Big Data Technologies and Analytics: AI, IoT and B lockchain Technology
Autor Aparna Kumarien Limba Engleză Hardback – 9 noi 2026
In recent decades, big data analytics has undergone rapid growth to become an essential tool in a variety of research and technological fields. Advances in technology and the rise of the internet led to an explosion in the volume of data available, and as data processing capabilities continue to improve, further expansion in this field is certain. It has never been more important to understand the state of this vital field and its likely applications in new areas of research.
Big Data Technologies and Analytics offers a thought-provoking investigation of this thriving field and its major touchstones, including artificial intelligence, the internet of things, blockchain, and more. It explores how these technologies are transforming industries and enabling a huge range of data-driven strategies. Addressing the topic of quantum computing and its potential to handle even larger datasets and computational issues, the book promises to point towards revolutionary developments in data analysis.
Big Data Technologies and Analytics readers will also find:
- Findings connected to United Nations Sustainable Development Goals (SDGs)
- Detailed discussion of big data topics including historical development, present-day innovations, and potential future directions
- Treatment of application areas from predictive maintenance to smart cities to safe transactions and more
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Specificații
ISBN-13: 9781394280278
ISBN-10: 1394280270
Pagini: 240
Greutate: 0.67 kg
Ediția:1. Auflage
Editura: John Wiley & Sons, Inc.
Locul publicării:Hoboken, United States
ISBN-10: 1394280270
Pagini: 240
Greutate: 0.67 kg
Ediția:1. Auflage
Editura: John Wiley & Sons, Inc.
Locul publicării:Hoboken, United States
Notă biografică
Aparna Kumari, PhD, a distinguished researcher and educator recognized globally for her impactful contributions to technology and academia. A recipient of the Shri Pralhad P Chhabria Award 2023 for Best Woman Professional (Early Career), she has been listed among the Top 2% of researchers worldwide by Stanford University across multiple years since 2021. Her research, including high-impact works on blockchain, AI, and fog computing in smart grids and healthcare, has earned a place in WHO's COVID-19 Global Literature database. With over 14 years of experience, Dr. Kumari has authored or co-authored 55 publications, including 31 papers in SCI-indexed journals and 18 IEEE ComSoc-sponsored international conference papers, with several highly downloaded publications on Elsevier and Wiley platforms. Some of her research works are published in top-cited journals, for instance, the Computer and Electrical Engineering, the IEEE NETWORKS, Computer Communications (Elsevier), and the International Journal of Communication Systems (Wiley). Several of her papers have earned Best Paper Awards, reflecting the outstanding quality and impact of her research contributions in various domains. She has edited multiple books with international publishers like Elsevier, Springer, etc. Her research interests include big data analytics, smart grid systems, blockchain technology, and sustainable smart city. She also holds a UK-design patent for a wearable health-monitoring device for mothers and fetuses and serves as an external reviewer for interdisciplinary research at Qatar University. Recently, Dr. Kumari has contributed as a judge at Accathon'24 and participated in India's Electric Vehicle Mission organized by ANRF for India's Electric Vehicle Mission, underscoring her dedication to technological advancement and knowledge sharing.
