Context-Based Data Analytics via Visual Intuition
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Solution Overview
Problem
Current data analytics systems rely heavily on time- and resource-intensive processes, requiring significant experience to extract insights from data, and often fail to provide intuitive understanding due to their reliance on absolute values rather than relative relationships, making it difficult for users to visualize and make decisions effectively.
Innovation Solution
A method and apparatus that utilize visual intuition and information visualization to facilitate context-based data analytics, allowing users to automate insights and understand information through relative values, enabling the generation of information contexts for research, analysis, and decision-making by processing data from various sources and applying user-driven contexts to determine Information Value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional data analytics systems process and present all data points, then complete information is provided, but it becomes difficult to extract insights and form understanding due to the sea of data points
Solution Approach 1:
The system extracts only the most relevant and meaningful data points from the complete dataset, presenting them in a visualized format that highlights key insights. This selective extraction allows users to see essential information without being overwhelmed by the full volume of data, resolving the contradiction between information completeness and ease of insight extraction.
Solution Approach 2:
The patent employs visual differentiation through color coding and visual properties to distinguish between different types of data points, their relationships, and their significance. This visual encoding enables users to quickly comprehend complex data relationships and extract insights without manually analyzing every data point, thus maintaining information completeness while improving ease of operation.
2Measurement precision
If absolute values are used to represent data, then precise measurements are provided, but intuitive understanding of relationships and changes is difficult to achieve
Solution Approach 1:
The system transforms one-dimensional absolute values into multi-dimensional visual representations that include spatial relationships, temporal sequences, and contextual connections. By adding visual dimensions such as position, size, color, and temporal ordering, the system maintains measurement precision while enabling intuitive understanding of relationships and changes among data points.
Solution Approach 2:
The patent implements nested visual structures where data points are organized hierarchically, with individual precise measurements nested within broader contextual groupings. This nested organization allows users to simultaneously view detailed precise measurements and their broader relationships, providing both measurement precision and intuitive understanding at multiple levels of abstraction.
3Loss of information
If extensive data points are collected for analysis, then comprehensive information is available, but the time and resources required for analysis increase significantly
Solution Approach 1:
The system performs preliminary automated analysis and filtering of data points before presentation to users, pre-identifying relevant patterns, relationships, and key insights. This preliminary action reduces the cognitive load on users by presenting only the most significant information while maintaining comprehensive information availability, thus improving analysis efficiency without sacrificing information comprehensiveness.
Solution Approach 2:
The patent introduces an intermediary automated analysis layer that mediates between the comprehensive dataset and the user, translating extensive data into curated visual presentations. This intermediary process automatically filters, prioritizes, and contextualizes data points, enabling users to access comprehensive information efficiently without manually processing every data point, thereby resolving the contradiction between information comprehensiveness and analysis efficiency.
Data Source
AI summary
Context-based data analytics based on visual intuition, and generation of information contexts for conducting research, analysis, and/or decision making. A plurality of information objects (IOs) are generated, each IO indicative of a plurality of data points corresponding to an object among objects for which information in a first domain is obtained, by containing information values (IVs) based upon context data, an IV generated based upon a product of each target data point, a relevance value (RV) of the target data point, a confidence value (CV) of the target data point, and a bias value. Visually displaying the IVs in a three dimensional space based on a distance formula to represent a relative relationship indicative of relevance as gravitational forces between the IOs using a set of concentric spheres corresponding to the IOs.