Comprehension Normalization for Big Data Insight
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Solution Overview
Problem
In the realm of Big Data, existing methods struggle to effectively compare and connect data sets due to differences in word meanings and contexts, leading to incomplete insights and inefficient knowledge extraction.
Innovation Solution
A computer-based tool implementing the Comprehension Normalization Method, which rephrases data between sets, allowing unique meanings to be derived and connected, enabling deeper insights by translating concepts between data sets using a dual nature approach inspired by quantum mechanics principles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional data comparison methods are used, then data processing is simple and fast, but the ability to capture unique meanings and generate insights is insufficient
Solution Approach 1:
The patent segments the comprehension normalization process into distinct operational components: identifying unique terms, generating contextual definitions, creating rephrased representations, and performing comparisons. This segmentation allows the system to handle complex semantic analysis through manageable steps, resolving the contradiction between preserving unique meaning and maintaining system simplicity.
Solution Approach 2:
The patent introduces an intermediary layer of contextual definitions and rephrased representations that mediate between raw data terms and comparison operations. This intermediary structure captures unique meanings without requiring direct complex processing of all data elements, thus preserving information while managing complexity.
2Adaptability or versatility
If data sets are treated with standardized meanings, then comparison is easier, but unique contextual meanings are lost
Solution Approach 1:
The patent applies local quality by generating context-specific definitions and rephrased representations for each unique term within its particular data set context. Rather than applying uniform standardization across all data, the system adapts the level of customization to each term's local context, preserving unique meanings while maintaining comparability through structured rephrasing.
Solution Approach 2:
The patent changes the parameter of term representation from fixed standardized meanings to dynamic contextual definitions. By allowing term meanings to be parameterized according to their specific context while maintaining a structured format for comparison, the system achieves both adaptability to unique meanings and ease of operation through consistent processing rules.
3Loss of information
If comprehensive rephrasing is performed between data sets, then insight generation improves, but processing time increases
Solution Approach 1:
The patent performs preliminary action by pre-identifying unique terms and generating their contextual definitions and rephrased representations before the actual comparison operation. This preparation work is done in advance, allowing the core comparison process to proceed more efficiently while still achieving comprehensive insight generation through the pre-computed contextual understanding.
Data Source
AI summary
The Comprehension Normalization Method of the present disclosure exploits the differences in the meanings of words or ideas between Big Data sets to build insight. When the comprehension normalization method is performed between two big data sets, both data sets take turns rephrasing the material of the other data set in their own language of understanding. The act of rephrasing a foreign idea connects the data within the set doing the rephrasing in a way it had not been connected before. After two sets take turns rephrasing the data within, both sets will become more connected than ever before and more insightful to the researcher.