Based on success logs, logreduce highlights useful text in failed logs.
The goal is to save time in finding a failure's root cause.
On average, learning run at 2000 lines per second, and
testing run at 1300 lines per seconds.
logreduce uses a model to learn successful logs and detect novelties in
- Random words are manually removed using regular expression
- Then lines are converted to a matrix of token occurrences
- An unsupervised learner implements neighbor searches