ML-Generated Rule UI Plug-In for Dynamic Data Constraints
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Solution Overview
Problem
Existing software applications struggle with user-specific, dynamic data constraints that cannot be foreseen by developers, leading to inefficient data cleansing processes and cumbersome user interactions due to static foreign key relationships and hard-to-configure constraints.
Innovation Solution
Implementing a machine-learning (ML) system to automatically determine and extend rules for data relations and constraints, allowing users to interactively configure and validate data entries through a user interface (UI) with ML-generated recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If developers define strict foreign key relationships and constraints in the data model, then data consistency and reliability are improved, but user flexibility and adaptability to dynamic constraints are worsened
Solution Approach 1:
The patent implements dynamic constraints that can change over time based on user needs and data characteristics. The maintenance UI allows users to modify constraints dynamically without requiring developer intervention, enabling the system to adapt to evolving requirements while maintaining data consistency through validated rule-based constraints.
Solution Approach 2:
The system enables users to independently configure and modify data constraints through the maintenance UI without requiring developer involvement. Users can add, remove, and adjust constraints on their own, making the system self-serviceable for constraint management while preserving data integrity through the underlying rule validation mechanism.
2Reliability
If developers configure all data constraints and relationships, then data model reliability is improved, but system complexity and configuration effort are worsened
Solution Approach 1:
The maintenance UI empowers users to independently manage data constraints without requiring developer configuration for every constraint. Users can add, modify, and remove constraints through the UI, eliminating the need for developers to pre-configure all possible constraints and reducing overall system configuration complexity.
Solution Approach 2:
The system allows dynamic modification of constraint parameters through the maintenance UI. Users can change constraint values, relationships, and configurations without altering the underlying data model structure, enabling flexible parameter adjustment while maintaining model reliability through validated changes.
3Manufacturing precision
If strict data constraints are enforced, then data quality and manufacturing precision are improved, but ease of operation and user productivity are worsened
Solution Approach 1:
The maintenance UI allows users to independently manage and adjust data constraints without requiring developer intervention. Users can modify constraints to match their operational needs while the system automatically validates changes to maintain data quality, enabling self-service constraint management that preserves both precision and ease of operation.
Solution Approach 2:
The system implements dynamic constraint enforcement that can be adjusted by users through the maintenance UI. Constraints are enforced with precision but can be modified dynamically based on operational context, allowing the system to maintain data quality while adapting to different user scenarios and improving ease of operation.
4Manufacturing precision
If asynchronous batch data cleansing processes are used, then data quality is improved, but loss of time and processing efficiency are worsened
Solution Approach 1:
The system performs data validation and constraint checking in advance during data entry through the maintenance UI, rather than waiting for asynchronous batch processing. Users receive immediate feedback on constraint violations and can correct issues before data is committed, preventing quality problems rather than fixing them later and eliminating the need for time-consuming batch cleansing processes.
Solution Approach 2:
The maintenance UI provides real-time feedback to users about constraint violations and data quality issues during data entry. This immediate feedback allows users to correct problems on the spot rather than waiting for asynchronous batch processing, significantly reducing the time loss associated with delayed data quality validation and cleansing.
Data Source
AI summary
In an implementation, one or more rules associated with a DO from a rules database is read by a rule user interface (UI) plug-in associated with a data object (DO) maintenance UI. The one or more rules for the DO to fields associated with the DO on the DO maintenance UI are related by the rule UI plug-in. The rule UI plug-in, using the related one or more rules, auto-populates and validates received values for the fields associated with the DO on the DO maintenance UI. The rule UI plug-in determines that one or more violations of the one or more rules has occurred and displays an additional UI with mutually exclusive options for mitigating the determined one or more violations of the one or more rules. A new rule is saved into the rules database.