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Log Parsing

Log Parsing is the task of transforming unstructured log data into a structured format that can be used to train machine learning algorithms. The structured log data is then used to identify patterns, trends, and anomalies, which can support decision-making and improve system performance, security, and reliability. The log parsing process involves the extraction of relevant information from log files, the conversion of this information into a standardized format, and the storage of the structured data in a database or other data repository.

Papers

Showing 2129 of 29 papers

TitleStatusHype
Interactive Log Parsing via Light-weight User Feedback0
System Log Parsing: A Survey0
SPINE: a scalable log parser with feedback guidance0
ESTA: An Esports Trajectory and Action DatasetCode0
Feature Selection for Fault Detection and Prediction based on Event Log Analysis0
Log-based Anomaly Detection Without Log ParsingCode1
On Automatic Parsing of Log RecordsCode0
Self-Supervised Log ParsingCode2
Delog: A Privacy Preserving Log Filtering Framework for Online Compute PlatformsCode0
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