Raw-Data Datastore for Automated Feature Recognition Retraining
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Solution Overview
Problem
Data analysis and feature recognition systems face issues such as the need for manual retraining when evaluation algorithms or target data areas (TDAs) are altered, leading to recognition failures due to algorithm or data confusion, and lack the ability to efficiently adapt to changes in algorithm or TDA configurations.
Innovation Solution
The implementation of a raw-data datastore that abstracts away dependencies on original data sources and processing selections, allowing for the storage of original data values and patterns, enabling 'plug-and-play' functionality with evaluation algorithms and TDAs, and facilitating the identification and correction of data and algorithm confusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual retraining is performed when evaluation algorithms or TDAs are altered, then feature recognition accuracy is maintained, but system productivity and adaptability deteriorate due to time-consuming retraining processes
Solution Approach 1:
The system performs preliminary actions by automatically detecting algorithm or TDA changes and initiating retraining processes before recognition failures occur. The change detection mechanism monitors algorithm configurations and TDA definitions, and when modifications are detected, the system automatically retrains the recognition models using the updated configurations, thereby maintaining accuracy without requiring manual intervention.
Solution Approach 2:
The system serves itself by implementing automated change detection and self-retraining capabilities. When the evaluation algorithm or TDA is modified, the system automatically detects the change, triggers the retraining process, and updates the recognition models without human intervention. This self-service mechanism eliminates the need for manual retraining while maintaining feature recognition accuracy.
2Measurement precision
If evaluation algorithms are modified to improve recognition capability, then measurement precision improves, but system reliability deteriorates due to algorithm or data confusion
Solution Approach 1:
The system implements feedback mechanisms that monitor recognition outcomes and detect confusion between similar features. When algorithm modifications cause recognition errors or confusion, the feedback loop identifies these issues and triggers corrective retraining actions. The system continuously evaluates recognition performance and adjusts the models accordingly, ensuring that improved recognition capability does not compromise system reliability.
Solution Approach 2:
The system applies preliminary anti-action by detecting potential algorithm confusion before it causes recognition failures. The change detection mechanism identifies when algorithm modifications may lead to confusion between similar features, and the system proactively retrains the models to prevent such confusion from occurring, thereby maintaining both precision and reliability.
3Measurement precision
If complete retraining is performed whenever changes occur, then recognition accuracy is maintained, but loss of time increases due to prohibitive retraining costs
Solution Approach 1:
The system applies partial action by performing selective retraining rather than complete retraining. The change detection mechanism identifies specific algorithms or TDAs that have been modified, and the system retrains only the recognition models affected by these changes. This partial retraining approach maintains recognition accuracy for the modified components while avoiding the time cost of retraining the entire system.
Solution Approach 2:
The system segments the retraining process by dividing the recognition model into independent components that can be trained separately. When an evaluation algorithm or TDA is modified, only the corresponding segment of the model is retrained, rather than retraining the entire system. This segmentation strategy significantly reduces retraining time while maintaining overall recognition accuracy.
Data Source
AI summary
A raw-data datastore during data analysis and feature recognition abstracts away and/or reduces dependency upon typically required components of datastore training. The datastore functions to store the original data values of a data set selection, which can represent a known feature. In some embodiments, the original data set is retained as the raw data value set referenced by the raw-data datastore. The use of this raw-data datastore eliminates the need for continued manual retraining of the original data values and patterns, which can be associated with a particular known feature, each time the pluralities of evaluation algorithms and/or the target data area are altered, changed, modified, or reconfigured.