Genetic Query Element Selection Across Domains for Consistent Search
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Solution Overview
Problem
Existing query generation systems lack efficient methods for optimizing the selection of query elements across multiple domains to ensure internal consistency and correlation with reference queries, leading to suboptimal query execution and analysis.
Innovation Solution
A genetic algorithm-based approach is employed to optimize the selection of query elements, utilizing techniques like principal component analysis (PCA), exploratory factor analysis (EFA), and Cronbach's alpha to ensure internal consistency and correlation, with the ability to transform scores and determine clusters for improved query execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of query elements is increased to improve internal consistency and correlation with reference queries, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces manual or rule-based query element selection with a genetic algorithm, an automated computational system that evolves optimal element combinations through iterative selection, crossover, and mutation operations. This substitution automates the complex process of selecting query elements that maximize internal consistency and correlation with reference queries.
Solution Approach 2:
The patent changes the selection criteria parameters by using genetic algorithms to optimize for specific metrics (internal consistency, correlation with reference queries) rather than relying on fixed rules. The system dynamically adjusts which query elements are selected based on their performance in meeting these optimized parameters.
2Measurement precision
If genetic algorithm optimization is applied to maximize internal consistency and correlation, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent performs preliminary actions by pre-processing candidate query elements and organizing them into domains before applying the genetic algorithm. This preparation work includes identifying relevant elements and structuring them in advance, which reduces the computational burden during the optimization phase and speeds up the overall process.
Solution Approach 2:
The patent applies partial action by focusing the genetic algorithm optimization on specific subsets of query elements and domains rather than processing all possible elements equally. This selective approach concentrates computational resources on the most critical elements, improving efficiency while maintaining optimization quality.
3Measurement precision
If multiple analysis methods (PCA, EFA, FA) are used to determine domain correspondence and subscales, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex analysis process into distinct stages: Principal Component Analysis (PCA) for initial domain identification, followed by Exploratory Factor Analysis (EFA) for subscale detection, and finally Factor Analysis (FA) for refinement. Each segmentation handles a specific aspect of the analysis, making the overall complex process more manageable and systematic.
Solution Approach 2:
The patent uses genetic algorithms as an intermediary mechanism that bridges the gap between multiple analysis methods and the final query element selection. The genetic algorithm processes the outputs from PCA, EFA, and FA analyses, integrating their results to determine the optimal set of query elements that satisfy both domain correspondence and optimization criteria.
Data Source
AI summary
A query generation system includes memory hardware configured to store instructions and processor hardware configured to execute the instructions. The instructions include obtaining a definition of a query specifying a number of elements of the query. The instructions include identifying candidate query elements for the query that correspond to a set of multiple domains. The instructions include, based on the specified number, determining a target number for each of the domains. The instructions include optimizing a selection of query elements from among the candidates. Using data from prior queries, the optimization maximizes at least one of internal consistency of the selected query elements and correlation of the selected query elements with a reference query. The optimization selects the target number of query elements for the domains. The instructions include storing the selected query elements. The instructions include transmitting information identifying the selected query elements to a query execution system.