Agnostic Graph Encoding for 2D Numerical Data
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Solution Overview
Problem
Existing methods for visualizing and comparing large sets of numerical data are inefficient, requiring significant storage space and processing time, and are difficult for both humans and machines to analyze, especially when dealing with thousands of data points.
Innovation Solution
A method for encoding 2D numerical data that selects a subset of data points based on transitional relationships, representing them as strings to preserve major trends, and generating metadata for agnostic representation, allowing for efficient storage and comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all data points are stored and processed to maintain complete information, then measurement precision is improved, but loss of time and productivity deteriorate
Solution Approach 1:
The patent extracts only the essential characteristics from complete data sets by identifying key data points that represent major trends. Instead of processing all data points, the system extracts representative points that capture the essential information needed for accurate analysis, thereby reducing processing time while maintaining measurement precision.
Solution Approach 2:
The patent transforms numerical data into a different parameter representation using symbolic encoding. By changing the parameter form from raw numerical values to encoded symbols that represent trends and relationships, the system enables faster processing while preserving the essential information needed for accurate measurement and analysis.
2Loss of information
If all data points are stored to preserve complete information, then loss of information is reduced, but quantity of substance increases
Solution Approach 1:
The patent extracts only the essential characteristics from complete data sets by identifying key data points that represent major trends. Instead of storing all data points, the system extracts representative points that capture the essential information needed for accurate analysis, thereby reducing storage requirements while maintaining information completeness.
Solution Approach 2:
The patent creates a simplified symbolic representation (copy) of the original numerical data that preserves the essential information and trends. This symbolic copy contains only the critical information needed for analysis, significantly reducing storage requirements while maintaining the completeness of essential data characteristics.
3Measurement precision
If detailed numerical data is used to maintain precision, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent uses symbolic encoding where different symbols represent different data characteristics and trends, making the data more visually interpretable and easier for humans to understand. This symbolic representation transforms precise but difficult-to-interpret numerical data into an easily interpretable format that maintains measurement precision while improving ease of operation.
4Loss of information
If thousands of data points are processed to maintain complete information, then loss of information is reduced, but productivity deteriorates
Solution Approach 1:
The patent extracts only the essential characteristics from complete data sets by identifying key data points that represent major trends. Instead of processing all data points for comparison, the system extracts representative points that capture the essential information needed for accurate analysis, thereby reducing processing requirements while maintaining data completeness.
Solution Approach 2:
The patent segments the complete data set into manageable portions by identifying and extracting key representative data points. This segmentation allows for more efficient processing and comparison of data while preserving the essential information contained in the complete data set, thereby improving productivity without sacrificing data completeness.
Data Source
AI summary
A method for encoding 2D numerical data comprises determining encoding parameters for a received set of 2D numerical data and generating a set of encoded data from the set of 2D numerical data according to the encoding parameters. The encoding parameters indicate a transitional relationship among a plurality of consecutive data points and a unitization interval for sampling the first set of 2D numerical data. When generating the set of encoded data, the encoding method sets a starting point, samples the set of 2D numerical data according to the unitization interval, and determines a string as a value of each data point of the set of the encoded data. The string indicates a position of a present encoded data point in the set of the encoded data, the transitional relationship, and a difference of magnitude between a present encoded data point and an immediately preceding one.