Data Importance Ranking via Recursive Matrix Correlation
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Solution Overview
Problem
Current data analysis systems face challenges in efficiently calculating the importance rank of data and transmitting it to servers, as they lack effective methods to analyze correlations and prioritize data based on relevance.
Innovation Solution
An electronic device and method that calculates a rank for data importance by analyzing correlations using adjacency matrices, normalizing data, and performing recursive calculations, while also transmitting data to a server based on this rank for efficient service provision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is transmitted without ranking by importance, then all data can be transmitted, but transmission efficiency is reduced and critical data may be delayed
Solution Approach 1:
The patent segments data into different priority levels by calculating importance ranks using correlation analysis. Data is divided into critical, important, and optional categories based on their rank values, allowing selective transmission of high-priority data first while maintaining overall transmission efficiency.
Solution Approach 2:
The patent changes the parameter of data transmission by introducing an importance rank parameter calculated through recursive matrix operations. This parameter determines the transmission order and priority, transforming the transmission process from uniform to prioritized based on data correlation and significance.
2Measurement precision
If complex correlation analysis is performed to rank data importance, then data priority can be accurately determined, but calculation complexity and processing time increase
Solution Approach 1:
The patent performs preliminary correlation analysis and matrix calculations before data transmission. By pre-calculating the importance ranks and storing them, the system avoids complex real-time calculations during transmission, reducing processing complexity while maintaining ranking accuracy.
Solution Approach 2:
The patent implements a dynamic ranking system where the importance matrix is updated recursively as new data arrives or conditions change. This allows the system to adapt to changing data correlations without performing complete recalculations, balancing accuracy with computational efficiency.
Data Source
AI summary
Provided is a method of calculating a rank for importance of data and an apparatus for performing the method. An electronic device includes a memory configured to store instructions and a processor electrically connected to the memory and configured to execute the instructions, and, when the instructions are executed by the processor, the processor is configured to perform a plurality of operations, and the plurality of operations includes calculating a first matrix for a correlation between the data, calculating a second matrix for importance of the data based on the first matrix, and calculating a rank of the data based on a result of a recursive calculation on the second matrix.