Cuprins
Acknowledgments xiii
About the Companion Website xv
1 Introduction to Big Data Analytics 1
1.1 Introduction 1
1.2 Data and Its Classification 4
1.3 Historical Evolution of the Big Data Era 10
1.4 Understanding Big Data 11
1.5 Big Data Analytics and Its Types 12
1.6 Importance of Big Data Analytics 14
1.7 The Role of Big Data Analytics in Sustainable Society 16
1.8 Conclusion 21
2 Data Management Technologies and Tools 27
2.1 Introduction 27
2.2 Analytics Technologies and Tools 30
2.3 Data Visualization Tools 34
2.4 Data Integration and Preparation Tools 34
2.5 Real-time Data Analytics Technologies 36
2.6 Conclusion 41
3 AI-based Big Data Analytics Techniques 47
3.1 Introduction 47
3.2 Data Quality and Processing Technologies 49
3.3 Statistical Approaches for Big Data Analytics 51
3.4 Machine Learning Models for Big Data Analysis 52
3.5 Deep Learning Architectures in Big Data Analytics 56
3.6 NLP for Textual Big Data 59
3.7 Conclusion 62
4 Real-world Applications of Big Data 69
4.1 Introduction 69
4.2 AI-driven Predictive Maintenance in Manufacturing 71
4.3 Data-driven Decision-making for E-commerce Personalization 76
4.4 Financial Services and Risk Analysis 79
4.5 Health-care Analytics for Patient Care 83
4.6 Role of Big Data in Creating Smart Cities and Efficient Urban Planning 86
4.7 Big Data Analytics for Autonomous Vehicles and Intelligent Transportation Systems 884.8 Conclusion 91
5 Cybersecurity Issues with Big Data and Handling Using Blockchain 97
5.1 Introduction 97
5.2 Cybersecurity Issues with Big Data 99
5.3 Types of Cyber Threats and Vulnerabilities 100
5.4 Understanding Blockchain in Cybersecurity 103
5.5 Decentralized Data Security and Integrity Using Blockchain 106
5.6 Smart Contracts in Big Data Management 107
5.7 Supply Chain Transparency 109
5.8 Challenges and Scalability Considerations 113
5.9 Conclusion 116
6 IoT-enabled Cloud-based Solutions for Big Data Analytics 121
6.1 Introduction to IoT-enabled Big Data Analytics 121
6.2 IoT Devices and Data Generation: Examining Energy Efficiency and Sustainable Device Design 124
6.3 Challenges in IoT Data Processing: Optimizing for Sustainable Resource Consumption 127
6.4 Cloud Computing in IoT Big Data Analytics: Environmental Impacts and Green Cloud Solutions 129
6.5 Integration of IoT and Cloud-based Approaches for Big Data Analytics: Advancing Sustainable Data Handling and Processing Strategies 131
6.6 Conclusion 133
7 Emerging Trends in Big Data Analytics 139
7.1 Introduction 139
7.2 Sustainable Approaches for Big Data Analytics 141
7.3 Edge Computing and IoT Analytics 144
7.4 Quantum Computing-based Analytics 147
7.5 Blockchain and Big Data Analytics 150Table of Contents xi
7.6 XAI-based to Enhanced Interpretability 153
7.7 Conclusion 157
8 Data Governance, Compliance, and Sustainability 163
8.1 Introduction 163
8.2 Ensuring Data Quality and Integrity 165
8.3 Regulatory Framework Adherence 168
8.4 Auditing and Compliance Monitoring Mechanisms 170
8.5 Sustainable Incident Response and Data Breach Management 172
8.6 Training and Awareness Programs for Ethical Use of Big Data 174
8.7 Conclusion 175
9 The Role of Big Data in Scientific Research 181
9.1 Introduction 181
9.2 Advancing Scientific Discovery 183
9.3 Data-intensive Research Fields 184
9.4 Genomic Data Analysis 186
9.5 HPC and Scientific Simulations 189
9.6 Data Collaboration in Global Scientific Projects 191
9.7 Sustainability Considerations in Big Data Research 192
9.8 Real-world Case Studies 194
9.9 Conclusion 197
10 Future Perspectives 203
10.1 Introduction 203
10.2 Emerging Data Management Trends 204
10.3 Generative AI-based Analytics for Sustainable Development of Society 207
10.4 Conclusion 211
Practice Set 212
Further Readings 215
