Oracle TimesTen In-Memory Database (TimesTen) is a full-featured, memory-optimized, relational database with persistence and recoverability. It provides applications with the instant responsiveness and very high throughput required by database-intensive applications. Deployed in the application tier, TimesTen operates on databases that fit entirely in physical memory (RAM). Applications access the TimesTen database using standard SQL interfaces. For customers with existing application data residing on the Oracle Database, TimesTen is deployed as an in-memory cache database with automatic data synchronization between TimesTen and the Oracle Database.
Vector Databases Usage: Typically used for vector search use cases such as visual, semantic, and multimodal search. More recently, they are paired with generative AI text models for conversational search experiences. Development Process: Begins with building an embedding model designed to encode a corpus (e.g., product images) into vectors. The data import process is referred to as data hydration. Application Development: Application developers utilize the database to search for similar products. This involves encoding a product image and using the vector to query for similar images. k-Nearest Neighbor (k-NN) Indexes: Within the model, k-nearest neighbor (k-NN) indexes facilitate efficient retrieval of vectors. A distance function like cosine is applied to rank results by similarity.
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