Information Cataloging via Graph Distance and Noise Ratios
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current information search methods are incompatible with large datasets, such as those found on the Internet and cloud servers, making it difficult to capture and establish relationships between vast amounts of data, particularly for entities like e-mail addresses, phone numbers, and names, due to prohibitive indexing requirements.
Innovation Solution
An information cataloging system that represents elements as nodes and relationships as edges in a directed graph, calculates distances and noise-to-signal ratios, and uses confidence levels to infer relationships, allowing for efficient association of large numbers of observations with high statistical confidence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional indexing methods are used to establish relationships between data elements, then relationship accuracy can be maintained, but the processing time and computational resources become prohibitive for large datasets
Solution Approach 1:
The patent changes the fundamental parameters of the search approach by transitioning from exact matching to probabilistic similarity scoring. It introduces confidence levels and noise-to-signal ratios as new parameters to evaluate relationships, allowing the system to work with large datasets without requiring exhaustive indexing while maintaining acceptable relationship accuracy through statistical methods
Solution Approach 2:
The patent introduces common connections as intermediary elements that mediate between disparate data nodes. By identifying shared connections (such as common email domains, organizational affiliations, or geographic locations), the system can infer relationships indirectly without requiring direct comparison of all data pairs, significantly reducing computational complexity
2Loss of information
If comprehensive indexing is performed on all available data to ensure complete relationship discovery, then relationship coverage is improved, but the computational complexity and resource requirements increase prohibitively
Solution Approach 1:
The patent segments the large-scale relationship discovery problem into smaller, manageable components by processing data in distributed clusters and breaking down the analysis into discrete confidence level calculations. This segmentation allows the system to handle comprehensive data coverage through parallel processing rather than requiring a single complex indexing operation
Solution Approach 2:
The patent applies partial action by focusing computational resources on calculating confidence levels for the most promising relationships identified through noise-to-signal ratio filtering, rather than performing exhaustive indexing on all possible data pairs. This selective approach maintains relationship coverage while reducing overall computational complexity
3Measurement precision
If strict matching criteria are applied to ensure high confidence in relationships, then relationship precision is improved, but the quantity of relationships discovered decreases
Solution Approach 1:
The patent introduces dynamic confidence thresholds that can be adjusted based on the specific data context and relationship type. Rather than applying a single static matching criterion, the system dynamically determines appropriate confidence levels for different relationship categories, allowing it to maintain precision for critical relationships while discovering additional relationships in contexts where lower confidence is acceptable
Data Source
AI summary
An information cataloging system disclosed herein provides a system and method for inferring relationships between various elements, such as e-mail address, phone number, etc., of various observations, such as business cards, observations obtained from the Internet, etc. The method comprises representing various elements, such as name, e-mail address, etc., using nodes, representing the relations between the various elements using edges connecting these nodes, computing a distance between two disparate nodes, wherein each of the two disparate nodes represent an element related to the entity. An implementation of the information cataloging system disclosed herein also provides a method of calculating noise and signal to noise ratio attached to various nodes and using such noise information in calculating confidence level of relationships between various elements.