Chi-Squared Device Attribute Analysis for Root Cause Detection
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Solution Overview
Problem
Existing methods for identifying root causes of issues in computing infrastructures often result in information overload and fail to accurately surface actionable commonalities, leading to inefficient troubleshooting.
Innovation Solution
Utilizing a chi-squared statistical test to analyze the distribution of device attributes in a computing infrastructure, comparing expected and observed distributions to generate a report highlighting attributes with a high probability of being root causes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used to identify root causes, then all device attributes are collected and analyzed, but this results in information overload and fails to accurately surface actionable commonalities
Solution Approach 1:
The patent segments the analysis by dividing device attributes into different groups and applying the chi-squared test separately to each attribute. This segmentation allows the system to focus on one attribute at a time, preventing information overload while maintaining accurate identification of root causes through statistical significance testing.
Solution Approach 2:
The patent changes the parameter of analysis by using statistical probability values (p-values) from chi-squared tests to rank attributes. Instead of analyzing all attributes equally, the system transforms the data into probability metrics that highlight only those attributes with statistically significant deviations, thereby filtering out noise and preventing information overload.
2Measurement precision
If all device attributes are analyzed to ensure comprehensive root cause identification, then accuracy is improved, but the complexity of the analysis process increases
Solution Approach 1:
The patent applies partial action by using the chi-squared test to identify only the most significant attributes rather than analyzing all attributes in equal detail. The statistical significance threshold allows the system to focus on a subset of attributes that are most likely to be root causes, reducing analysis complexity while maintaining accuracy for the most critical factors.
Solution Approach 2:
The patent simplifies the complex analysis process by transforming multi-dimensional attribute data into a single probability metric (p-value) through the chi-squared test. This parameter transformation allows attributes to be ranked and compared easily, reducing the complexity of the analysis process while preserving the ability to identify root causes accurately.
3Measurement precision
If statistical testing is applied to each attribute, then the ability to identify significant commonalities is improved, but the computational resources required increase
Solution Approach 1:
The patent segments the computational workload by applying the chi-squared test to individual attributes separately rather than analyzing all attributes simultaneously. This segmentation allows the system to process attributes in manageable units, reducing peak computational resource requirements while still achieving precise identification of significant commonalities through statistical testing.
Data Source
AI summary
A set of devices may be monitored and a subset of the set of devices which is experiencing a problem may be determined. A cardinality of the subset of the set of devices may be divided by a cardinality of the set of devices to obtain a ratio. An expected distribution over a set of attribute values may be determined by multiplying the ratio with a count of devices corresponding to each attribute value. An observed distribution over the set of attribute values may be determined by counting devices which are experiencing the problem corresponding to each attribute value. A probability value may be determined which represents a probability that the expected distribution matches the observed distribution. A report may be generated which includes attributes and the associated probability values, where an attribute is included in the report if the corresponding probability is less than a threshold.