Comments (1)
We convert the templates we found to multiple time-series and then perform anomaly detection on top of those.
Masking improves Drain accuracy and also allows you to extract parameters and perform anomaly detection on those too.
We would gladly accept a PR for grok support ..
A blog post or paper about the next steps of the pipeline is planned for the next few months.
Thanks!
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Related Issues (20)
- Drain3 deprecation warning with pip install command. HOT 2
- visualize drain parse tree (feature) HOT 1
- Hi, I've been trying to use drain for running log anomaly detection on some logs.
- Log Matching on new data HOT 2
- Chinese and English hybrid log template mining HOT 5
- Some DRAIN templates with <*> do not have parameters extracted HOT 7
- PermissionError when running with Persistance
- Is it possible to freeze templates when trainning? HOT 2
- Add a py.typed marker file
- `extra_delimiters` does not account for prefixed/suffixed delimiters
- Drain3 in golang HOT 2
- Masking Prefix and Suffix should not be escaped HOT 1
- A interesting issues. HOT 1
- big_file demo result's first cluster content is empty
- masking question,if i want to output the masking such as real info of the date,how can i putput
- Avoid creating many redis connections when you want to have seperate buckets of templates. HOT 2
- [Question] HOT 2
- one question, how to do Incremental learning in drain3 training?
- one question, how to do Incremental learning in drain3 training? HOT 6
- Release a new https://pypi.org/ version to update dependencies HOT 2
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