Question about feature extraction on bgl dataset about logdeep HOT 2 OPEN

If you use machine learning methods, you can directly use the sequence obtained from the sliding window to extract features. I have tried to use the bgl data set on https://github.com/logpai/loglizer. I set window_size=1h and step_size=0.5h to get a better result,
I did not continue to do experiments on the bgl data set on the lstm model :(

But the lstm method requires the input length to be consistent, so you need to set a fixed window like hdfs for unsupervised learning.

I think the event sequence with only 1 event can be ignored as noise during training（donot join training）, and can be simply padding during testing. Of course, this is my simple understanding. . .

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Thank you for the reply!

I think I will try tuning the window_size and step_size to generate the event sequences for bgl data to get a more evenly distributed length of event sequences (ideally not having sequence length varies from 1, 2, 3 to 12541...), then I can proceed to set a better fixed window on the event sequences just like what we do on hdfs to generate sequential vector and then fit the lstm model. :)

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