What technique does Oracle AI Vector Search use to reduce the search space?

Oracle AI Vector Search employs clustering and partitioning techniques to reduce the search space effectively. Clustering involves grouping similar data points together, which allows the search algorithm to focus only on relevant clusters rather than the entire dataset. This drastically enhances the search efficiency and speed by narrowing down the possible matches.

Partitioning further enhances this process by dividing the data into distinct sections, which can be searched independently. By structuring the data in this manner, the search process becomes more manageable and computationally efficient, as the system can avoid irrelevant parts of the data during the search operation. Together, these techniques minimize the volume of data that needs to be processed during searches, leading to quicker and more accurate results.

In contrast, text normalization generally deals with the preparation of data by standardizing text inputs to improve consistency and remove variations that could affect the search outcome. Cloud storage optimization focuses on efficiently utilizing storage resources rather than directly impacting search performance. Basic linear regression is a statistical tool used for predictive modeling rather than for improving the search mechanisms in a vector-based setting.