Causal Map Root Cause Analysis for Network Feature Independence
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for root cause analysis in network environments struggle to determine directional cause-effect relationships between features due to limitations in time-aggregated data and reliance on domain knowledge, failing to provide comprehensive explanations for corrective actions.
Innovation Solution
A method and apparatus that generate a causal map by performing independence tests on a dataset of network features, identifying dependent relationships and pathways to determine root causes, using a hierarchical architecture and statistical tests like the chi-squared test to establish causality without requiring real-time data or deep domain expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical approaches are used to detect association patterns, then the accuracy of root cause analysis is improved, but the complexity of the system increases due to the need for independence tests and causal map construction
Solution Approach 1:
The patent segments the feature set into multiple subsets (first subset, second subset, third subset) and performs independence tests in staged manner across different time windows. This segmentation reduces the complexity of analyzing all features simultaneously while maintaining accurate causal detection through systematic, layered analysis.
Solution Approach 2:
The patent performs preliminary independence tests between features and the primary feature before conducting the full causal analysis. By pre-filtering features based on statistical independence in earlier time windows, the system reduces the search space for causal relationships, improving accuracy without proportionally increasing complexity.
2Ease of operation
If domain knowledge and prior correlations are used to determine corrective actions, then the ease of operation is improved, but the adaptability to new network conditions deteriorates
Solution Approach 1:
The patent implements a self-service approach where the system automatically performs statistical independence tests and constructs causal maps without requiring manual domain knowledge input. The automated statistical methods adapt to new network conditions by learning patterns from data, eliminating the need for operators to manually configure domain-specific rules while maintaining ease of operation.
Solution Approach 2:
The patent uses statistical parameters (independence test results, p-values, correlation coefficients) to dynamically determine causal relationships rather than relying on fixed domain knowledge thresholds. This allows the system to adapt to changing network conditions by adjusting its analysis based on actual data patterns rather than static pre-configured rules.
3Ease of manufacture
If time-aggregated data is used for analysis, then the ease of manufacture and data processing is improved, but the measurement precision of causal relationships deteriorates due to inability to detect directional cause-effect
Solution Approach 1:
The patent segments the time-aggregated data into multiple time windows (first time window, second time window, third time window) and performs independence tests on each segment separately. This allows the system to maintain the processing efficiency of aggregated data while recovering directional causal information by analyzing temporal patterns across segmented periods, thereby improving measurement precision without sacrificing ease of processing.
Data Source
AI summary
Embodiments described herein relate to a method and an apparatus for determining a first causal map for the root cause analysis of a primary event in a network environment. A method, implemented in an apparatus, comprises obtaining (302) a first data set, wherein each entry in the first data set comprises values of a plurality of features representative of the network environment, wherein the plurality of features comprises a primary feature representative of the primary event; for each first feature in a first subset of the plurality of features, performing (304) an independence test on the first data set to determine a relationship between the first feature and the primary feature; for each first feature in the first subset for which the independence test indicates a dependent relationship to the primary feature, performing (306) the independence test on the first data set to determine a relationship between the first feature and each second feature in a second subset of the plurality of features; and based on results of the steps of performing the independence test, determining (308) one or more pathways in the first causal map between at least one root cause for the primary event and the primary feature.