Rule Engine Decouples Data Model From Strict Schema
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Solution Overview
Problem
Current strict schema systems face high coupling dependency on data models, leading to low development efficiency, poor code cohesion, and limited enrichment functions, making it difficult to modify data processing logic and maintain databases as they expand.
Innovation Solution
The method involves translating a data model into transformation rule definitions and matching them with corresponding actions, using a rule engine and action engine to perform extraction, filtering, and enrichment operations, reducing coupling dependency and enhancing flexibility in data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a strict schema system is used to maintain data integrity and structure, then data consistency and reliability are improved, but the coupling dependency on data models increases and development efficiency decreases
Solution Approach 1:
The patent segments the data processing system into distinct components: a rule engine that manages transformation rules separately from the strict schema system. This segmentation allows the schema to enforce data integrity while the rule engine handles flexible data model transformations independently, reducing coupling and improving development efficiency without compromising data consistency.
Solution Approach 2:
The patent introduces a rule engine as an intermediary layer between the data model and the strict schema system. This mediator translates data models into transformation rules that operate on the strict schema data, allowing flexible data processing logic to be implemented without directly modifying the rigid schema structure, thus maintaining both reliability and productivity.
2Reliability
If the strict schema system enforces rigid data structure rules, then data integrity is maintained, but code cohesion deteriorates and the system becomes difficult to modify
Solution Approach 1:
The patent divides the system into separate modules: the strict schema engine that enforces data integrity rules and the rule engine that contains transformation logic. This segmentation improves code cohesion by placing related functionality together and reduces complexity by allowing independent modification of each module without affecting the other.
Solution Approach 2:
The patent makes the transformation logic dynamic by using a configurable rule engine that can adapt to different data models without requiring changes to the strict schema structure. Rules can be added, modified, or removed flexibly, enabling the system to evolve while maintaining data integrity constraints.
3Reliability
If the data processing logic is tightly coupled with the strict schema system, then data consistency is ensured, but flexibility and ease of modification are reduced
Solution Approach 1:
The rule engine serves as an intermediary that decouples data processing logic from the strict schema system. It translates high-level data model requirements into specific transformation operations that work with the strict schema, ensuring data consistency while providing flexibility in how transformations are defined and modified.
Solution Approach 2:
The rule engine provides universal functionality by handling multiple types of data transformations through a unified rule-based approach. It can process different data models and apply various transformation strategies without requiring schema changes, enhancing adaptability while maintaining consistency with the strict schema system.
Data Source
AI summary
A system may port a data model into a strict schema system, translate the data model into a transformation rule definition, fit the transformation rule definition to a transform action, receive strict schema data, perform the transform action on the strict schema data based on the transformation rule definition to form rough data, and execute filtering and enriching operations on the rough data to form loose schema data.