Anomaly Influence Extraction via Relationship Graph Shared Extents
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Solution Overview
Problem
Existing methods fail to effectively detect anomaly influence processes when anomalies occur at multiple points in a network system, requiring manual analysis and being dependent on operator expertise, and may not accurately identify the extent of influence.
Innovation Solution
An information processing apparatus that uses a relationship graph to extract reach extents from detected anomaly locations and identifies shared extents across multiple paths, allowing for the extraction of anomaly influence processes even when anomalies occur at multiple points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of logs is performed to locate anomaly cause and extent of influence, then the anomaly can be detected, but the process requires large amount of man-hour and is dependent on operator ability
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated computer-based system that uses relationship graphs and path extraction algorithms to automatically identify anomaly causes and extent of influence, eliminating dependence on operator ability and significantly reducing analysis time
Solution Approach 2:
The system enables self-service anomaly analysis by automatically extracting reach extents and shared extents from relationship graphs without requiring human operators to manually trace through logs, allowing the system to independently identify anomaly propagation paths
2Reliability
If existing anomaly detection methods are used, then single-point anomalies can be detected, but anomalies at multiple points cannot have their influence processes detected
Solution Approach 1:
The patent merges multiple reach extents from different anomaly locations by extracting their shared extents, combining individual anomaly analyses into a unified view that captures the overall anomaly propagation process across the system
Solution Approach 2:
The relationship graph approach provides a universal framework that can handle both single-point and multi-point anomalies uniformly, making the system adaptable to various anomaly scenarios without requiring different detection methods
Data Source
AI summary
An information processing apparatus includes: a reach-extent extraction unit configured to extract, with use of a relationship graph representing relationships between a plurality of elements included in a system and location information that indicates, on the relationship graph, a plurality of locations in the system where anomalies have been detected, paths in the relationship graph as being reach extents, the path including a set of the elements that are directly or indirectly related to each of the plurality of locations as a source; and a shared-extent extraction unit configured to extract an influence process of an anomaly by extracting an extent that is shared in at least a prescribed number of paths among paths in the relationship graph that have been extracted as the reach extents.