N-Layer Data Dependency Sharing for Faster Decision Service Loading
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Solution Overview
Problem
Existing computing systems face inefficiencies due to repeated loading of the same or similar data across multiple layers in N-layer architectures, leading to increased computational burden and prolonged processing times.
Innovation Solution
An intelligent data processor is utilized to identify and optimize data dependencies by intelligently managing data flows and modifying API specifications, ensuring data is propagated efficiently across layers without redundant loading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is repeatedly loaded during data processing by different computing services, then data availability is ensured, but computing system burden increases and processing efficiency decreases
Solution Approach 1:
The system performs preliminary identification of data dependencies across N-layer architecture before data processing occurs. By analyzing which data will be needed by multiple computing services in advance, the system prepares optimization strategies such as data caching or sharing mechanisms, thereby ensuring data availability while avoiding repeated loading during actual processing.
Solution Approach 2:
The patent introduces an intermediary data dependency analysis mechanism that sits between the data storage layer and computing services. This intermediary analyzes data access patterns and coordinates data sharing across layers, acting as a mediator that ensures data availability to multiple services without requiring each service to independently load the same data, thus reducing computing system burden.
2Adaptability or versatility
If data is loaded at each layer independently, then data access autonomy is maintained, but redundant data loading occurs and system resources are wasted
Solution Approach 1:
The patent implements a universal data dependency analysis framework that serves multiple functions across the N-layer architecture. This framework can be applied at different layers (data layer, service layer, application layer) and serves both analysis and optimization purposes, enabling data access autonomy while coordinating resource usage across the entire system to eliminate redundant loading.
Solution Approach 2:
The system incorporates feedback mechanisms where data dependency analysis results from one layer inform data loading decisions at other layers. When a computing service loads data, this information is fed back to the data dependency analysis component, which then adjusts data sharing strategies across the architecture, maintaining autonomous access while optimizing resource utilization through coordinated feedback.
3Ease of manufacture
If traditional data loading methods are used without dependency analysis, then implementation simplicity is maintained, but end-to-end execution time increases due to duplicate data calls
Solution Approach 1:
The patent segments the data processing architecture into distinct layers (data layer, service layer, application layer) with clear responsibility boundaries. The data dependency analysis is implemented as a separate, modular component that can be integrated without fundamentally redesigning existing systems, maintaining implementation simplicity while enabling execution time optimization through targeted dependency management.
Data Source
AI summary
There are provided systems and methods for managing data dependencies in an N-layer architecture for data loading optimizations. A service provider, such as an electronic transaction processor for digital transactions, may utilize different decision services that implement rules and/or artificial intelligence models for decision-making of data including data in production computing environment. Decision services may be used for data processing and decision-making, where multiple decision services may be invoked during run-time in order to complete a data processing request. When processing data, data loads may be required by decision services, where multiple data loads that are the same or similar may be utilized by different data services. Thus, the service provider may provide data loading optimization by making these data loads available across multiple decision services. This may be done based on an intelligent and/or algorithmic process based on data storage requirements.