Semantic prefetching predicts an application’s likely data needs from meaning and context rather than from access patterns alone. It can use application state, user-specific data, business logic, or relationships between entities to retrieve data before the application requests it. section 11.2.2 section 11.2
Common semantic signals support different prediction strategies:
- Dependency-based prefetching follows relationships in a database schema or object model, such as loading permissions with a user profile. It may retrieve related data that the application never uses. section 11.2.2
- Context-based prefetching uses current application state, such as location or navigation state, to anticipate the next relevant data. section 11.2.2
- History-based prefetching uses recorded user actions to predict future actions and retrieve data associated with those recurring behaviors. section 11.2.2
- Machine-learning approaches can model complex relationships between data accesses and adapt predictions as workloads change. section 11.2
Checkout and location-aware applications
An e-commerce application can prefetch recommendations, a shopping cart, or address and payment details as a user approaches checkout. A location-aware application can prefetch nearby points of interest based on the current location or the user’s previous interests. section 11.2.2
The central trade-off is prediction accuracy versus resource use. Conservative strategies miss useful opportunities; aggressive strategies waste bandwidth, storage, processing, or cache capacity, and a wrong prediction can increase latency by displacing a better prefetch. section 11.2
