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Vector Search in Databases for AI Seminar
Last Updated: 2026-06-01 11:30:53
Abstract
The seminar covers core concepts and state-of-the-art research of filtered vector search. Filtered vector search is the building block of database queries that combine vector search with relational query operators. Many databases are adding support for vector search driven by the demand of RAG and semantic search for access to the information that databases store.
Objective
Students will learn to: - Understand trends, opportunities, and requirements of integrating vector search in databases. - Analyze and critique state-of-the-art systems, techniques, and data structures for vector search presented in research papers. - Discuss and analyze the new capabilities enabled by querying structured and unstructured data via vector search. - Lead and participate in technical discussions about research papers
Content
Topics include definition of vector search, discussion of target use cases (RAG, Semantic Search, Recommendation systems), vector search index data structures, query optimization across relational operators and vector search, challenges to achieving high recall and high performance introduced by relational operators.
Resources
Literature
The research paper reading list will be posted on the course website and will include papers from conferences like VLDB, SIGMOD, CIDR, KDD, WWW, USENIX, NIPS.
General Information
- Language
- English
- Levels
- MSC
Examination
- Type
- graded semester performance
Registration & Places
- Max Places
- 25
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| seminar | Vector Search in Databases for AI Seminar |
|
2 h weekly |