Understanding Indexing Efficiency for Approximate Nearest Neighbor Search in High-dimensional Vector Databases
Deep learning has transformed almost all types of data (e.g., images, videos, documents) into high-dimension vectors, which in turn forms Vector Databases as the data engines of various applications. As a result, queries on vector databases have become the cornerstone for many important online servi...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Published: |
Massachusetts Institute of Technology
2024
|
| Online Access: | https://hdl.handle.net/1721.1/156935 |