Ontology-Based Visualization Ranking for Large Datasets
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Solution Overview
Problem
Current technologies face challenges in analyzing and presenting large datasets, such as healthcare data, in a format that is understandable to non-technical users, as the sheer volume and complexity of data make it difficult to identify important relationships and visualize meaningful insights.
Innovation Solution
A system and method that generates visualizations of datasets by creating an ontology to indicate relationships between variables, prioritizes visualizations based on user intent and standards, and uses AI/ML to filter and rank visualizations, allowing users to navigate and explore data effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If comprehensive data analysis is performed on large datasets, then the completeness of information is improved, but the complexity of presentation increases
Solution Approach 1:
The patent segments the large dataset into multiple visualizations based on variables and relationships, presenting comprehensive information through divided graphical representations rather than a single complex display
Solution Approach 2:
The patent introduces an intermediary system that automatically selects and generates appropriate visualizations between the raw data and the user, translating complex data relationships into understandable graphical formats without exposing users to the underlying complexity
2Loss of information
If multiple visualizations are generated to represent all data relationships, then the completeness of insights is improved, but the difficulty of navigation increases
Solution Approach 1:
The patent performs preliminary action by automatically generating and ranking visualizations before user interaction, pre-organizing the content based on importance and user intent so that users don't need to navigate through unorganized options
Solution Approach 2:
The patent incorporates feedback mechanisms that learn from user interactions with visualizations, adjusting future visualization selections and rankings based on user preferences and behavior patterns
3Ease of operation
If AI/ML techniques are used to filter and rank visualizations, then the ease of understanding is improved, but the computational resources required increase
Solution Approach 1:
The patent applies partial action by using AI/ML techniques selectively to rank and filter visualizations rather than processing all possible visualizations equally, applying computational resources only where needed to achieve the desired ease of understanding
Data Source
AI summary
Disclosed is a system to obtain the data set including multiple variables. The system extracts the multiple variables from the data set. Based on the data set, the system creates an ontology indicating multiple relationships between two or more variables among the multiple variables, where a relationship among multiple relationships indicates a correlation between the two or more variables. The system obtains an intent associated with the user, and a visualization standard, where the visualization standard indicates an attribute associated with the visualization. The system generates a sequence of multiple visualizations to present to the user by ranking the multiple visualizations based on the correlation between the two or more variables, the visualization standard and the intent associated with the user. The system presents the sequence of multiple visualizations based on the ranking.