Optimizing Nextgen Communication Networks with Decision Analytics 1

Advanced Decision Intelligence for Scalable Networks, Enterprise Systems and Real-Time Analytics
1. Auflage 2026
ISBN: 978-1-83669-059-7
Verlag: Wiley

Buch, Englisch, 304 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 603 g

In the rapidly evolving era of intelligent communication systems and digital transformation, Optimizing NextGen Communication Networks with Decision Analytics 1 explores the technologies reshaping enterprises, healthcare, finance, education and society. It brings together research and practical innovations in artificial intelligence (AI), cloud computing, big data analytics, machine learning, smart enterprise systems, immersive technologies and sustainable digital practices.

This book highlights scalable architectures for real-time data processing, AI-driven automation, cloud optimization, smart retail analytics, cybersecurity, healthcare intelligence and financial prediction models. It also examines emerging themes such as augmented reality, sustainability, inclusive digital ecosystems, organizational transformation and predictive decision-making through intelligent analytics.

Integrating theoretical foundations with practical applications and case studies, this book is a valuable resource for researchers, academics, industry professionals, policymakers and postgraduate students interested in next generation intelligent systems and decision analytics.

Autoren/Hrsg.

Pradhan, Devasis

Tun, Hla Myo

Banjerdpongchai, David

Chatterjee, Prasenjit

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik
• Technische Wissenschaften Bauingenieurwesen Mathematische Methoden, Computeranwendungen (Bauingenieurwesen)
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Preface xv
Devasis PRADHAN

Acknowledgments xvii

Chapter 1. Real-Time Big Data Processing in the Cloud: Scalable, Cost-Efficient, AI-Driven Solutions for Financial Analytics 1
Saravanan Thirumazhisai PRABHAGARAN, Ishu Anand JAISWAL and Hina GANDHI

1.1. Introduction 1
1.2. The evolution of big data in finance 2
1.3. Architectural components of real-time analytics 3
1.4. Real-time ingestion and stream processing 4
1.5. Cost optimization strategies in cloud infrastructure 6
1.6. Case studies and applications in financial analytics 8
1.7. How data enters the pipeline 9
1.8. Processing in real-time 9
1.9. Storing the results 9
1.10. Scaling without blowing the budget 10
1.11. A few examples 10
1.12. Additional implementation stories 11
1.13. Common pitfalls and how to avoid them 14
1.14. Lessons learned 14
1.15. Where the model sits in the pipeline 15
1.16. Example: fraud model in production 15
1.17. How teams deploy models 16
1.18. Retraining and model drift 16
1.19. Latency considerations 17
1.20. A few more lessons 19
1.21. Why people do it anyway 19
1.22. What usually goes wrong 19
1.23. How some teams made it work 20
1.24. A few stories 21
1.25. Conclusion 23
1.26. References 23

Chapter 2. Scalable Microservices and Data Integration for Modern eCommerce and Enterprise Systems 27
Aneeshkumar P. SUNDESWARAN, Dilip Prakash VALANARASU and Anaswara Thekkan RAJAN

2.1. Introduction 28
2.2. Literature review 32
2.3. Methodology 35
2.4. Results 42
2.5. Conjunction with former work 45
2.6. Future direction 46
2.7. Conclusion 48
2.8. References 51

Chapter 3. Modern Data Engineering on GCP: Scalable Pipelines, Real-Time Processing and Governance 55
Jagadeesh THIRUVEEDULA and Souvari Ranjan BISWAL

3.1. Introduction 55
3.2. Designing scalable pipelines 57
3.3. Real-time data processing 58
3.4. Data governance and compliance 59
3.5. Case study: real-time fraud detection 61
3.6. Advanced patterns and best practices 61
3.7. Monitoring and observability 63
3.8. Cost optimization strategies 63
3.9. ML integration 64
3.10. Looking ahead 64
3.11. Final thoughts 65
3.12. References 65

Chapter 4. AI for Personalization and Security in Digital Travel Platforms 67
Kartheek DOKKA, Saravanan Thirumazhisai PRABHAGARAN and Maheswari GOVINDARAJU

4.1. Introduction 67
4.2. Evolution of AI in travel and digital services 70
4.3. AI-driven personalization techniques 74
4.4. AI in platform and transactional security 76
4.5. Integration of AI in travel platforms 78
4.6. Challenges and ethical considerations 81
4.7. Future outlook and strategic implications 85
4.8. Conclusion 87
4.9. References 88

Chapter 5. Modernizing Legacy E-commerce Platforms: Cloud Migration, DevOps and API-First Strategies 91
Dilip Prakash VALANARASU and Arnab KAR

