Telemetry Log Vectorization for Anomaly Detection
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Solution Overview
Problem
Existing approaches face challenges in efficiently processing and understanding large volumes of telemetry data to identify whole-machine states and key indicators of issues, particularly with text-based telemetry log data, which is difficult to analyze and utilize in machine learning due to its complexity and heterogeneity.
Innovation Solution
The system converts log entries into numerical vectors, performs similarity searches, and applies machine learning to identify signatures of known issues, filtering out irrelevant data sources to focus on meaningful telemetry log data, thereby enhancing the precision and recall of identifying known user issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If text-based telemetry log data is directly analyzed using traditional methods, then the complexity and heterogeneity of the data make it difficult to process and understand, but converting to numerical vectors through machine learning enables efficient processing and pattern recognition
Solution Approach 1:
The patent transforms text-based telemetry log data into numerical vector representations, changing the parameter form from unstructured text to structured numerical data. This enables the application of machine learning algorithms and similarity searches, resolving the contradiction by making the data processable while maintaining the information content.
Solution Approach 2:
The patent replaces traditional text analysis methods with machine learning-based vector representation and similarity search. This substitution enables automated pattern recognition and issue identification, significantly improving processing efficiency while handling complex heterogeneous data.
2Reliability
If all telemetry data sources are processed to ensure comprehensive issue detection, then the recall improves, but the false positives increase and processing overhead grows
Solution Approach 1:
The patent extracts only the relevant features and patterns from telemetry data by converting them into vector representations that capture essential information. This extraction process filters out noise and irrelevant data, improving detection accuracy while reducing false positives.
Solution Approach 2:
The system uses similarity search to compare current telemetry vectors against known issue patterns, providing feedback-based identification. This approach learns from known issues and applies the knowledge to detect similar patterns, improving reliability while maintaining low false positive rates through pattern matching rather than exhaustive processing.
3Productivity
If traditional text-based methods are used to identify device issues, then the analysis is difficult to scale, but converting to vector-based machine learning approaches enables scalable processing across multiple devices and data sources
Solution Approach 1:
The patent changes the parameter representation from text to numerical vectors, enabling the use of efficient vector similarity search algorithms. This transformation makes the system scalable to large numbers of devices and data sources while reducing the difficulty of detecting and measuring issues through automated pattern recognition.
Data Source
AI summary
A system can receive a group of computer log entries that comprise letters in an alphabet. The system can convert log entries of the group of computer log entries into respective first vectors that comprise numerical values. The system can perform a first similarity search with respect to the first vectors to identify respective groups of vectors that identify a same known computer issue. The system can perform machine learning on the respective groups of vectors to identify signatures of known computer issues. The system can perform a second similarity search with respect to second vectors and third vectors that correspond to the signatures of known computer issues to identify devices that correspond to the second vectors that have at least one known computer issue of the signatures of known computer issues. The system can store an indication of the devices.