Log Management Engine Parameter Suggestion via Activity Clustering
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Solution Overview
Problem
The complexity of modern computing systems and the vast amount of data generated, particularly in data centers, makes it challenging for users to identify and monitor relevant parameters for system health and performance analysis.
Innovation Solution
A computing system with a log management engine that analyzes user activity and suggests parameters to monitor by clustering event log data, using templates, atoms, and similarity estimation to automatically identify relevant parameters based on user input and external monitoring records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually analyze vast amounts of log data to identify relevant parameters, then monitoring accuracy improves, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system performs self-service by automatically analyzing user activity patterns and log data to generate parameter recommendations without requiring manual user intervention. The log management engine autonomously clusters events, identifies patterns, and suggests relevant parameters based on observed user behavior, eliminating the time-consuming manual analysis process while maintaining high accuracy.
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated computational system. The log management engine uses algorithms to cluster log events, estimate similarity between user activities, and generate parameter recommendations, substituting human cognitive effort with automated information processing that is both faster and more consistent.
2Measurement precision
If users manually review all log data to identify relevant parameters, then parameter selection accuracy improves, but operational complexity increases
Solution Approach 1:
The system autonomously performs the complex task of parameter identification by analyzing user activity patterns and log data. The log management engine self-manages the entire process from data collection to parameter recommendation, eliminating the need for users to navigate complex analysis procedures while delivering accurate results.
Solution Approach 2:
The log management engine acts as an intermediary between the vast log data and the user. It processes and filters the complex log information, transforming it into simplified parameter recommendations that are easy for users to understand and act upon, without exposing users to the underlying complexity of log analysis.
3Loss of information
If the system provides detailed analysis of all log parameters, then information completeness improves, but data complexity and user burden increase
Solution Approach 1:
The system extracts only the most relevant parameters from the vast log data based on user activity patterns. Instead of presenting all available log information, the log management engine identifies and extracts the specific parameters that are most useful for the user's current context, reducing information overload while maintaining completeness of relevant data.
Solution Approach 2:
The system applies local quality by providing different levels of detail based on user needs and context. Rather than uniformly presenting all log data with the same level of detail, the log management engine tailors the information presentation to highlight only the locally relevant parameters for each user's specific activity and monitoring needs.
Data Source
AI summary
A non-transitory, computer readable storage device includes software that, while being executed by a processor, causes the processor to choose, based on user activity, a plurality of candidate parameters to be monitored from a plurality of event messages. Further, the processor executes the software to estimate a level of similarity between the chosen plurality of candidate parameters by computing a similarity score for at least two of the chosen candidate parameters. Still further, the processor executes the software to determine a plurality of parameters from the chosen candidate parameters if the similarity score for the plurality of parameters is greater than a threshold.