Currently neural network doesn't adjust itself to changing result set however, we should be considering the fact that result set typically changes over time and thus this case should be handled properly.

Acceptance criteria / Research outcome:

• Decision if we should set a fixed limit for the result set, such as 100,000 items.
  • Proposal what that limit should be.
  • Try if integer.maximum works.
  • Find a graceful fallback once all output units are "used up" (ideally forgetting old/irrelevant ones).
• Improve performance, potentially by reducing the overall number of units (both in hidden layers and above-mentioned output layer).
• Improve neural network storage performance
  • Debounce storage; only store once per time unit (default to e.g. 2 mins).
  • Assure proper synchronization on storage operations.

• Timebox research to 5 SP.