Big-Data Network Diagrams with Co-Occurrence-Based Display Subsets
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data-processing systems face challenges in handling large and complex data sets, leading to resource wastage and cognitive strain for human operators due to the generation of unnecessary, superfluous, and redundant information, which can result in inaccurate data processing and remedial actions.
Innovation Solution
A data visualization system analyzes data sets to identify co-occurrences between variables without determining their meanings, generating network diagrams that visually distinguish nodes and edges based on co-occurrence quantities, and provides a subset of the diagram tailored to the display device's size to reduce cognitive load and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data-processing software is used to handle large and complex data sets, then data processing can be performed, but resource wastage and cognitive strain occur due to generation of unnecessary information
Solution Approach 1:
The patent segments the network diagram into a subset containing only the most relevant nodes and edges based on co-occurrence frequency. By dividing the complete diagram into a manageable subset, the system reduces the amount of information processed and displayed, thereby reducing computational resources and cognitive load while maintaining processing efficiency.
Solution Approach 2:
The patent extracts only the essential information from the complete network diagram by identifying and selecting nodes with highest co-occurrence frequencies and their associated edges. This extraction process removes unnecessary and redundant information, reducing resource consumption while preserving the core analytical value of the data processing system.
2Loss of information
If a complete network diagram is generated to display all variables and relationships, then comprehensive data interpretation is achieved, but cognitive strain increases due to overwhelming information
Solution Approach 1:
The patent segments the comprehensive network diagram into a focused subset that highlights only the most significant relationships based on co-occurrence frequency. This segmentation maintains accurate data interpretation by preserving critical information while reducing cognitive load by eliminating less relevant details from the display.
Solution Approach 2:
The patent applies local quality by differentiating the importance of nodes and edges in the network diagram. Nodes with higher co-occurrence frequencies are prioritized and displayed with greater prominence, while less important nodes are excluded. This creates a localized focus on critical relationships, maintaining interpretation accuracy while reducing overall cognitive strain.
3Measurement precision
If detailed analysis of variable meanings is performed, then accurate relationship determination is achieved, but processing time increases
Solution Approach 1:
The patent employs self-service by allowing the data itself to determine relationships through co-occurrence frequency analysis without requiring external interpretation or manual analysis of variable meanings. The system automatically identifies relationships based on patterns in the data, achieving accurate relationship determination while significantly reducing processing time compared to manual analysis methods.
Data Source
AI summary
A device may analyze a plurality of entries of a data set. The plurality of entries identify a plurality of variables. Each entry indicates a co-occurrence of variables of different types. The device may identify, based on analyzing the plurality of entries, variables of a first type and variables of a second type. The device may determine relationships between the variables of the first type and variables of the second type based on co-occurrences indicated by the plurality of entries. The relationships are determined without determining meaning of the variables of the first type and the variables of the second type. The device may detect a display size of a display device. The device may determine, based on the display size, a subset of the network diagram to provide to the display device and may provide the subset of the network diagram to the display device.