Automated Data Facet Generation for ML Transformation
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Solution Overview
Problem
Data preparation for machine learning tasks is a complex and time-consuming process requiring deep understanding of data nature, business application, and domain knowledge, with data scientists spending up to 80% of their time finding and preparing data and researching algorithms, highlighting the need for automated data transformation recommendations.
Innovation Solution
A method and system for automatically identifying data facets and generating data transformations, which include receiving data, associating elements with data types, generating data facets, and recommending optimal transformations and algorithms based on user profiles, business metadata, and community knowledge, providing generated computer code for data transformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual data preparation and transformation selection is performed by data scientists, then data quality and model performance are improved, but time consumption and labor effort increase significantly
Solution Approach 1:
The system enables automated self-service for data transformation selection and generation. The data transformation recommendation module automatically analyzes dataset characteristics, identifies appropriate transformations, and generates executable code without requiring manual intervention from data scientists, thus reducing time consumption while maintaining data quality through algorithmic selection criteria
Solution Approach 2:
The patent introduces an intermediary automated recommendation system between the raw data and the machine learning model. This intermediary module acts as a mediator that automatically performs data transformation selection and code generation, bridging the gap between manual preparation and automated processing while maintaining quality standards
2Reliability
If comprehensive data transformation options are explored to find optimal transformations, then model performance is improved, but system complexity and computational overhead increase
Solution Approach 1:
The system segments the complex task of data transformation selection into distinct modular components: dataset analysis module, transformation recommendation module, and code generation module. Each module handles a specific aspect of the process, reducing overall system complexity while comprehensively exploring transformation options through structured segmentation of the decision-making process
Solution Approach 2:
The recommendation system is designed as a universal multi-functional platform that can handle various data types, transformation categories, and machine learning tasks through a single integrated system. The system universally applies transformation recommendations across different datasets and scenarios, reducing the need for multiple specialized tools and simplifying the overall system architecture
3Productivity
If automated data transformation recommendation system is implemented, then productivity and efficiency are improved, but initial system setup and implementation complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-analyzing dataset characteristics and pre-generating transformation recommendations before actual data processing begins. The recommendation module prepares transformation code templates and selection criteria in advance, enabling rapid data preparation execution without requiring complex real-time decision-making, thus improving productivity while managing implementation complexity through upfront preparation
Data Source
AI summary
A method, computer program, and computer system are provided for data facet generation. Data associated with a dataset is received. The received data includes one or more data entries having one or more elements. The one or more elements are associated with one or more data types. One or more data facets are generated for each of the data entries with the received data based on the associated data type. One or more transformations are generated for the data facet corresponding to a machine learning task associated with the dataset. A recommendation is provided to a user based on the generated transformation. The provided recommendation includes generated computer code corresponding to an optimal transformation associated with the machine learning task.