Full-Dimensional PCA for Complex Data Anomaly Detection
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Solution Overview
Problem
Existing data anomaly detection methods, such as the 3-sigma rule, fail to accurately identify anomalies in complex data sets, leading to incorrect results in downstream processes.
Innovation Solution
Generate a new coordinate system using principal component analysis (PCA) without reducing dimensions, transform data into this system, and apply a sigma rule to detect anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sigma rule is used for anomaly detection, then the detection method is simple, but the detection accuracy is low for complex data sets
Solution Approach 1:
The patent transforms data from the original coordinate system to a new coordinate system using PCA, effectively changing the dimensional perspective. This allows anomalies that are hidden in complex multi-dimensional relationships to become detectable as simple deviations along principal components, resolving the contradiction between maintaining simplicity and improving detection accuracy.
Solution Approach 2:
The patent changes the parameter representation by transforming data into principal component space, where anomalies are detected using the same sigma rule but applied to transformed parameters. This parameter transformation maintains the simplicity of the sigma rule while improving detection accuracy through better parameter representation.
2Measurement precision
If PCA is applied to transform data into a new coordinate system, then anomaly detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies PCA to transform data into a new coordinate system, which requires computational resources. However, by focusing on the principal components that capture the most variance, the method achieves high detection accuracy with reasonable computational effort, applying the transformation only where most beneficial.
Data Source
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AI summary
Systems and methods are directed to detecting data anomalies. A data analysis system accesses data generated on a data platform. The data analysis system then analyzes the data to detect one or more data anomalies. The analyzing includes generating an optimal coordinate system without reducing a number of dimensions using principal component analysis (PCA), transforming the data into the optimal coordinate system without reducing the number of dimensions, and applying a sigma rule to the transformed data on the optimal coordinate system. The sigma rule can be the 3-sigma rule. In some cases, the data analysis system generates and transmits a notification or alert to a user or downstream component regarding the one or more data anomalies. In some cases, the data analysis system removes the one or more data anomalies to derive updated data and can provide the updated data to downstream systems for use.