data Processing; System Implementation Method; Experimental Results And Discussion; Conclusion; Acknowledgments; References; SECTION III BIG DATA APPLICATIONS IN BUSINESS, FINANCE AND MANAGEMENT;
• Chapter 10 Big Data, Big Change: In The Financial Management; Abstract; Introduction To Big Data; Big Data, Big Change: Accounting Data Processing; Big Data, Big Change: Comprehensive Budget Management; Big Data, Big Change: Management Accounting; Big Data, Big Challenge

References; Chapter 7 Towards The Development Of Best Data Security For Big Data; Abstract; Introduction; Current Data Security For Big Data; Feasibility And Obstacles Of Big Data; The Proposed Security Intelligence Model For Big Data; Review Methodology; Conclusions And Research Indications; References; Chapter 8 Risk Analysis Technique On Inconsistent Interview Big Data Based On Rough Set Approach; Abstract; Introduction; Data Preprocessing; Usace & Hierarchical Holographic Model Based Investment Risk Analysis; Result Analysis; Conclusions; References

• Contents:
• 1 The promise, application and pitfalls of big data 1 John Storm Pedersen and Adrian Wilkinson
• 2 Man versus cyborg 22 Vladimir Estivill-Castro
• 3 Big data and application 49 Patrick Mikalef
• 4 Big data and human resource management 69 Tobias M. Scholz
• 5 Big data in the energy industry 90 Petyo Bonev and Magnus Soederberg
• 6 A brief introduction to 'big data' and its application in tourism 107 Ali Reza Alaei and Susanne Becken
• 7 Big data in government: The case of 'smart cities' 133 Karl Loefgren and William Webster
• 8 Cyborg bureaucracy: Frontline work in digitalized labor and welfare services 149 Eric Breit, Cathrine Egeland and Ida Bring L.berg
• 9 Data analytics and health services quality: Implementing eHealth initiatives wisely 170 Peter Ross, Therese Kelly, Sanjeev Hiremath and Adrian Wilkinson
• 10 Data-driven management in practice in the digital welfare state 200 John Storm Pedersen
• 11 Social work in the Danish digitalized welfare state - and the use of digital technologies for professional knowledge in child services 224 Anna Olejasz Lyneborg
• 12 Big data in social welfare 245 Philip Gillingham
• 13 Big data and data governance: From the 'world of ideas' to the 'world of practice' 264 Anders Sandgaard
• 14 Artificial reality: The practice of analytics and big data in educational research 287 Ben Kei Daniel
• 15 The digital welfare state: Dataism versus relationshipism 301 John Storm Pedersen
• 16 Big data in political communication 325 Peter Aagaard
• 17 Rumour detection in social media 348 Henry Nguyen and Bela Stantic
• 18 Big data and professionals: What we can learn from Michael Polanyi 366 Giorgio Baruchello Index 391.
• (source: Nielsen Book Data)

Since the early 2000s, digital data has transformed the way we live and work. This timely book looks to big data analytics to understand this revolutionary change, unpacking the impact of big data analytics on the mobilization and allocation of individuals, organizations and societies' resources. Contributions from leading experts on modern technological trends examine the promises, applications and pitfalls of big data. The contributors assess the ways in which contemporary trajectories of data processing have increased efficiency and had a transformative effect on all avenues of life, from energy, tourism and social media, to human resources, welfare systems and urban citizenship. At a time when our personal data is more valuable than ever, this book seeks to make sense of how big data analytics has transformed our lives and how it will continue to shape society in the future. Astute and comprehensive, this book is critical reading for business and management scholars with a focus on information systems and communications technologies. It will also prove to be vital information for students and researchers of big data and digital society, as well as politics and administration more widely.
(source: Nielsen Book Data)

• Introduction.- Background.- Large Scale Data Analytics.- Query Framework.- Results and Discussion.- Conclusion and Future Works.
• (source: Nielsen Book Data)

This book presents a language integrated query framework for big data. The continuous, rapid growth of data information to volumes of up to terabytes (1,024 gigabytes) or petabytes (1,048,576 gigabytes) means that the need for a system to manage and query information from large scale data sources is becoming more urgent. Currently available frameworks and methodologies are limited in terms of efficiency and querying compatibility between data sources due to the differences in information storage structures. For this research, the authors designed and programmed a framework based on the fundamentals of language integrated query to query existing data sources without the process of data restructuring. A web portal for the framework was also built to enable users to query protein data from the Protein Data Bank (PDB) and implement it on Microsoft Azure, a cloud computing environment known for its reliability, vast computing resources and cost-effectiveness.
(source: Nielsen Book Data)

