CS6530

This graduate-level course covers the design and implementation of relational database system kernels and advanced data management techniques. Topics include relational models, SQL, indexing (in-memory, learned), storage (row vs. column stores), and query processing. It also explores AI-driven database optimization, automatic database tuning (self-driving databases), transactions, concurrency control, logging, and recovery. The course discusses modern application of AI in DBMS, focusing on self-optimization, security, NLP for database queries, and human-centric data management. Additional topics include differential privacy, probabilistic databases, data provenance, retrieval-augmented generation (RAG), vector databases, data lakes, and query result diversification. Ethical considerations in data management are also discussed. Students will engage in hands-on projects, implementing core database modules and exploring modern large-scale data management techniques.

CS 3500.

Fall