Interactive Anomaly Detection System for Operational Analytics
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Solution Overview
Problem
Operational analytics face challenges in big data scenarios due to the complexity of formulating queries to extract meaningful insights from large volumes of data, especially when experts are scarce and data relationships are intricate, leading to difficulties in detecting system anomalies and patterns effectively.
Innovation Solution
An interactive ecosystem with a data processor, anomaly processor, and interaction processor that automatically detects system anomalies and patterns without requiring explicit queries, using a limited set of anomaly detectors and pattern recognition schemes, and provides a graphical user interface for prioritization and feedback-based analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If query-based operational analytics are used to extract insights from operations data, then domain experts can analyze specific aspects of system operations, but the complexity of formulating appropriate analytic queries increases significantly in big data scenarios
Solution Approach 1:
The system performs automatic anomaly detection and pattern recognition without requiring domain experts to formulate queries. The anomaly processor autonomously analyzes operations data, detects anomalies based on learned patterns, and generates results that are directly presented to users, eliminating the need for complex query formulation while maintaining analytical accuracy
Solution Approach 2:
The patent introduces an intermediary anomaly processing system that sits between the raw operations data and the domain expert. This intermediary automatically performs the complex analysis work by detecting anomalies, identifying patterns, and generating meaningful insights, thereby shielding users from query formulation complexity while preserving measurement precision
2Reliability
If comprehensive anomaly detection is performed on large volumes of operations data, then system anomalies and patterns can be detected effectively, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by continuously learning normal operational patterns from historical data and pre-establishing baseline behaviors. When new data arrives, the anomaly detection leverages these pre-learned patterns to quickly identify deviations, reducing processing time while maintaining detection effectiveness
Solution Approach 2:
The anomaly processing is segmented into distinct components: pattern learning from historical data, real-time anomaly detection against learned patterns, and result generation. This segmentation allows each component to be optimized independently, improving overall processing efficiency while maintaining comprehensive detection capability
3Ease of operation
If a limited set of anomaly detectors and pattern recognition schemes are used, then the system complexity is reduced and ease of operation improves, but the ability to detect all types of system anomalies and patterns may be limited
Solution Approach 1:
The anomaly detection system is designed with universal, domain-agnostic algorithms that can detect multiple types of anomalies across different operational contexts. The pattern recognition schemes are formulated to identify fundamental anomaly patterns that manifest across various domains, allowing a limited set of detectors to handle diverse anomaly types without requiring domain-specific customization
Solution Approach 2:
The system adapts to different anomaly types by changing detection parameters and thresholds rather than requiring different detection algorithms. The anomaly processors can adjust sensitivity, time windows, and pattern matching criteria to effectively detect various anomaly types using the same core detection mechanisms, maintaining both versatility and simplicity
Data Source
AI summary
Interactive detection of system anomalies is disclosed. One example is a system including a data processor, an anomaly processor, and an interaction processor. Input data related to a series of events and telemetry measurements is received by the data processor. The anomaly processor detects presence of a system anomaly in the input data, the system anomaly indicative of a rare situation that is distant from a norm of a distribution based on the series of events and telemetry measurements. The interaction processor is communicatively linked to the anomaly processor and to an interactive graphical user interface. The interaction processor displays, via the interactive graphical user interface, an output data stream based on the presence of the system anomaly, receives, from the interactive graphical user interface, feedback data associated with the output data stream, and provides the feedback data to the anomaly processor for operations analytics based on the feedback data.