Dynamic Transformation Code Prediction for Missing Data Elements
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Solution Overview
Problem
Missing data, including fields with junk or unexpected data, poses challenges in software development and quality assurance processes, making it difficult to trace sources and regenerate information, leading to data aggregation and processing issues.
Innovation Solution
A computing platform executes an extract, transform, and load process, detects missing data, generates a prediction model using machine learning algorithms, and determines a confidence level to generate transformation code, with feedback from subject matter experts to refine the model, ensuring accurate prediction and code generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual code changes are made to produce missing data, then data completeness is improved, but development time and testing requirements increase
Solution Approach 1:
The system performs preliminary actions by training machine learning models on historical data beforehand, so that when missing data is detected, the pre-trained model can immediately generate predictions without requiring manual code changes or extensive testing cycles
Solution Approach 2:
The system enables self-service by allowing the machine learning model to automatically generate and execute transformation code for data imputation without requiring manual intervention, thus reducing both development time and testing requirements while maintaining data completeness
2Productivity
If machine learning algorithms are used to predict missing data, then data processing efficiency is improved, but system complexity increases
Solution Approach 1:
The system segments the complex task of missing data handling into distinct phases: data analysis phase, model training phase, prediction phase, and code generation phase. Each phase uses specific machine learning algorithms appropriately, reducing overall system complexity while maintaining high processing efficiency
Solution Approach 2:
The system introduces an intermediary layer consisting of pre-trained machine learning models that act as mediators between the raw data and the code generation process. This intermediary handles the complexity of pattern recognition and prediction, allowing the rest of the system to remain relatively simple while achieving high productivity
3Reliability
If records with missing data are skipped from processing, then data processing issues are avoided, but data aggregation accuracy deteriorates
Solution Approach 1:
The system converts the harmful effect of missing data into a benefit by using the missing data patterns as training examples for machine learning models. The model learns from historical missing data scenarios and transforms them into accurate predictions, thereby improving data aggregation accuracy while maintaining processing reliability
Data Source
AI summary
Aspects of the disclosure relate to dynamic transformation code prediction and generation for an unavailable data element. In some embodiments, a computing platform may execute an extract, transform, and load process on input data received from a plurality of data sources and detect a missing data element in the received input data. Subsequently, the computing platform may generate a prediction model with respect to the missing data element, which may include executing a first, second, and third machine learning algorithm. Next, the computing platform may determine a confidence level of the prediction model. In response to determining that the confidence level is at or above the predetermined threshold, the computing platform may generate executable transformation code implementing the prediction model. Thereafter, the computing platform may monitor transformation code implementations and execute a fourth machine learning algorithm to adjust the prediction model based on the transformation code implementations.