Transformer Log Encoding for Unified Telemetry Anomaly Analysis
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Solution Overview
Problem
Existing log analysis techniques face challenges due to non-standardized recording practices across different systems, leading to inefficiencies in automating parsing and analyzing logs, which requires significant memory, time, and computing resources, and are not easily generalizable across various tasks.
Innovation Solution
A neural network-based approach that encodes log messages using transformer encoders and contrastive learning to produce unified representations, enabling efficient encoding and classification without task-specific labels, and combines log information with telemetry data for enhanced anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional log parsing and analysis techniques are used, then logs can be processed, but significant memory, time, and computing resources are consumed
Solution Approach 1:
The patent transforms log data from raw text format into numerical vector representations through embedding layers, changing the parameter form from categorical strings to continuous numerical vectors. This transformation enables efficient processing by neural networks while reducing the computational complexity of text parsing operations.
Solution Approach 2:
The patent replaces traditional mechanical log parsing methods (regex-based extraction, manual pattern matching) with a neural network-based encoding system. The transformer encoder automatically learns patterns and representations from log data, substituting rule-based mechanical processing with adaptive learned representations that require fewer computational resources.
2Adaptability or versatility
If traditional log analysis methods are used, then logs can be analyzed, but the methods are not easily generalizable across various tasks and domains
Solution Approach 1:
The patent creates a universal log encoding system using transformer encoders that can be applied across multiple tasks (anomaly detection, incident prediction, root cause analysis) and different domains. The same encoding architecture processes logs from various systems uniformly, enabling one system to serve multiple functions without task-specific customization.
Solution Approach 2:
The patent segments log analysis into distinct functional components: encoding layer, transformer encoder, and task-specific processing layers. This segmentation allows the universal encoding component to handle domain-general features while task-specific layers handle particular requirements, reducing overall system complexity through modular design.
3Reliability
If logs from different systems are processed, then comprehensive monitoring is achieved, but non-standardized recording practices make automation challenging
Solution Approach 1:
The patent employs dynamic neural network-based encoding that adapts to different log formats and styles from various systems. Rather than requiring static parsing rules for each system, the transformer encoder dynamically learns appropriate representations from the data itself, automatically adjusting to non-standardized practices across different logging systems.
Data Source
AI summary
Methods, systems, and machine-readable mediums to perform a neural network to encode log data. In at least one embodiment, a processor comprising one or more circuits to encode at least one log message, at least in part, by encoding a first type of information in the at least one log message to obtain a first encoding, encoding a second type of information in the at least one log message to obtain a second encoding, and obtaining a resultant encoding at least in part by combing at least the first and second encodings.