• Introduction.- Background.- Large Scale Data Analytics.- Query Framework.- Results and Discussion.- Conclusion and Future Works.
• (source: Nielsen Book Data)

This book presents a language integrated query framework for big data. The continuous, rapid growth of data information to volumes of up to terabytes (1,024 gigabytes) or petabytes (1,048,576 gigabytes) means that the need for a system to manage and query information from large scale data sources is becoming more urgent. Currently available frameworks and methodologies are limited in terms of efficiency and querying compatibility between data sources due to the differences in information storage structures. For this research, the authors designed and programmed a framework based on the fundamentals of language integrated query to query existing data sources without the process of data restructuring. A web portal for the framework was also built to enable users to query protein data from the Protein Data Bank (PDB) and implement it on Microsoft Azure, a cloud computing environment known for its reliability, vast computing resources and cost-effectiveness.
(source: Nielsen Book Data)

• BYTE SIZE CHART
• REFERENCES
• FURTHER READING
• INDEX.
• (source: Nielsen Book Data)

Since long before computers were even thought of, data has been collected and organized by diverse cultures across the world. Once access to the Internet became a reality for large swathes of the world's population, the amount of data generated each day became huge, and continues to grow exponentially. It includes all our uploaded documents, video, and photos, all our social media traffic, our online shopping, even the GPS data from our cars. 'Big Data' represents a qualitative change, not simply a quantitative one. The term refers both to the new technologies involved, and to the way it can be used by business and government. Dawn E. Holmes uses a variety of case studies to explain how data is stored, analysed, and exploited by a variety of bodies from big companies to organizations concerned with disease control. Big data is transforming the way businesses operate, and the way medical research can be carried out. At the same time, it raises important ethical issues; Holmes discusses cases such as the Snowden affair, data security, and domestic smart devices which can be hijacked by hackers. ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable.
(source: Nielsen Book Data)

• section I. Genesis of big data computing
• section II. Road to big data computing
• section III. Big data computing
• section IV. Big data computing applications

Is the Brexit vote successful big data politics or the end of democracy? Why do airlines overbook, and why do banks get it wrong so often? How does big data enable Netflix to forecast a hit, CERN to find the Higgs boson and medics to discover if red wine really is good for you? And how are companies using big data to benefit from smart meters, use advertising that spies on you and develop the gig economy, where workers are managed by the whim of an algorithm? The volumes of data we now access can give unparalleled abilities to make predictions, respond to customer demand and solve problems. But Big Brother's shadow hovers over it. Though big data can set us free and enhance our lives, it has the potential to create an underclass and a totalitarian state. With big data ever-present, you can't afford to ignore it. Acclaimed science writer Brian Clegg - a habitual early adopter of new technology (and the owner of the second-ever copy of Windows in the UK) - brings big data to life.
(source: Nielsen Book Data)

• section 1. Introduction of big data
• section 2. Networking theory and design for big data
• section 3. Networking security for big data
• section 4. Platforms and systems for big data applications

