Single Click Delta Analysis for Network Log Variance Detection
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Solution Overview
Problem
Analyzing vast amounts of data from computer networks for IT security, operations, and compliance is difficult, expensive, and ineffective due to its voluminous and rapid generation, requiring significant expertise to identify relevant information.
Innovation Solution
A data collection and analysis platform that enables single-click delta analysis, allowing users to perform advanced analysis capabilities with intuitive access, including comparison of results across different time ranges and data sources, through clustering and summarization of log data, and automatic parser selection and generation for efficient data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert contractors manually analyze large volumes of rapidly generated network data, then relevant information can be identified, but the process becomes expensive and time-consuming
Solution Approach 1:
The system enables automated self-analysis of network data through machine learning models and algorithms that automatically identify relevant information without requiring manual expert intervention. The platform performs clustering, anomaly detection, and pattern recognition autonomously, allowing the data analysis system to serve itself rather than relying on external expert contractors.
Solution Approach 2:
The patent replaces the mechanical manual analysis process performed by expert contractors with automated computational systems including machine learning models, clustering algorithms, and data processing pipelines. These electronic and algorithmic systems substitute human experts, enabling rapid automated analysis of large-scale network data while maintaining or improving identification accuracy.
2Measurement precision
If expert contractors are used to analyze voluminous network data, then relevant information can be identified, but the cost increases significantly
Solution Approach 1:
The system performs automated self-analysis using machine learning models and algorithms that identify relevant information without requiring expensive external expert contractors. The platform autonomously executes clustering, anomaly detection, and pattern recognition tasks, eliminating the need for costly manual expert intervention while maintaining high identification accuracy.
Solution Approach 2:
The patent replaces the expensive mechanical process of hiring expert contractors with automated computational systems including machine learning models, data processing algorithms, and intelligent analysis pipelines. These electronic systems substitute human experts, significantly reducing analysis costs while maintaining or improving identification accuracy through scalable automated processing.
3Measurement precision
If complex queries and manual analysis methods are used, then detailed analysis can be performed, but the ease of operation decreases
Solution Approach 1:
The patent introduces an intelligent intermediary layer between the user and the complex data analysis processes. This intermediary includes automated machine learning models, clustering algorithms, and smart query generation systems that translate simple user inputs into sophisticated analysis operations. The intermediary handles the complexity internally while presenting simplified interfaces to users, enabling deep analysis through simple interactions.
Solution Approach 2:
The system performs self-service by automatically generating and executing complex analysis queries without requiring users to manually construct them. The platform autonomously performs clustering, anomaly detection, and pattern recognition based on simple user inputs or predefined configurations, eliminating the need for users to understand or write complex queries while maintaining deep analytical capabilities.
4Productivity
If automated processing is implemented, then efficiency increases, but the complexity of the system increases
Solution Approach 1:
The patent segments the automated data processing system into distinct modular components including data collection modules, preprocessing modules, machine learning model modules, clustering algorithms, anomaly detection modules, and visualization modules. Each module performs a specific function and can be independently developed, deployed, and maintained. This segmentation enables high processing efficiency through specialized automated processing while managing system complexity through modular architecture that allows independent management of each component.
Data Source
AI summary
Single-click delta analysis is disclosed. A user query of status information collected from one or more monitored devices is received from a user. In response to receiving an indication from the user to determine a variance between different portions of the collected status information, a target query and a baseline query are generated using the user query. The generated target query and the generated baseline query are performed, respectively, against data in a data store including the status information collected from the one or more monitored devices. A target set of status information results and a baseline set of status information results are obtained in response to performing, respectively, the generated target query and the generated baseline query. The obtained target and baseline sets of results are combined. Output indicative of a variance between the target and baseline sets of status information results is provided based at least in part on the combining.