Rule Variable Weight Mapping for Faster Decision Processing
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Solution Overview
Problem
Existing data processing rules often consume unnecessary processing resources and time due to inefficient vocabulary and grammar usage during rule construction, leading to suboptimal decision-making in computing services.
Innovation Solution
A variable mapping system is implemented to determine processing weights for rule variables, correlating similar variables and optimizing their selection for rule authoring, using AI and machine learning to analyze and map variables based on data loading costs and resources, thereby reducing processing loads and improving resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If different variables are used for similar data operations in rule construction, then rule authoring flexibility is improved, but processing load and time increase
Solution Approach 1:
The system assigns different weight parameters to variables based on their processing characteristics. The variable weight determination module calculates weights considering factors like data source, transformation complexity, and access frequency. This allows the system to quantify and optimize variable selection, replacing high-weight variables with equivalent low-weight alternatives to reduce processing time while maintaining rule functionality.
Solution Approach 2:
The system creates variable mappings where multiple variables are grouped into equivalence classes based on their functional similarity. Instead of treating each variable as unique, the system copies the functional behavior across mapped variables and selects the most efficient representative from each class, reducing processing overhead while preserving rule authoring flexibility.
2Adaptability or versatility
If multiple variables with different processing characteristics are used, then rule functionality is improved, but processing resources are wasted
Solution Approach 1:
The system introduces weight parameters that reflect the actual processing resource consumption of each variable. By changing from uniform variable treatment to weight-based differentiation, the system can identify and eliminate resource-intensive variables while maintaining equivalent functionality through lower-weight alternatives, thus reducing overall processing resource waste.
Solution Approach 2:
The system replaces manual rule optimization with an automated variable weight determination and optimization mechanism. The processor automatically calculates weights, identifies optimization opportunities, and suggests or applies variable replacements, substituting human effort with an efficient computational system that continuously optimizes resource usage.
3Productivity
If variable processing weights are determined and optimized, then processing efficiency is improved, but system complexity increases
Solution Approach 1:
The system segments the variable optimization problem into distinct modular components: variable weight determination module, variable mapping module, and rule optimization module. Each component handles a specific aspect of the optimization process, making the overall complex system manageable through functional decomposition and independent development of each module.
Solution Approach 2:
The system introduces variable weights as an intermediary parameter that mediates between raw variable characteristics and optimization decisions. Instead of directly comparing complex variable properties, the system uses weights as a simplified intermediate representation that facilitates efficient comparison and optimization while abstracting away the underlying complexity.
Data Source
AI summary
There are provided systems and methods for determining processing weights of rule variables for rule processing optimization. A service provider, such as an electronic transaction processor for digital transactions, may utilize different decision services that implement rules for decision-making of data including real-time data in production computing environments. Rules may correspond to coded statements that perform an automated decision-making service for the computing services and platforms of the service provider. When writing rules, different variables for data objects may be utilized, where each variable may perform a different operation and/or utilize a different operation for fetching and retrieving data used during rule processing. Each variable may therefore have a different data processing weights based on processing requirements of the data. Thus, optimization of rule authoring may be performed by mapping variables to other similar variables and showing a processing weight of each variable.