METING: A Robust Log Parser Based on Frequent n-Gram Mining
Résumé
Execution logs are a pervasive resource to monitor modern information systems. Due to the lack of structure in raw log datasets, log parsing methods are used to automatically retrieve the structure of logs and gather logs of common templates. Parametric log parser are commonly preferred since they can modulate their behaviour to fit different types of datasets. These methods rely on strong syntactic assumptions on log structure e.g. all logs of a common template have the same number of words. Yet, some reference datasets do not comply with these assumptions and are still not effectively treated by any of the state-of-the-art log parsers. We propose a new parametric log parser based on frequent n-gram mining: this soft text-driven approach offers a more flexible syntactic representation of logs, which fits a great majority of log data, especially the challenging ones. Our comprehensive evaluations show that the approach is robust and clearly outperforms existing methods on these challenging datasets.
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