Machine Learning Models for Automated Data Processing Recommendations
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Solution Overview
Problem
The task of determining appropriate data processing actions for datasets is time-consuming and prone to errors due to reliance on personal user experience, especially when different domains require specific rule sets for data cleansing and standardization.
Innovation Solution
A method utilizing machine learning models to determine and recommend data processing actions based on dataset features, with multiple models providing scores for confidence, allowing for efficient processing by selecting actions with high relevance and avoiding unnecessary resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data processing actions are determined based on personal user experience and manual rule sets, then data quality improvement can be achieved, but the process becomes time-consuming and prone to errors
Solution Approach 1:
The system enables self-service by allowing the data processing system to automatically determine appropriate processing actions through machine learning models, eliminating the need for manual expert analysis. The models analyze dataset features and autonomously select processing actions, making the system self-sufficient while improving both speed and accuracy.
Solution Approach 2:
The patent replaces the mechanical system of manual expert analysis with an automated machine learning-based system. Instead of relying on human experts to manually determine processing actions, the system uses trained models that automatically analyze dataset characteristics and recommend appropriate actions, significantly reducing time while maintaining or improving accuracy.
2Reliability
If multiple machine learning models are used to determine data processing actions, then accuracy and reliability improve, but system complexity increases
Solution Approach 1:
The patent merges multiple machine learning models into a unified recommendation system. Instead of using models in isolation, the system combines their outputs through a recommendation module that aggregates predictions and selects the most appropriate processing actions. This merging approach maintains high reliability while managing system complexity through integrated architecture.
Solution Approach 2:
The recommendation system serves as a universal component that handles outputs from multiple different machine learning models. This multi-functional module can process recommendations from various model types (classification, regression, clustering) and translate them into coherent data processing actions, reducing overall system complexity by creating a single point of integration.
3Manufacturing precision
If data processing actions are customized for different domains, then data quality improvement is enhanced, but the time required to determine appropriate rule sets increases
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models on domain-specific data characteristics before actual data processing occurs. During the modeling phase, the system learns domain-specific patterns, relationships, and appropriate processing actions. When new data arrives, the pre-trained models immediately apply this learned knowledge without requiring time-consuming domain analysis, thus maintaining high standardization quality while reducing processing time.
Solution Approach 2:
The patent implements parameter changes by adapting model behavior based on domain-specific features of the input data. The system dynamically adjusts processing parameters and selection criteria according to the detected domain characteristics, allowing customized data quality improvement for each domain without manual rule set selection. The models automatically modify their processing approach based on data parameters such as data type, source, and structure.
Data Source
AI summary
A method, computer system, and computer program product for providing recommendations about processing datasets. A set of machine learning models are provided for use in respectively determining data processing action performable on a dataset based on a respective set of features of the dataset. A current dataset is received. A set of features of the current dataset are determined. One or more data processing actions are generated to be executed on the current dataset, which are determined by at least two machine learning models of the provided set, based on the determined set of features of the current dataset. One or more of the data processing actions are performed on the current dataset.