Edge Anomaly Detection for Data Format Transfer Bottlenecks
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Solution Overview
Problem
Existing anomaly detection systems rely on centralized processing, leading to latency, inefficiencies, and increased probability of anomalous data due to single points of failure, which reduces data integrity and security, and manual intervention introduces human error and resource inefficiencies.
Innovation Solution
Implementing edge computing for decentralized anomaly detection and resolution across devices, using data pattern recognition and historical data comparisons to identify and address anomalous data before, during, and after each operation, leveraging machine learning algorithms to generate compatible data structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized processing is used for anomaly detection, then data integrity can be maintained through centralized control, but latency increases and efficiency decreases
Solution Approach 1:
The patent segments the centralized anomaly detection system into distributed edge computing nodes deployed across multiple devices in the data transfer path. Each edge node independently performs anomaly detection on local data, eliminating the need to centralize all processing while maintaining detection capability through distributed intelligence.
Solution Approach 2:
The patent transitions from a single-dimensional centralized processing architecture to a multi-dimensional distributed architecture where anomaly detection occurs across multiple spatial locations (edge devices) and temporal points (before, during, and after operations). This dimensional expansion reduces latency by performing detection closer to the data source.
2Measurement precision
If centralized processing is used for anomaly detection, then comprehensive data analysis can be performed, but inefficiencies and single points of failure increase
Solution Approach 1:
The patent divides the centralized processing function into multiple independent edge computing nodes distributed across the data transfer infrastructure. Each node performs anomaly detection locally, eliminating the single point of failure while collectively maintaining comprehensive detection coverage through their distributed positions.
Solution Approach 2:
The patent changes the operational parameters of anomaly detection by enabling multiple detection instances across different devices rather than a single centralized instance. This parameter change from centralized to distributed operation improves system reliability while maintaining detection accuracy through collective intelligence.
3Adaptability or versatility
If manual user intervention is used to monitor and check for anomalies, then flexibility in handling complex anomalies can be achieved, but human error and response time increase
Solution Approach 1:
The patent implements self-service anomaly detection through automated edge computing nodes that continuously monitor and detect anomalies without human intervention. The system autonomously identifies and flags anomalous data, eliminating the need for manual monitoring while maintaining adaptability through sophisticated detection algorithms.
Solution Approach 2:
The patent replaces the mechanical system of manual human monitoring with an automated computational system based on edge computing and machine learning algorithms. This substitution eliminates human error and reduces response time while maintaining the flexibility to handle complex anomalies through advanced detection capabilities.
4Productivity
If anomalous data is not detected timely and allowed to propagate, then data transfer continuity is maintained, but resource utilization efficiency decreases
Solution Approach 1:
The patent performs preliminary anomaly detection at the edge devices before data is fully processed and propagated through the system. By detecting and addressing anomalies early in the data transfer process, the system prevents wasteful propagation of erroneous data, saving computational resources downstream while maintaining data transfer continuity for valid data.
Solution Approach 2:
The patent extracts and removes anomalous data from the data stream at the edge devices where it is detected, preventing its propagation through the rest of the system. This extraction of harmful elements (anomalies) protects downstream resources from wasting computational effort processing invalid data while maintaining throughput for legitimate data.
Data Source
AI summary
A system for detecting and addressing anomalous data is disclosed. The system receives a first application data from a source device. The system receives a request to perform an operation on the first application data. In response to receiving the request, the system generates a second application data by performing the operation on the first application data. The operation includes transferring the first application data from a first data format to a second data format. The system determines that a portion of the second application data is anomalous. The system compares the second application data with a historical application data that is associated with a solution instruction. Based on the comparison, the system determines that the second application data corresponds to the historical application data. In response, the system executes the solution instruction to address the anomalous portion of the second application data.