Volume 1
Table of Contents (Pg. 1-36), Chapters 1-7 (Pg. 1-161) Chapter 1: Introduction to Database Management
Chapter 2: Data Models: Hierarchical, Network, Relational, and NoSQL
Chapter 3: Importance of Databases in Modern Applications
Chapter 4: Evolution of Database Technologies
Chapter 5: Database Design: ER Diagrams and Normalization
Chapter 6: Database Design: SQL Basics - Queries, Joins, Transactions
Chapter 7: Introduction to Artificial Intelligence
Chapters 8-12 (Pg. 162-326)
Chapter 8: AI in Data Analysis and Decision Making
Chapter 9: Role of AI in Modern Databases
Chapter 10: AI-Driven Database Optimization
Chapter 11: Predictive Analytics and Data Mining in Database Design
Chapter 12: Introduction to Machine Learning
Chapters 13-19 (Pg. 327-498)
Chapter 13: Machine Learning in Databases
Chapter 14: Natural Language Processing (NLP) in Databases
Chapter 15: AI-Powered Database Design: Automated Schema Design
Chapter 16: AI for Data Normalization and Integrity
Chapter 17: Case Studies of AI-Driven Database Design
Chapter 18: AI for Database Security: Threat Detection and Prevention
Chapter 19: AI for Database Security: Anomaly Detection in Database Access
Volume 2
Chapters 20-23 (Pg. 499-623)
Chapter 20: AI for Data Encryption and Privacy
Chapter 21: AI in Data Integration and ETL Processes: Data Cleaning and Transformation
Chapter 22: Automated ETL Pipelines
Chapter 23: Real-Time Data Integration with AI
Chapters 24-25 (Pg. 624-768)
Chapter 24: Query Optimization with AI
Chapter 25: Indexing Strategies and AI
Chapters 26-27 (Pg. 769-940)
Chapter 26: Resource Management and Load Balancing
Chapter 27: Predictive Analytics in AI Data Warehouses
Volume 3
Chapters 28-29 (Pg. 941-1056)
Chapter 28: Handling Large-Scale Data with AI for Big Data Management
Chapter 29: AI in Distributed Databases for Big Data
Chapters 30-32 (Pg. 1057-1175)
Chapter 30: Big Data Analytics and AI
Chapter 31: Cloud Database Services and AI
Chapter 32: AI for Cloud Database Management
Chapters 33-36 (Pg. 1176-1521)
Chapter 33: Real-Time Data Processing with AI
Chapter 34: AI in Database Maintenance and Monitoring
Chapter 35: Ethical Considerations in AI and Databases
Chapter 36: Innovations Shaping AI and Database Management
Volume 4
Chapters 37-38 (Pg. 1522-1637)
Chapter 37: The Future of Autonomous Databases
Chapter 38: Tools and Technologies for AI in Databases
Chapter 39 and Appendix A-E (Pg. 1638-1737)
Chapter 39: Database Management Tools with AI Capabilities
Appendix F-G (Pg. 1738-1908)
Each volume delivers a unique perspective on database management in the AI era, with comprehensive coverage from foundational design principles to ethical considerations in AI applications. Highlights include Chapter 10 on AI-driven optimization and Chapter 35 on ethical concerns, making this collection both an academic treasure and a professional essential.
Whether you're exploring databases for academic purposes or incorporating AI into your professional toolkit, this hardcover set is designed to be a lasting reference in the evolving world of data management.
