Condition Tree Segmentation for Repeated Variable Evaluation
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Solution Overview
Problem
Evaluating a condition tree using a set of variables is computationally expensive, and repeated evaluations compound resource consumption due to unchanged static variables.
Innovation Solution
Identify and replace static subparts of the condition tree with processing results, generating an optimized condition tree that omits static variables, reducing computational cost by evaluating only dynamic subparts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If repeated evaluations of the condition tree are performed to identify variables that affect the evaluation result, then comprehensive variable analysis is achieved, but resource consumption increases due to redundant computations of static variables
Solution Approach 1:
The condition tree is segmented into static subparts and dynamic subparts. Static subparts containing variables that do not change across evaluations are identified and separated from dynamic subparts. This segmentation allows the system to evaluate only the dynamic subparts in repeated evaluations, eliminating redundant computations of static portions while maintaining complete variable analysis capability.
Solution Approach 2:
Static subparts of the condition tree are evaluated once in advance before repeated evaluations begin. The results of these preliminary evaluations are stored and reused across multiple evaluations. This preliminary action eliminates the need to re-evaluate static variables in each subsequent evaluation, significantly reducing computational resource consumption while preserving the ability to identify all variables affecting the evaluation result.
2Reliability
If the condition tree is evaluated using all variables in each iteration, then complete evaluation accuracy is maintained, but computation time increases due to unchanged static variables being reprocessed
Solution Approach 1:
The condition tree is divided into static and dynamic subparts. Static subparts are evaluated once and their results cached, while dynamic subparts are re-evaluated in each iteration. This segmentation maintains complete evaluation accuracy by ensuring both static and dynamic variables are considered, while reducing computation time by eliminating redundant processing of unchanged static variables.
Solution Approach 2:
The evaluation process maintains continuity by seamlessly integrating the pre-evaluated static subparts with the dynamically re-evaluated subparts. The cached results of static subparts are continuously reused across iterations, while dynamic subparts are continuously updated, ensuring complete and accurate evaluation results without redundant computation of static portions.
3Adaptability or versatility
If static variables are included in repeated evaluations, then comprehensive variable coverage is ensured, but computational efficiency decreases due to redundant processing
Solution Approach 1:
The condition tree is segmented into static and dynamic subparts based on variable characteristics. Static subparts are identified and evaluated once, with results stored for reuse. Dynamic subparts are re-evaluated in each iteration. This segmentation ensures comprehensive variable coverage by including both static and dynamic variables in the overall evaluation, while improving computational efficiency by eliminating redundant processing of static variables.
Solution Approach 2:
Static subparts are evaluated in advance and their results are stored before repeated evaluations begin. This preliminary evaluation ensures that static variables are covered in the analysis without being reprocessed in each iteration. The cached results are then integrated with dynamic subpart evaluations, maintaining comprehensive variable coverage while significantly improving evaluation efficiency.
Data Source
AI summary
Aspects of the present disclosure relate to condition tree optimization techniques. In examples, a condition tree is comprised of rules and associated logical operators. The condition tree is used to process a set of variables, thereby generating an evaluation result. In some instances, multiple evaluations are performed, for example to identify variables that affect the evaluation result of the condition tree. As compared to dynamic variables that may change across various iterations, certain variables may be static. Subparts of the condition tree associated with such static variables may be identified, such that they may be evaluated and replaced with a resulting processing result, thereby generating an “optimized” condition tree. Thus, the optimized condition tree may comprise dynamic subparts and processing results in place of static subparts. The condition tree may be processed to identify variables that negatively affect an evaluation result, which may be identified as candidate variables for change.