ML Rule Extension for Dynamic Data Constraint Validation
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Solution Overview
Problem
Existing software applications struggle with user-specific and dynamic data constraints that cannot be foreseen by developers, leading to inefficient data entry and cleansing processes due to rigid foreign key relationships and static rule enforcement.
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 static constraints, then data consistency is ensured, but the system cannot adapt to user-specific and dynamic data requirements
Solution Approach 1:
The system transitions from static developer-defined constraints to dynamic user-specific constraints. Machine learning models analyze user behavior patterns and automatically generate adaptive constraints that evolve with usage, allowing the system to maintain data consistency while adapting to individual user needs and dynamic business requirements.
Solution Approach 2:
The system enables users to define their own data constraints without developer intervention. Through ML-powered analysis of user interactions and data patterns, the system automatically generates and enforces constraints that reflect actual user needs, eliminating the need for developers to anticipate all possible data relationship scenarios.
2Reliability
If developers configure all data constraints manually, then complete control over data relations is achieved, but the complexity and time required for configuration increases significantly
Solution Approach 1:
The system performs preliminary analysis of data patterns and user behavior before constraints are needed. Machine learning models continuously learn from data interactions and pre-generate appropriate constraints, so when data entry occurs, the constraints are already in place and ready for enforcement, eliminating configuration delays.
Solution Approach 2:
The system replaces manual mechanical configuration of constraints by developers with an automated intelligence-based system. ML algorithms analyze data relationships and automatically generate constraint rules, substituting the time-consuming manual configuration process with automated intelligent generation that scales with system usage.
3Reliability
If rigid foreign key relationships are enforced, then referential integrity is maintained, but data entry flexibility and user autonomy are reduced
Solution Approach 1:
The system changes the parameters of constraint enforcement from binary (enforced/not enforced) to graduated levels. ML models analyze the nature and severity of constraint violations and apply appropriate response levels, allowing the system to maintain referential integrity for critical relationships while permitting flexibility for less critical data entries based on user context and business rules.
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.