Confidence Threshold Adjustment for Data Field Mapping
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Solution Overview
Problem
Existing application integration management techniques lack efficient methods for optimizing workflow integration by adjusting confidence level thresholds during application integration sessions, leading to suboptimal data field mapping and potential errors.
Innovation Solution
A computer-implemented method using a machine learning mapping recommendation model to automatically map data fields based on a confidence level threshold, allowing user input adjustments to derive candidate default threshold values and update the confidence level threshold for improved mapping precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic data field mapping is performed with a default confidence level threshold, then mapping productivity is improved, but mapping precision deteriorates due to false positives
Solution Approach 1:
The system collects user feedback on automatic mapping results through true positive/false positive labels, processes this feedback to derive adjusted confidence level threshold values, and applies these adjusted thresholds in subsequent mapping operations. This closed-loop feedback mechanism enables the system to learn from errors and continuously improve mapping precision while maintaining automated efficiency.
Solution Approach 2:
The system dynamically adjusts the confidence level threshold parameter based on feedback analysis. Instead of using a fixed default threshold, the system derives adjusted threshold values that optimize the balance between mapping productivity and precision. This parameter adaptation allows the system to reduce false positives while maintaining high mapping throughput.
2Measurement precision
If user feedback processing is implemented to adjust confidence level thresholds, then mapping precision is improved, but system complexity increases
Solution Approach 1:
The system automatically processes user feedback and derives adjusted confidence level threshold values without requiring manual intervention or complex configuration. The feedback processing mechanism operates autonomously, analyzing true positive/false positive patterns and self-adjusting the threshold parameter. This self-service approach improves precision while minimizing the operational complexity burden on users.
Solution Approach 2:
The system pre-processes and analyzes user feedback during integration sessions to derive adjusted confidence level threshold values that will be applied in subsequent mapping operations. By performing this analysis in advance and preparing optimized thresholds beforehand, the system reduces the computational and operational complexity during actual mapping execution.
3Reliability
If confidence level threshold is adjusted based on user feedback, then false positives are reduced, but processing time increases
Solution Approach 1:
The system applies adjusted confidence level thresholds selectively based on the analysis of user feedback patterns. Instead of reprocessing all mappings or performing exhaustive analysis, the system focuses on deriving threshold adjustments from representative feedback samples and applies these adjustments to reduce false positives in subsequent operations. This partial action approach maintains reliability while minimizing additional processing time.
Data Source
AI summary
Techniques are described with regard to application integration management. An associated computer-implemented method includes receiving from at least one user a request to map a plurality of data fields associated with a workflow during an application integration session and automatically mapping all data fields among the plurality of data fields for which a mapping confidence value is greater than or equal to a confidence level threshold set at a default value. The method further includes receiving mapping evaluations from each of the at least one user for each automatic data field mapping based upon the confidence level threshold set at the default value, processing at least one confidence level threshold adjustment input selection from one or more of the at least one user, and deriving at least one candidate default confidence level threshold value based upon the at least one confidence level threshold adjustment input selection.