Log Parsing
10 papers with code • 0 benchmarks • 0 datasets
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.
Benchmarks
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Most implemented papers
Self-Supervised Log Parsing
This allows the coupling of the MLM as pre-training with a downstream anomaly detection task.
Delog: A Privacy Preserving Log Filtering Framework for Online Compute Platforms
In many software applications, logs serve as the only interface between the application and the developer.
On Automatic Parsing of Log Records
We create a tool that generates synthetic Apache log records which we used to train recurrent-neural-network-based MT models.
Log-based Anomaly Detection Without Log Parsing
The log parsing errors could cause the loss of important information for anomaly detection.
LogAI: A Library for Log Analytics and Intelligence
In order to enable users to perform multiple types of AI-based log analysis tasks in a uniform manner, we introduce LogAI (https://github. com/salesforce/logai), a one-stop open source library for log analytics and intelligence.
Log Parsing: How Far Can ChatGPT Go?
Our results show that ChatGPT can achieve promising results for log parsing with appropriate prompts, especially with few-shot prompting.
Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies
LogPrompt employs large language models (LLMs) to perform online log analysis tasks via a suite of advanced prompt strategies tailored for log tasks, which enhances LLMs' performance by up to 380. 7% compared with simple prompts.
On the Effectiveness of Log Representation for Log-based Anomaly Detection
mooselab/suppmaterial-logrepforanomalydetection
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We believe our comprehensive comparison of log representation techniques can help researchers and practitioners better understand the characteristics of different log representation techniques and provide them with guidance for selecting the most suitable ones for their ML-based log analysis workflow.
Learning Representations on Logs for AIOps
Pranjal-Gupta2/learning-representations-on-logs-for-aiops
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Automated log analysis is a critical task in AIOps as it provides key insights for SREs to identify and address ongoing faults.
Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging
Log parsing, which entails transforming raw log messages into structured templates, constitutes a critical phase in the automation of log analytics.
