Data Visualization Engine for Complex Relationship Analysis
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Solution Overview
Problem
Current data visualization tools struggle to effectively visualize complex data relationships, especially as data volumes increase, and often require manual construction, which is time-consuming and difficult, with limited support for presenting intricate relationships like manager reporting structures, social networks, or product categories.
Innovation Solution
A data visualization engine that retrieves and displays data fields and relationships using connectors or visual cues, allowing users to encode relationships in mark positions, connector properties, and aggregation, enabling quick and easy creation of data visualizations by treating relationships as data fields or using them to specify positions and connections between marks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data volume increases, then data analysis capability improves, but difficulty to extract meaning and identify relationships increases
Solution Approach 1:
The patent transforms two-dimensional table data into three-dimensional visual representations by adding spatial relationships. Entities are positioned in 2D space based on their relationships, creating a third dimension of spatial information that makes hidden patterns and connections visible, thereby reducing the difficulty of extracting meaning from large datasets.
Solution Approach 2:
The patent introduces visual intermediaries including connectors, highlights, and spatial positioning that mediate between raw data and human understanding. These visual elements serve as intermediaries to represent relationships and patterns, making complex data structures more accessible and easier to analyze.
2Adaptability or versatility
If manual data visualization construction is used, then customization flexibility improves, but time consumption and difficulty increase
Solution Approach 1:
The system automatically generates visualizations by processing the input data structure itself. The data's inherent relationships and patterns are automatically translated into visual representations without requiring manual construction, thereby reducing time consumption while maintaining customization through data-driven automatic adaptation.
Solution Approach 2:
The patent automatically adjusts visualization parameters such as entity positioning, connector styling, and highlight effects based on the data's structural characteristics. This automatic parameter adjustment maintains customization flexibility while eliminating manual construction time, as the system adapts visualization parameters to the specific data structure being processed.
3Device complexity
If simple node-link diagrams are used, then visualization simplicity improves, but ability to present complex data relationships deteriorates
Solution Approach 1:
The patent segments complex data relationships into distinct visual components: entities are represented as separate nodes, relationships as connectors, and patterns as highlights. This segmentation allows simple basic elements to be combined to represent complex relationships, maintaining visualization simplicity while enhancing the ability to present complex data structures.
Solution Approach 2:
The patent creates composite visual representations by combining multiple visualization techniques including node-link diagrams, spatial positioning, connectors, and highlights. This composite approach integrates the simplicity of basic node-link structures with the expressive power of additional visual elements, enabling effective presentation of complex data relationships.
4Measurement precision
If relationships are visualized using connectors and positioning, then relationship representation accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent replaces complex mechanical data processing operations with visual computation. Instead of requiring complex algorithms to process and represent relationships, the system uses visual mechanisms such as spatial positioning and connector drawing that naturally encode relationship information, thereby reducing processing complexity while maintaining representation accuracy.
Data Source
AI summary
A method generates data visualizations. A computing device retrieves a set of tuples from a database according to user selection. Each tuple has the same set of fields. The device identifies a relation between tuples. The relation is a non-empty set of ordered pairs of tuples from the set of tuples. A user selects a base tuple from the set of tuples and the device forms a filtered subset of tuples consisting of the selected base tuple and those tuples that are connected to the selected base tuple by a sequence of tuples that are related by the relation. The user selects an aggregation level, which consisting of fields from the set of fields. The device generates and displays a data visualization by aggregating the filtered subset of tuples at the selected aggregation level to form a set of aggregated tuples, and displaying each aggregated tuple as a visible mark.