Mixed-Data Correlation Using Iterative Confidence Matching
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Solution Overview
Problem
Conventional systems struggle to efficiently sort and correlate unstructured and structured data together, leading to obfuscated interrelationships and low accuracy due to naive sorting methods that ignore data structure, making further processing difficult.
Innovation Solution
A system and method that employs a data comparator to apply sorting and matching algorithms to structured and unstructured data, utilizing context data and machine learning classifiers to identify associations and metadata, and iteratively refine matches with confidence scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional systems use naive sorting methods for mixed data, then the sorting process is simple and fast, but the accuracy and efficiency of correlating unstructured and structured data deteriorates
Solution Approach 1:
The system segments the data into structured and unstructured portions, applying different sorting strategies to each. Structured data is sorted using intelligent sorting that respects field relationships, while unstructured data is sorted separately. This segmentation allows the system to maintain high accuracy for data correlation while preserving processing efficiency.
Solution Approach 2:
The patent applies local quality by treating structured and unstructured data differently in the sorting process. Instead of applying a uniform naive sorting method to all data, the system applies intelligent sorting to structured data portions and simpler sorting to unstructured data portions, optimizing both accuracy and efficiency for each data type.
2Measurement precision
If conventional systems apply intelligent sorting to structured data, then data correlation accuracy improves, but the complexity of processing mixed data increases
Solution Approach 1:
The system divides the data processing task into segments, handling structured and unstructured data separately. This segmentation reduces overall processing complexity by allowing the system to use simple sorting for unstructured data and intelligent sorting only where needed for structured data, rather than attempting to apply complex sorting to all data uniformly.
Solution Approach 2:
The patent introduces an intermediary approach by using a data comparator that can identify and separate structured from unstructured data. This intermediary mechanism simplifies the processing of mixed data types by first classifying data and then applying appropriate sorting methods, reducing the complexity of handling heterogeneous data.
3Productivity
If conventional systems sort all data uniformly, then the sorting process is efficient, but the interrelationships and structure of data are obfuscated
Solution Approach 1:
The system segments data into structured and unstructured portions, preserving the structural information of structured data through intelligent sorting that respects field relationships. This segmentation prevents the obfuscation of interrelationships by ensuring that structured data maintains its logical organization while unstructured data is processed separately.
Solution Approach 2:
The patent applies local quality by maintaining different sorting behaviors for different data portions. Structured data undergoes intelligent sorting that preserves its structure and interrelationships, while unstructured data undergoes simpler sorting. This localized approach prevents the loss of structural information in structured data while maintaining overall processing efficiency.
Data Source
AI summary
In some aspects, the disclosure is directed to methods and systems for sorting, correlation, and/or matching of structured and unstructured data. In some implementations, metadata associated with structured data may be applied to unstructured data based on the results of the sort or correlation. Such sorting or matching may be performed through an efficient iterative process with progressive confidence scores, and use a priori knowledge from correlations between structured data values to identify potentially associated unstructured data values.