Visual Data Merge Pipelines With ML-Guided Transformations
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Solution Overview
Problem
Combining data from multiple sources is resource-intensive, as it requires designing and deploying microservices that consume power and processing resources, and hard-coded transformations often reduce the accuracy and usefulness of the combined data.
Innovation Solution
A system for visually building data merge pipelines using existing hardware and software resources, allowing users to drag and drop data sources and output endpoints, and applying machine learning models to recommend transformations, thereby conserving resources and improving data accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If microservices are designed and deployed to combine data from multiple sources, then data integration capability is improved, but power and processing resources are consumed
Solution Approach 1:
The patent uses templates to copy proven data integration patterns and transformations. Instead of designing custom microservices for each integration task, the system replicates successful integration templates that have already been optimized for resource efficiency, allowing rapid deployment without consuming additional resources for design and testing
Solution Approach 2:
The system dynamically adjusts transformation parameters based on data characteristics and resource availability. Machine learning models analyze data patterns and automatically optimize transformation parameters to achieve effective data integration while minimizing processing resource consumption and power usage
2Extent of automation
If hard-coded transformations are applied to combined data, then data processing is automated, but accuracy and usefulness of the transformed data are reduced
Solution Approach 1:
The patent implements feedback loops where machine learning models continuously learn from transformation results and data quality metrics. The system monitors transformed data accuracy and automatically adjusts transformation logic based on performance feedback, enabling automated processing that improves over time while maintaining high accuracy
Solution Approach 2:
The transformation logic transitions from static hard-coded rules to dynamic machine learning-based transformations. The system adapts transformation parameters and logic in real-time based on data characteristics, ensuring high accuracy while maintaining full automation through self-learning models that evolve with incoming data patterns
Data Source
AI summary
In some implementations, a data merger may receive a configuration associated with a first data source. The data merger may receive a configuration associated with a second data source. The data merger may receive a configuration associated with a first output endpoint. The data merger may receive an indication of a first transformation to apply to first data received from the first data source and second data received from the second source, such that the first output endpoint transmits a combination of the first data and the second data after application of the first transformation. The data merger may provide the first data, received from the first data source, to a machine learning model and receive an indication of a second transformation recommended by the machine learning model. The data merger may transmit the indication of the second transformation.