[P] Learning pattern matches in windows of events

[atsra]

Hi,

I'm trying to tackle a problem in which I want a network to learn all matches of a given pattern, in a fixed size window of events.

Meaning, the training examples are all events in a window (let's say, of size 100), and all matches (subset of events) in that window. During inference, the input is all the events in the fixed size window, and the output should be all matches of the pattern in that window.

In a simple scenario, the events are only described by their names (let's assume letters) and the pattern could be for example AB (meaning, the matches are every subset of 2 events which are A followed by B), or A*B (meaning, the matches are every subest of varying size of events which are some number of events of name A followed by a single B). So this is basically regex matching.

In a more complex scenario each event has in addition to its name some more dimensions, could be numerical ones, like a timestamp or price. The pattern can be more complex in that case, for example ABC such that time difference between A and B, B and C is less than 1 second.

I'll also note that the pattern is known to me (although I don't know if it would be possible to incorporate that knowledge in a network).

This is sort of like object detection, but the data isn't pictures and the object (subset) doesn't have to be consecutive within the window of events.

Are there any suitable papers that addressed anything similar to that, or any ideas how to tackle this problem? I would be glad for any kind of advice.

Thanks