5.1. Introduction 92
5.2. Understanding legacy e-commerce architectures 94
5.3. Cloud migration strategies for legacy systems 97
5.4. DevOps as a catalyst for e-commerce agility 101
5.5. API-first strategy: modularizing e-commerce architectures 105
5.6. Security, observability and compliance in modern platforms 108
5.7. Strategic roadmap and future directions 111
5.8. Conclusion 113
5.9. References 114

Chapter 6. Building Scalable and Resilient Stream Processing Systems with Kafka and Serverless Tools 119
Vamsi Krishna KOGANTI and Naveen Saikrishna PUPPALA

6.1. Introduction: the evolution of stream processing in cloud-native systems 119
6.2. Apache Kafka: core principles and its role in stream ingestion 121
6.3. Serverless computing paradigms in stream processing 123
6.4. Architecting scalable stream processing pipelines 126
6.5. Resilience and fault tolerance in distributed stream systems 129
6.6. Performance optimization, observability and cost management 132
6.7. Conclusion: future directions for serverless and Kafka-based architectures 134
6.8. References 136

Chapter 7. AI-Driven Automation for Scalable Media, Data and Search Systems 139
Aatishkumar DHAMI and Maheswari GOVINDARAJU

7.1. Introduction 139
7.2. The rise of automated data pipelines 141
7.3. Scaling media processing without losing control 142
7.4. The art and trouble of real-time search systems 143
7.5. Building robust, automated data pipelines 147
7.6. Machine learning in analytics and personalization 148
7.7. Monitoring and keeping systems trustworthy 149
7.8. Training methods for sorting 151
7.9. Checks and alerts 152
7.10. Manual controls and overrides 152
7.11. Monitoring and cost controls 154
7.12. Fast updates and small delays 155
7.13. Checks that made a difference 155
7.14. When costs rose fast 156
7.15. Adjustments made during high-volume events 156
7.16. Conclusion 158
7.17. References 158

Chapter 8. Scalable and Cost-Efficient Sap Cloud Migration and Operations for Large Enterprises 163
Ankit Kumar GUPTA

8.1. Introduction 163
8.2. Background and related work 166
8.3. Drivers and benefits of SAP cloud migration 170
8.4. Migration planning framework 172
8.5. Migration execution strategies 174
8.6. Post-migration optimization 175
8.7. Case studies 179
8.8. Challenges, risks and future directions 181
8.9. Conclusion 184
8.10. References 184

Chapter 9. Integrative Morphological Assessment of Ayurvedic Haritaki (Terminalia chebula Retz.) Varieties: A Comparative Study Across Indian Regions and the Western Ghats 191
Komal TAYADE, Elingbam SINGH, Varsha GHATE, Suresh JAGTAP and Rakshanda PATIL

9.1. Introduction 192
9.2. Materials and methods 194
9.3. Results and discussion 197
9.4. Conclusion 205
9.5. References 205

Chapter 10. Smart Analytics in SAP: Streamlining Big Data for Faster, Smarter Business Decisions 207
Vaidheyar Raman BALASUBRAMANIAN

10.1. Introduction 208
10.2. Applications and use cases across industries 212
10.3. Tools, functionalities and AI/ML integration in SAP smart analytics 215
10.4. Implementation challenges and best practices for SAP smart analytics 219
10.5. Future scope 223
10.6. Conclusion 226
10.7. References 226

Chapter 11. AI-Powered Feedback Loops in Digital Advertising 229
Divij PASRIJA

11.1. Introduction 229
11.2. Understanding feedback loops in digital advertising 232
11.3. Role of AI in enhancing feedback mechanisms 237
11.4. Architectures and models for AI-driven ad feedback loops. 241
11.5. Real-world applications 244
11.6. Challenges, ethical considerations and future directions 248
11.7. Conclusion 251
11.8. References 251

List of Authors 257
Index 261

Über Autor(innen)

Devasis Pradhan is the Assistant Director of Research at Acharya Institutes, and the Dean of Research at the Acharya Institute of Technology, Bangalore, India. His research specializes in AI, communication networks, computational intelligence, data analytics and emerging digital technologies.

Hla Myo Tun is the Rector of Yangon Technological University, Myanmar. His research specializes in intelligent systems, engineering technologies, data analytics and next-generation communication infrastructures.

David Banjerdpongchai is a Professor at Chulalongkorn University, Thailand. His research specializes in advanced computing, AI, smart technologies and interdisciplinary engineering applications.

Prasenjit Chatterjee is a Professor and Dean (Research and Consultancy) at MCKV Institute of Engineering, India. His expertise specializes in decision analytics, sustainable systems, operations management and AI-driven engineering innovation.