Index 217
About the Companion Website xv
1 Introduction to Big Data Analytics 1
1.1 Introduction 1
1.2 Data and Its Classification 4
1.3 Historical Evolution of the Big Data Era 10
1.4 Understanding Big Data 11
1.5 Big Data Analytics and Its Types 12
1.6 Importance of Big Data Analytics 14
1.7 The Role of Big Data Analytics in Sustainable Society 16
1.8 Conclusion 21
2 Data Management Technologies and Tools 27
2.1 Introduction 27
2.2 Analytics Technologies and Tools 30
2.3 Data Visualization Tools 34
2.4 Data Integration and Preparation Tools 34
2.5 Real-time Data Analytics Technologies 36
2.6 Conclusion 41
3 AI-based Big Data Analytics Techniques 47
3.1 Introduction 47
3.2 Data Quality and Processing Technologies 49
3.3 Statistical Approaches for Big Data Analytics 51
3.4 Machine Learning Models for Big Data Analysis 52
3.5 Deep Learning Architectures in Big Data Analytics 56
3.6 NLP for Textual Big Data 59
3.7 Conclusion 62
4 Real-world Applications of Big Data 69
4.1 Introduction 69
4.2 AI-driven Predictive Maintenance in Manufacturing 71
4.3 Data-driven Decision-making for E-commerce Personalization 76
4.4 Financial Services and Risk Analysis 79
4.5 Health-care Analytics for Patient Care 83
4.6 Role of Big Data in Creating Smart Cities and Efficient Urban Planning 86
4.7 Big Data Analytics for Autonomous Vehicles and Intelligent Transportation Systems 884.8 Conclusion 91
5 Cybersecurity Issues with Big Data and Handling Using Blockchain 97
5.1 Introduction 97
5.2 Cybersecurity Issues with Big Data 99
5.3 Types of Cyber Threats and Vulnerabilities 100
5.4 Understanding Blockchain in Cybersecurity 103
5.5 Decentralized Data Security and Integrity Using Blockchain 106
5.6 Smart Contracts in Big Data Management 107
5.7 Supply Chain Transparency 109
5.8 Challenges and Scalability Considerations 113
5.9 Conclusion 116
6 IoT-enabled Cloud-based Solutions for Big Data Analytics 121
6.1 Introduction to IoT-enabled Big Data Analytics 121
6.2 IoT Devices and Data Generation: Examining Energy Efficiency and Sustainable Device Design 124
6.3 Challenges in IoT Data Processing: Optimizing for Sustainable Resource Consumption 127
6.4 Cloud Computing in IoT Big Data Analytics: Environmental Impacts and Green Cloud Solutions 129
6.5 Integration of IoT and Cloud-based Approaches for Big Data Analytics: Advancing Sustainable Data Handling and Processing Strategies 131
6.6 Conclusion 133
7 Emerging Trends in Big Data Analytics 139
7.1 Introduction 139
7.2 Sustainable Approaches for Big Data Analytics 141
7.3 Edge Computing and IoT Analytics 144
7.4 Quantum Computing-based Analytics 147
7.5 Blockchain and Big Data Analytics 150Table of Contents xi
7.6 XAI-based to Enhanced Interpretability 153
7.7 Conclusion 157
8 Data Governance, Compliance, and Sustainability 163
8.1 Introduction 163
8.2 Ensuring Data Quality and Integrity 165
8.3 Regulatory Framework Adherence 168
8.4 Auditing and Compliance Monitoring Mechanisms 170
8.5 Sustainable Incident Response and Data Breach Management 172
8.6 Training and Awareness Programs for Ethical Use of Big Data 174
8.7 Conclusion 175
9 The Role of Big Data in Scientific Research 181
9.1 Introduction 181
9.2 Advancing Scientific Discovery 183
9.3 Data-intensive Research Fields 184
9.4 Genomic Data Analysis 186
9.5 HPC and Scientific Simulations 189
9.6 Data Collaboration in Global Scientific Projects 191
9.7 Sustainability Considerations in Big Data Research 192
9.8 Real-world Case Studies 194
9.9 Conclusion 197
10 Future Perspectives 203
10.1 Introduction 203
10.2 Emerging Data Management Trends 204
10.3 Generative AI-based Analytics for Sustainable Development of Society 207
10.4 Conclusion 211
Practice Set 212
Further Readings 215
Index 217