Data Analysis System for Rapid Relevance Identification
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Solution Overview
Problem
Current data analysis methods for big data are inefficient in rapidly identifying relevance between different particular items, requiring repetitive analysis and struggling to find connections between them.
Innovation Solution
A data analysis system comprising a transmission unit, storage unit, control unit, processing unit, and display unit that generates parameters and research approaches to analyze data using statistical algorithms, producing integration information to quickly understand the relevance between items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword-based searching and analysis is performed for each particular item separately, then analysis results for individual items can be obtained, but it requires repetitive analysis and makes it difficult to find relevance between different particular items
Solution Approach 1:
The patent merges multiple separate analyses into a unified analysis framework. Instead of performing keyword-based searching and analysis separately for each particular item, the system integrates multiple items into a single analysis process that simultaneously evaluates relationships between items, thereby eliminating repetitive operations and improving research efficiency while maintaining analysis accuracy.
Solution Approach 2:
The patent creates a universal analysis platform that can handle multiple particular items simultaneously. The system designs an analysis framework that serves multiple functions: it can analyze individual items, compare relationships between items, and generate comprehensive results all within a single operational system, thus improving productivity without sacrificing measurement precision.
2Loss of information
If repetitive analysis is performed for different particular items, then comprehensive analysis results can be obtained for each item, but time consumption increases and relevance between items cannot be efficiently identified
Solution Approach 1:
The patent implements preliminary action by pre-processing and organizing data structures before performing detailed analysis. The system prepares unified data frameworks and relationship models in advance, allowing subsequent analyses to be conducted more efficiently without losing information completeness. This preliminary preparation enables the system to avoid repetitive full analyses while maintaining comprehensive result quality.
Solution Approach 2:
The patent uses copying mechanisms to reuse analysis results and data structures across different particular items. Instead of performing complete repetitive analyses, the system copies and adapts previously computed results, relationship models, and data frameworks to new analysis contexts, significantly reducing time consumption while preserving the completeness and accuracy of analysis results.
3Measurement precision
If traditional analysis methods are used to explore relationships between events, then detailed individual analyses can be conducted, but it is difficult to rapidly understand relevance between different events
Solution Approach 1:
The patent introduces another dimension by adding a relationship analysis layer that operates alongside traditional individual item analysis. The system visualizes and analyzes events not only in isolation but also in relation to other events through graphical interfaces and relationship metrics, enabling users to rapidly understand relevance between different events while maintaining detailed analysis quality through the multi-dimensional approach.
Data Source
AI summary
A data analysis system includes: a transmission unit receiving research data; a storage unit saving the research data; a control unit generating a research approach, a first parameter, and a second parameter according to an operation instruction; a processing unit obtains research data from to-be-analyzed data by using the transmission unit according to the research approach, and the parameters; the processing unit analyzes the parameters and the research data by using a statistical algorithm, to generate statistical information; and then analyzes the related first parameter, second parameter, and various pieces of research data according to a test algorithm, to generate a statistical test; and a display unit, connected to the processing unit and used to display integration information, where the integration information is obtained by the processing unit by integrating the related first parameter, second parameter, statistical information, and statistical test according to an integration algorithm.