AR Database Modeling With ML Entity Extraction and Feedback
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Solution Overview
Problem
Creating a database model has become tedious and time-consuming due to increasing data dependency issues in large organizations, necessitating an intelligent database modelling system.
Innovation Solution
A system utilizing augmented reality and machine learning to assist users in designing database models by capturing user drawings, suggesting entities and attributes, and generating conceptual and physical database models through machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual database modelling is used, then complete control over data structure is maintained, but the process becomes tedious and time-consuming
Solution Approach 1:
The system enables self-service by having the machine learning model automatically analyze user drawings, extract entities and relationships, and generate database models without requiring extensive manual configuration. The system serves itself by interpreting the user's sketch and autonomously creating the complete database structure.
Solution Approach 2:
The patent replaces the mechanical manual process of creating database models with an automated machine learning-based system. Instead of manually defining all database structures, users simply draw concepts, and the ML system substitutes the complex mechanical work of model generation, extraction, and transformation.
2Loss of time
If manual database modelling is performed, then accuracy and control are maintained, but time consumption increases
Solution Approach 1:
The system incorporates feedback mechanisms where the machine learning model processes user drawings, generates database models, and allows users to review and provide feedback. The model can be retrained or adjusted based on user feedback to improve accuracy while maintaining speed.
Solution Approach 2:
The system performs preliminary action by automatically extracting entities and relationships from user drawings before generating the final database model. This preliminary processing of the drawing data enables rapid model generation while maintaining accuracy through automated analysis.
3Productivity
If automated database modelling is implemented, then productivity increases, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary machine learning model that mediates between the simple user drawing input and the complex database model generation. This intermediary layer simplifies the overall system architecture by providing a clear separation: users interact with simple drawing tools, the ML model processes this into structured data, and the database model is generated automatically.
Data Source
AI summary
Systems, computer program products, and methods are described herein for intelligent database modelling. The present invention is configured to capture, using the augmented reality application, a drawing made by the user on a medium; initiate a first machine learning model on the drawing; determine that the user is attempting to design a database model; extract at least a first entity from the drawing; determine, using the first machine learning model, one or more attributes for the first entity; initiate, via the augmented reality application, a first push notification for display on the computing device of the user; electronically receive, via the augmented reality application, a user selection of a subset of attributes from the one or more attributes; generate, using the augmented reality application, a conceptual database model based on at least the first entity and the subset of attributes.