• INTRODUCTION OF BIG DATA Orchestrating Science DMZs for Big Data Acceleration: Challenges and Approaches
• Saptarshi Debroy and Prasad Calyam A Survey of Virtual Machine Placement in Cloud Computing for Big Data
• Yang Wang, Jie Wu, Shaojie Tang, and Wu Zhang Big Data Management Challenges, Approaches, Tools, and Their Limitations
• Michel Adiba, Juan Carlos Castrejon, Javier A. Espinosa-Oviedo, Genoveva Vargas-Solar, and Jose-Luis Zechinelli-Martini Big Data Distributed Systems Management
• Rashid Saeed NETWORKING THEORY AND DESIGN FOR BIG DATA Moving Big Data to the Cloud: Online Cost-Minimizing Algorithms
• Linquan Zhang, Chuan Wu, Zongpeng Li, Chuanxiong Guo, Minghua Chen, and Francis C. M. Lau Data Process and Analysis Technologies of Big Data
• Peter Wlodarczak, Mustafa Ally, and Jeffrey Soar Network Configuration and Flow Scheduling for Big Data Applications
• Jerome Francois and Raouf Speedup of Big Data Transfer on the Internet
• Guangyan Huang, Wanlei Zhou, and Jing He Energy Aware Survivable Routing in Ever-Escalating Data Environments
• Bing Luo, William Liu, and Adnan Al-Anbuky NETWORKING SECURITY FOR BIG DATA A Review of Network Intrusion Detection in the Big Data Era: Challenges and Future Trends
• Weizhi Meng and Wenjuan Li Toward MapReduce-Based Machine Learning Techniques for Processing Massive Network Threat Monitoring
• Linqiang Ge, Hanling Zhang, Guobin Xu, Wei Yu, Chen Chen, and Erik Blasch Anonymous Communication for Big Data
• Rongxin Lu Flow-Based Anomaly Detection in Big Data
• Zahra Jadidi, Vallipuram Muthukkumarasamy, Elankayer Sithirasenan, and Kalvinder Singh PLATFORMS AND SYSTEM FOR BIG DATA APPLICATIONS Mining Social Media with SDN-Enabled Big Data Platform to Transform TV Watching Experience
• Yonggang Wen Trends in Cloud Infrastructures for Big Data
• Yacine Djemaiel A User Data Profile-Aware Policy-Based Network Management Framework in the Era of Big Data
• Fadi Alhaddadin, William Liu, and Jairo A. Gutierrez Circuit Emulation for Big Data Transfers in Clouds
• Marat Zhanikeev Index.
• (source: Nielsen Book Data)

Networking for Big Data supplies an unprecedented look at cutting-edge research on the networking and communication aspects of Big Data. Starting with a comprehensive introduction to Big Data and its networking issues, it offers deep technical coverage of both theory and applications. The book is divided into four sections: introduction to Big Data, networking theory and design for Big Data, networking security for Big Data, and platforms and systems for Big Data applications. Focusing on key networking issues in Big Data, the book explains network design and implementation for Big Data. It examines how network topology impacts data collection and explores Big Data storage and resource management. * Addresses the virtual machine placement problem * Describes widespread network and information security technologies for Big Data * Explores network configuration and flow scheduling for Big Data applications * Presents a systematic set of techniques that optimize throughput and improve bandwidth for efficient Big Data transfer on the Internet * Tackles the trade-off problem between energy efficiency and service resiliency The book covers distributed Big Data storage and retrieval as well as security, trust, and privacy protection for Big Data collection, storage, and search. It discusses the use of cloud infrastructures and highlights its benefits to overcome the identified issues and to provide new approaches for managing huge volumes of heterogeneous data. The text concludes by proposing an innovative user data profile-aware policy-based network management framework that can help you exploit and differentiate user data profiles to achieve better power efficiency and optimized resource management.
(source: Nielsen Book Data)

Big data is ubiquitous but heterogeneous. Big data can be used to tally clicks and traffic on web pages, find patterns in stock trades, track consumer preferences, identify linguistic correlations in large corpuses of texts. This book examines big data not as an undifferentiated whole but contextually, investigating the varied challenges posed by big data for health, science, law, commerce, and politics. Taken together, the chapters reveal a complex set of problems, practices, and policies.The advent of big data methodologies has challenged the theory-driven approach to scientific knowledge in favor of a data-driven one. Social media platforms and self-tracking tools change the way we see ourselves and others. The collection of data by corporations and government threatens privacy while promoting transparency. Meanwhile, politicians, policy makers, and ethicists are ill-prepared to deal with big data's ramifications. The contributors look at big data's effect on individuals as it exerts social control through monitoring, mining, and manipulation; big data and society, examining both its empowering and its constraining effects; big data and science, considering issues of data governance, provenance, reuse, and trust; and big data and organizations, discussing data responsibility, "data harm, " and decision making.ContributorsRyan Abbott, Cristina Alaimo, Kent R. Anderson, Mark Andrejevic, Diane E. Bailey, Mike Bailey, Mark Burdon, Fred H. Cate, Jorge L. Contreras, Simon DeDeo, Hamid R. Ekbia, Allison Goodwell, Jannis Kallinikos, Inna Kouper, M. Lynne Markus, Michael Mattioli, Paul Ohm, Scott Peppet, Beth Plale, Jason Portenoy, Julie Rennecker, Katie Shilton, Dan Sholler, Cassidy R. Sugimoto, Isuru Suriarachchi, Jevin D. West.