REVIEW 1 cited by
Length Matters: Clustering System Log Messages using Length of Words
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Length Matters: Clustering System Log Messages using Length of Words
read the original abstract
The analysis techniques of system log messages (syslog messages) have a long history from when the syslog mechanism was invented. Typically, the analysis consists of two parts, one is a message template generation, and the other is finding something interesting using the messages classified by the inferred templates. It is important to generate better templates to achieve better, precise, or convincible analysis results. In this paper, we propose a classification methodology using the length of words of each message. Our method is suitable for online template generation because it does not require two-pass analysis to generate template messages, that is an important factor considering increasing amount of log messages produced by a large number of system components such as cloud infrastructure.
Forward citations
Cited by 1 Pith paper
-
CelerLog: Fast Log Parsing via Dynamic Routing
CelerLog dynamically routes logs to statistical or LLM processors based on pattern density, delivering leading accuracy on 14 datasets while being 7.9-18.6x faster than pure LLM parsers and cutting token use by 80-94%.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.