Anomaly Detection via Tripoint Arbitration
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Solution Overview
Problem
Existing anomaly detection methods rely on assumptions about the underlying data and require skilled administrators to set and tune parameters, making them impractical for heterogeneous data environments, such as health monitoring systems in cloud services, where diverse data types need continuous monitoring.
Innovation Solution
The use of tripoint arbitration for similarity analysis, which determines dissimilarity based on unbiased observations between data points within the sample, eliminating the need for external analyst assumptions and parameter tuning by using a distance function specific to the data type, and aggregating similarities through arbiter points to create an aggregate similarity matrix for clustering and anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical approaches and machine learning methods are used for anomaly detection, then detection capability is improved, but requirement for skilled administrators to set and tune parameters increases
Solution Approach 1:
The system performs self-service by automatically selecting appropriate distance functions and tuning parameters based on the intrinsic properties of the data being analyzed. The anomaly detection system adapts to different data types (numeric, categorical, text, time series) autonomously without requiring external administrator intervention for parameter configuration.
Solution Approach 2:
The system dynamically changes parameters based on the data characteristics. Different distance functions are automatically selected depending on the data type being analyzed, and the system adapts its computational approach to match the specific properties of the input data, eliminating the need for manual parameter tuning.
2Measurement precision
If distributional assumptions are made about the data, then statistical anomaly detection can be performed, but applicability to heterogeneous data environments decreases
Solution Approach 1:
The system applies local quality by selecting different distance functions tailored to specific data types. Each data type (numeric, categorical, text, time series) receives a specialized distance function optimized for its characteristics, allowing the system to maintain high detection accuracy across heterogeneous data environments without making universal distributional assumptions.
Solution Approach 2:
The system achieves universality by providing a unified anomaly detection framework that can handle multiple data types through automatic selection of appropriate distance functions. The same core algorithm adapts to diverse data formats including numeric, categorical, text, and time series data, making the system versatile across different domains.
3Measurement precision
If traditional anomaly detection methods are used, then detection of anomalies can be achieved, but computational complexity increases for large-scale heterogeneous data
Solution Approach 1:
The system segments the computational task by dividing the data processing into type-specific stages. Each data type undergoes preprocessing and distance function selection appropriate to its characteristics, allowing the system to manage computational complexity through structured decomposition of the overall anomaly detection process.
Data Source
AI summary
Systems, methods, and other embodiments associated with anomaly detection using tripoint arbitration are described. In one embodiment, a method includes identifying a set of clusters that correspond to a nominal sample of data points in a sample space. A point z is determined to be an anomaly with respect to the nominal sample when, for each cluster, a tripoint arbitration similarity between data points in the cluster calculated with z as arbiter is greater than a threshold.