DAG Feature Assembly Recipes for Error-Prone SQL Transformations
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Solution Overview
Problem
Existing methods for assembling feature data for machine learning and pattern recognition models are time-consuming, computationally intensive, and prone to errors due to the complexity of SQL code revisions and data aggregation, especially when modifying or reusing SQL code for different feature data requirements.
Innovation Solution
The use of YAML files to create directed acyclic graph (DAG) recipes that simplify data transformation and aggregation, allowing for parallel computing architectures like the Dask Python library to reduce computational resources and human effort in assembling feature data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional SQL code methods are used for data aggregation and transformation, then feature data can be assembled, but the process becomes time-consuming and computationally intensive
Solution Approach 1:
The patent segments the feature data assembly process into distinct transformation steps defined in YAML files, where each step can be independently processed. This segmentation enables parallel execution of multiple transformations, improving productivity while reducing overall computational resource usage compared to monolithic SQL code execution.
Solution Approach 2:
The patent implements preliminary action by pre-defining transformation recipes in YAML files that specify the exact sequence of operations needed for feature data assembly. These pre-configured recipes eliminate the need for repeated SQL code writing and debugging, significantly improving assembly speed while reducing computational overhead from redundant processing.
2Adaptability or versatility
If complex SQL code revisions are performed for different feature data requirements, then data transformation can be adapted, but errors increase and maintenance difficulty rises
Solution Approach 1:
The patent uses copying by replicating proven transformation patterns across multiple YAML recipe files. Instead of revising complex SQL code for each new feature requirement, standardized transformation templates are copied and customized with specific parameters, maintaining consistency and reducing errors while improving adaptability to different data requirements.
Solution Approach 2:
The patent implements universality through YAML-based transformation recipes that can handle multiple data types and transformation operations within a unified framework. This universal approach allows the same recipe structure to be applied across different feature data requirements, reducing the need for error-prone custom SQL code while maintaining high flexibility and adaptability.
3Ease of operation
If manual SQL code writing and revision is performed, then data assembly can be customized, but human effort and time requirements increase
Solution Approach 1:
The patent implements self-service by enabling automated generation of feature data assembly code from YAML recipe definitions. The system automatically translates high-level YAML specifications into executable transformation code, eliminating the need for manual SQL code writing and revision. This self-service approach dramatically reduces human effort and time requirements while maintaining ease of operation through intuitive YAML configuration.
Solution Approach 2:
The patent substitutes the mechanical process of manual SQL code writing and debugging with an automated code generation system that translates YAML recipes into executable transformations. This replacement eliminates time-consuming manual coding activities while preserving full customization capability through the YAML configuration interface, significantly improving ease of operation and reducing time loss.
Data Source
AI summary
An example method includes receiving a feature definition that defines desired model feature data. The method further includes receiving transformation information for performing at least one transformation on raw data that generates the desired model feature data. The method further includes receiving source information including a raw data structure definition identifying a data arrangement of the raw data. The method further includes generating a machine learning data assembling software object based at least in part on the feature definition, the transformation information, and the source information. The method further includes generating feature data including the desired model feature data from the raw data based on the machine learning data assembling software object. The generating of the feature data includes retrieving the raw data based on the source information and transforming the raw data based on the transformation information to generate the desired model feature data according to the feature definition.