Anomaly Diagnosis via Deviation Analysis and Baseline Comparison
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Solution Overview
Problem
Current anomaly detection systems fail to provide clients with a clear understanding of system issues and actionable solutions, lacking the ability to analyze system architecture and peer systems, thus not bridging the gap between anomaly alerts and effective resolutions.
Innovation Solution
The Headliner system, a multi-source analysis engine driven by structured domain models, analyzes performance data from various sources to determine anomalies, provides ranked probable causes, and recommends actionable steps for resolution, using fuzzy logic to handle partial truths and incorporating client feedback for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If anomaly detection systems provide detailed analysis of system architecture and peer systems, then the understanding of system issues improves, but the complexity of the system increases
Solution Approach 1:
The patent introduces an intermediary analysis layer that sits between raw anomaly data and the client. This layer aggregates data from multiple sources (system architecture, peer systems, historical data) and transforms it into synthesized insights. The intermediary performs deviation analysis comparing current performance against baselines and peer systems, then presents unified findings without exposing the underlying complexity to the client.
Solution Approach 2:
The patent merges multiple data sources and analysis methods into a unified diagnostic view. It combines system architecture analysis, peer system comparison, historical performance data, and real-time monitoring into a single integrated anomaly detection system. The merging of these diverse inputs allows comprehensive understanding while presenting a unified, simplified output to the client.
2Reliability
If the system provides comprehensive analysis and recommended procedures, then the effectiveness of resolving anomalies improves, but the time required for analysis increases
Solution Approach 1:
The patent performs preliminary actions by pre-establishing baseline performance metrics for each tier and component before anomalies occur. Historical performance data is collected and analyzed in advance to create expected behavior profiles. When anomalies occur, the system compares against these pre-computed baselines rather than analyzing from scratch, significantly reducing detection time while maintaining comprehensive analysis.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors performance deviations and adjusts its analysis based on learned patterns. Recommended procedures are refined based on feedback from resolving previous anomalies, making the system progressively more efficient. The feedback loop allows the system to optimize analysis paths and provide increasingly accurate recommendations without increasing analysis time.
3Measurement precision
If the system monitors multiple tiers and components in detail, then the precision of anomaly detection improves, but the quantity of data to process increases
Solution Approach 1:
The patent segments the system into distinct tiers (presentation tier, application tier, data tier) and monitors each tier independently with tier-specific baseline metrics. This segmentation allows the system to focus analysis on specific layers where anomalies occur, rather than processing all data uniformly. Each tier's performance is evaluated separately, improving detection precision while reducing overall data processing requirements through hierarchical organization.
Solution Approach 2:
The patent applies local quality by tailoring the monitoring and analysis approach to each specific tier and component type. Different baselines, metrics, and analysis methods are applied locally to each tier based on its specific characteristics and expected behavior patterns. This localized approach ensures high precision for each component type while avoiding the overhead of applying uniform detailed monitoring across the entire system.
Data Source
AI summary
A method for diagnosing system anomalies and presenting recommended solutions is described. The method comprises receiving current component performance data and historical component performance data regarding performance of a component of a multi-tier system. A baseline tier performance for each tier and a baseline component performance is determined. A cause of an anomaly in the performance of the component is determined by discovering deviations in current component performance data and current tier performance in compared to their baseline values. Based on these deviations, a recommended procedure for addressing the cause of the anomaly is determined. A display is formatted within a user interface of the recommended procedure for addressing the cause of the anomaly in the performance of the component.