Anomaly Detection in Data Pipelines via Regression Analysis
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Solution Overview
Problem
Current methods fail to accurately detect anomalies in heterogeneous data pipelines within data centers or cloud infrastructure without requiring extensive knowledge of data sources, intensive computational power, and struggle to capture data seasonality and trends effectively.
Innovation Solution
A system utilizing a combination of FPGA computing systems, machine learning techniques, and data processing frameworks like Apache Spark Streaming to analyze data pipelines, identifying anomalies by training predictive models and calculating breakpoints, which allows for early and accurate anomaly detection across diverse data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning techniques are used to detect anomalies in heterogeneous data pipelines, then detection accuracy improves, but computational power requirements increase
Solution Approach 1:
The patent segments the data pipeline monitoring into multiple independent anomaly detectors, each responsible for specific data sources or pipeline stages. This segmentation allows distributed processing that reduces the computational burden on any single system while maintaining comprehensive monitoring coverage across heterogeneous data sources.
Solution Approach 2:
The patent introduces intermediary components that preprocess and aggregate data before feeding it to machine learning models. These intermediaries include data normalization layers, feature extraction modules, and caching mechanisms that reduce the complexity and volume of data requiring intensive computational processing.
2Reliability
If comprehensive monitoring of all computing technologies and systems is implemented, then detection coverage improves, but system complexity increases
Solution Approach 1:
The patent implements universal anomaly detectors that can handle multiple types of data sources and pipeline configurations through standardized interfaces. These detectors are designed to be multi-functional, adapting to different data formats and pipeline architectures without requiring separate specialized monitoring systems for each technology.
Solution Approach 2:
The patent employs configurable parameters and adaptive thresholds that allow the monitoring system to adjust its behavior based on the specific characteristics of each data source and pipeline stage. This parameter-driven approach enables comprehensive monitoring while maintaining manageable system complexity through centralized configuration rather than hard-coded complexity.
3Measurement precision
If rule-based anomaly detection with defined thresholds is used, then false positives are reduced, but ability to capture seasonality and trends decreases
Solution Approach 1:
The patent implements dynamic threshold adjustment mechanisms that adapt to seasonal patterns and trends in the data. Rather than using fixed thresholds, the system learns and adapts to normal variations in data behavior over time, allowing it to maintain low false positive rates while remaining sensitive to actual anomalies even when seasonality and trends are present.
Solution Approach 2:
The patent incorporates feedback loops where detection results and performance metrics are continuously fed back into the system to refine anomaly detection parameters. This feedback mechanism allows the system to learn from false positives and negatives, progressively improving its ability to distinguish between normal seasonal variations and actual anomalies while maintaining precision.
Data Source
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AI summary
A method and system for detecting anomalies in a data pipeline are disclosed. Data tables are extracted from data pipelines. A best time column is calculated for each data table. Table lines are aggregated so as to establish counts of data lines per a time interval, and corresponding sequences of points are produced that are being analysed through regression methods. An anomaly is raised when points have counts of data lines that lie outside confidence intervals around expected values for such counts.