Cross Visualization Interaction via Correlation Tables
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Solution Overview
Problem
Current data visualization tools lack the ability to seamlessly interact and correlate across different visual representations, limiting users' ability to customize and compare data insights effectively.
Innovation Solution
A visual analyzer system that enables cross-visualization interaction by generating and displaying multiple visualizations, allowing users to select and highlight corresponding portions across different visualizations through a graphical user interface, utilizing correlation tables to identify and mark dimension members and their relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple visualizations are displayed to represent different data aspects, then data exploration capability is improved, but system complexity increases
Solution Approach 1:
The system segments data representation into multiple independent visualizations, each focusing on specific data aspects or dimensions. This allows users to explore different facets of the data separately while maintaining overall system manageability through modular visualization components.
Solution Approach 2:
The system implements a universal correlation mechanism that works across all visualization types. The correlation table structure and interaction framework provide multi-functional support for linking different visualizations, reducing the need for separate handling logic for each visualization pair.
2Adaptability or versatility
If cross-visualization correlation features are added to enable seamless interaction, then user customization capability is improved, but device complexity increases
Solution Approach 1:
The system introduces correlation tables as intermediary structures that mediate between multiple visualizations. These tables store dimensional relationships and enable automatic correlation computation, acting as a buffer that simplifies the interaction logic between diverse visualization types while enabling rich cross-visualization features.
Solution Approach 2:
The system performs preliminary actions by pre-computing and storing dimensional correlations in correlation tables before user interaction occurs. This advance preparation of relationship data enables rapid on-demand correlation queries without requiring complex real-time computation during user interactions.
3Measurement precision
If correlation tables are used to identify dimension members across visualizations, then data correlation accuracy is improved, but information processing time increases
Solution Approach 1:
The system pre-computes dimensional correlations and stores them in correlation tables during data loading or visualization creation. This preliminary action ensures high correlation accuracy when users interact with visualizations, as the relationships are already established and stored rather than computed on-demand.
Solution Approach 2:
The correlation tables store dimension member relationships at a granular local level, maintaining precise mappings for specific dimension combinations. This localized precision allows accurate correlation queries without requiring global recomputation, reducing processing time for specific correlation requests.
Data Source
AI summary
Some embodiments disclose techniques for enabling cross visualization interaction between data corresponding to different visualizations. In an embodiment, a visual analyzer system is disclosed that is capable of generating and displaying a plurality of visualizations to a user via a graphical user interface. The visual analyzer system is capable of receiving a selection of a particular portion of a visualization from a user and correlating the selected portion with portions of data in other visualizations that are displayed to the user. In some embodiments, the visual analyzer system is capable of highlighting the correlated portions of the data and presenting the highlighted portions via the graphical user interface to a user.