Intelligent Data Visualization Configuration via Image-Derived Color Formatting
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Solution Overview
Problem
Current data processing systems face challenges in allowing users to effectively interpret associations between large datasets and numerous entities, as existing techniques often fail to provide clear visualizations, making it difficult for users to associate specific entities with related data.
Innovation Solution
The described technologies enable intelligent configuration of data visualizations by determining formatting elements based on input data characteristics, such as primary colors from images, to generate visualizations that help users identify associated subjects and display related data in a clear and interpretable manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If pre-determined layouts are used to display large datasets, then data can be organized systematically, but users cannot readily interpret associations between entities and related data
Solution Approach 1:
The system applies different visual formatting elements to different parts of the data visualization based on local characteristics. Images are extracted from input data and used as formatting elements specific to each entity, allowing users to readily associate entities with their related data through distinctive visual markers while maintaining systematic organization of the overall dataset.
Solution Approach 2:
The system determines primary colors from extracted images and applies these colors as formatting elements in the data visualization. By coloring visual elements according to the primary colors derived from entity-associated images, the system creates clear visual associations between entities and their data while maintaining systematic layout organization.
2Quantity of substance
If large amounts of data are displayed in preset layouts, then comprehensive information is presented, but user interpretation efficiency decreases
Solution Approach 1:
The system extracts images from input data and determines primary colors from these images. These primary colors are then applied as formatting elements to visual elements in the data display, enabling users to quickly interpret associations among large amounts of data through color-coded visual cues, thereby maintaining comprehensive data presentation while significantly improving interpretation efficiency.
Solution Approach 2:
The system extracts and uses images from the input data as formatting elements in the visualization. By copying visual characteristics (images and their derived colors) from the source data and applying them to the display format, the system creates intuitive visual associations that enhance user interpretation efficiency without losing comprehensive data information.
3Adaptability or versatility
If traditional data visualization methods are used, then data can be displayed, but graphical associations between entities and related data are not provided
Solution Approach 1:
The system extracts images from input data and uses them as formatting elements specific to each entity's visual representation. This local application of image-derived formatting creates distinctive visual markers for each entity, providing clear graphical associations between entities and their related data while maintaining the flexibility of the overall visualization system.
Solution Approach 2:
The system copies images from the input data and uses them as formatting elements in the visualization output. By copying these visual elements and their derived primary colors to the display format, the system creates inherent graphical associations that link entities with their related data, enhancing the adaptability and informativeness of the visualization.
Data Source
AI summary
The techniques described herein determine configurations for data visualizations based on characteristics interpreted from input data. Input data including a plurality of images may be obtained. For instance, the input data may include one or more files containing images associated with an entity. The techniques disclosed herein may determine a characteristic, such as a primary color, based on the input data. The techniques disclosed herein may determine an individual entity or subject to be associated with the characteristic. Techniques disclosed herein also involve the generation of output data defining a visualization based on the characteristic. A rendering of the output data provides an indication of the individual entity or subject. In some configurations, a rendering of the output data provides a graphical association between data in a dataset and the individual entity or subject.