Multi-Layer Schema Mapping for Distributed Engine Integration
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Solution Overview
Problem
Distributed computing systems face challenges in efficiently configuring and integrating multiple computation engines due to incompatible data formats, complex workflows, and maintaining end-to-end data integrity, leading to inefficiencies and latency.
Innovation Solution
A multi-layer configuration framework with middleware that standardizes data formats, employs schema mapping algorithms, and manages data ingestion pipelines to streamline integration, automate data discovery, and coordinate engine executions, preserving hierarchical relationships and dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple heterogeneous computation engines are integrated in distributed computing systems, then computational capability and functionality are enhanced, but system complexity and integration difficulty increase due to incompatible data formats and structures
Solution Approach 1:
The patent introduces a middleware layer with standardized data schemas and transformation components that act as intermediaries between heterogeneous computation engines. This middleware handles format compatibility and data mapping, allowing diverse engines to communicate through a common interface without direct integration complexity
Solution Approach 2:
The patent implements a universal data schema framework that can accommodate multiple data formats and structures through configurable mapping rules. This universal layer enables the system to handle various computation engine outputs through a standardized interface, reducing integration complexity
2Reliability
If data transformation and schema mapping are performed to ensure compatibility across computation engines, then data integrity is maintained, but computational overhead and processing time increase
Solution Approach 1:
The patent pre-defines data schemas and transformation rules for common data formats before runtime. During execution, the system performs schema matching and data transformation using these pre-configured templates, significantly reducing real-time computational overhead while maintaining data integrity
Solution Approach 2:
The patent dynamically adjusts transformation depth and complexity based on data characteristics and engine requirements. The system identifies essential transformation parameters and applies only necessary conversions, optimizing the balance between data integrity and processing efficiency
3Reliability
If comprehensive data validation and quality assurance mechanisms are implemented, then data quality and compatibility are improved, but system complexity and operational overhead increase
Solution Approach 1:
The patent divides validation and quality assurance into modular components distributed across the middleware layer. Each computation engine interface has dedicated validation rules and transformation validators, preventing monolithic complexity while ensuring comprehensive data quality checks through segmented, manageable units
Data Source
AI summary
A system can receive, from a source application, input data corresponding to a predefined data schema. The system can identify, based on the input data, a plurality of policy configurations, where each policy configuration includes a first layer defining a first subset of policies, a second layer defining a second subset of policies, and a third layer defining data associated with a profile data structure. The system can map the input data to input fields defined within the first layer, the second layer, and the third layer of each policy configuration. The system can select, in response to mapping the input data to the input fields, a policy configuration for a target engine. The system can generate, based on the policy configuration, executable instructions for the target engine. The system can transmit the executable instructions to the target engine to cause the target engine to execute a network operation.