Edge Server Data Correctness Filter with Automatic Rule Updates
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Solution Overview
Problem
There are challenges in ensuring data correctness and security, particularly with the increased use of 5G and IoT devices, where incorrect data can lead to security threats and incorrect decision-making.
Innovation Solution
The method involves identifying incorrectness bias in data received at an edge server through a filter with filter rules, performing corrective measures, and automatically updating the filter rules to reduce latency in future data validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If filter rules are manually configured and updated, then data validation accuracy is maintained, but system complexity and update latency increase
Solution Approach 1:
The filter rules are automatically updated by the edge server itself based on detected incorrectness biases, eliminating the need for manual configuration. The system monitors its own performance and self-adjusts the filter rules to reduce latency while maintaining validation accuracy.
Solution Approach 2:
The system implements a feedback mechanism where detected incorrectness biases are used to automatically update filter rules. The edge server monitors data validation results and uses this feedback to refine future filtering decisions, creating a continuous improvement loop.
2Measurement precision
If comprehensive filter rules are applied to detect all types of incorrectness, then detection accuracy improves, but processing time and latency increase
Solution Approach 1:
The filter rules dynamically adapt based on detected incorrectness biases. Instead of using static comprehensive rules, the system adjusts the filtering strategy in real-time based on the specific patterns of incorrectness detected in the data stream, optimizing the balance between detection accuracy and processing speed.
Solution Approach 2:
The system changes parameters of the filter rules based on the extent and spread of detected incorrectness biases. When certain types of incorrectness are detected, the system modifies filter parameters to prioritize detection of those specific patterns, reducing overall processing time while maintaining accuracy for the most critical validation tasks.
3Reliability
If real-time data validation is performed, then data integrity is maintained, but computational resources and processing load increase
Solution Approach 1:
The system applies partial validation actions based on the detected incorrectness biases. Instead of validating all data points with full computational intensity, the system focuses computational resources on validating data patterns that show signs of incorrectness, reducing overall processing load while maintaining data integrity for critical validations.
Solution Approach 2:
The system extracts and focuses validation efforts on the most critical data validation tasks. By identifying and isolating the specific incorrectness patterns that need attention, the system can apply intensive validation only where needed rather than uniformly across all data, reducing computational burden.
Data Source
AI summary
A method, system and apparatus for providing edge data correctness and correctness spread determination, including identifying an incorrectness bias in data received at an edge server of a network through a filter with filter rules, performing corrective measures to the data according to the incorrectness bias that is identified, and learning and automatically updating the filter rules of the filter based on an extent of the incorrectness bias of the data and spread of the incorrectness bias of the data to reduce latency in future data validation of the data.