Ontology Rule Guard for Multi-Country Data Integrity
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Solution Overview
Problem
Existing business application architectures face challenges with scattered and overlapping business rules, complex rule management, and inefficient performance due to rigid schemas and static rule enforcement, leading to software defects and difficulty in adapting to country and client-specific requirements.
Innovation Solution
A rule guard system that utilizes ontologies and metadata to dynamically generate and enforce context-sensitive rules, allowing for scalable, customizable, and high-throughput data processing by integrating an ontology-driven approach with a fluid database that adapts to organizational and regulatory needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rigid schemas and static rule enforcement are used, then data integrity is maintained, but adaptability to country and client-specific requirements deteriorates
Solution Approach 1:
The patent implements dynamic schema evolution where the fluid database schema automatically adapts to country and client-specific requirements through ontology-driven migrations. The system transitions from rigid static schemas to dynamic schemas that can evolve over time while maintaining data integrity through controlled transformations and validations.
Solution Approach 2:
The system changes schema parameters dynamically by applying ontology-based transformations that modify database structure according to contextual requirements. Country-specific and client-specific parameters are incorporated through configurable ontology instances that adjust field definitions, data types, and relationships without requiring manual code changes.
2Ease of operation
If scattered and overlapping business rules are used, then implementation flexibility is maintained, but rule management complexity increases
Solution Approach 1:
The patent merges scattered and overlapping business rules into a unified ontology-based rule management system. Rules are consolidated within the fluid database schema and enforced through a centralized rule engine, eliminating duplication and reducing management complexity while preserving implementation flexibility through configurable rule application.
Solution Approach 2:
The system creates a universal rule enforcement mechanism that handles multiple business rules through a single ontology-driven framework. The rule engine can enforce various types of rules (validation, transformation, compliance) using the same underlying infrastructure, reducing management complexity while maintaining flexibility.
3Adaptability or versatility
If manual code writing and extensive developer intervention are used, then customizations can be implemented, but productivity decreases
Solution Approach 1:
The system implements self-service through automated ontology-driven schema generation and rule enforcement. The fluid database automatically generates and updates its schema based on ontology definitions, and the rule engine automatically enforces business rules without requiring manual code writing or extensive developer intervention, thereby increasing productivity while maintaining customization capability.
Solution Approach 2:
The patent replaces manual mechanical code writing with automated ontology-based generation. Schema definitions and business rules are expressed in ontologies that are automatically translated into database structures and enforcement logic, eliminating repetitive manual coding tasks and significantly improving development efficiency.
4Productivity
If static rule enforcement is used, then system performance is maintained, but ability to respond to changing requirements deteriorates
Solution Approach 1:
The system transitions from static to dynamic rule enforcement where the fluid database schema and business rules can evolve automatically based on changing requirements. The ontology-driven approach enables performance-optimized rule enforcement that adapts to new country and client-specific requirements without requiring system rewrites, maintaining high performance while improving adaptability.
Data Source
AI summary
A payload including data may be received that is to be written to any of the entities included in an application database schema, where the application database schema governs storage of application data in a fluid database for a software application. A context for the payload is identified, where the context identifies an entity that is to be updated with the data in the payload. The context may include an attribute identifying a country and/or a client. Rules stored in a rule registry are searched and a set of rules is retrieved that matches the context for the payload. The rules are segregated according to context levels and are associated with the entities. The rules stored in the rule registry include logic executable to enforce the rules. The retrieved set of rules are enforced, in the payload received, by execution of the logic included in the set of rules retrieved.