Data Analysis Assembly Evolution for Complex Dataset Insights
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Solution Overview
Problem
Existing data analysis tools are inadequate in searching for patterns and correlations among large datasets, limiting their ability to provide additional insights beyond simple queries.
Innovation Solution
A computer-implemented method that forms an initial assembly of datasets and algorithmic relationships, simulates their evolution through interactions and randomization, and culled assemblies that fail to meet target objective functions, ultimately providing datasets that meet an optimality criterion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing data analysis tools are used to search for patterns and correlations among large datasets, then simple queries can be executed, but the ability to provide additional insights and discover novel relationships is limited
Solution Approach 1:
The data analysis system is segmented into multiple autonomous assemblies, each capable of independently analyzing specific aspects of the dataset. These assemblies work in parallel to discover different patterns and relationships, thereby preserving more insights without proportionally increasing overall system complexity.
Solution Approach 2:
The system dynamically changes parameters such as dataset variations and algorithmic conditions across different assemblies. By varying these parameters, the system can explore multiple hypothesis spaces simultaneously, discovering novel relationships that single-parameter tools would miss.
2Manufacturing precision
If datasets and algorithmic relationships are evolved through multiple iterations with interactions and randomization, then optimized relationships are discovered, but computational time and resources increase
Solution Approach 1:
Assemblies are pre-configured with initial datasets and algorithmic relationships before the evolution process begins. This preliminary setup allows the system to start from informed positions rather than random states, reducing the number of iterations needed to reach optimized relationships.
Solution Approach 2:
The evolution process maintains continuous useful action by having assemblies interact and share findings throughout the iteration process. Rather than isolated sequential processing, assemblies continuously refine relationships based on ongoing interactions, achieving precision more efficiently.
3Reliability
If assemblies are culled based on target objective functions, then quality relationships are maintained, but the diversity of explored solutions may be reduced
Solution Approach 1:
The culling process uses feedback from target objective functions to guide which assemblies are retained and which are eliminated. This feedback mechanism ensures that only assemblies meeting quality thresholds continue, maintaining reliability while the feedback loop continuously adapts to preserve diverse solution paths.
Solution Approach 2:
The culling criteria and assembly population are dynamic rather than static. The system adapts culling thresholds and assembly configurations during evolution, allowing versatility to be maintained in early stages while reliability is emphasized in later stages as optimal solutions emerge.
Data Source
AI summary
A data analysis and processing method includes forming an initial assembly of datasets comprising multiple entities, where each entity is a collection of variables and relationships that define how entities interact with each other, simulating an evolution of the initial assembly by performing multiple iterations in which a first iteration uses the initial assembly as a starting assembly, and querying, during the simulating, the evolution of the initial assembly, for datasets that meet an optimality criterion.