Analytic Data Focus Representations for Visualization
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Solution Overview
Problem
Conventional tag clouds become ineffective for large datasets as they represent textual data randomly, making them difficult to read and analyze, especially in 'big data' analytics contexts, where they convey little information and are unsuitable for complex data sources.
Innovation Solution
The development of analytic data focus representations and generalized tag cloud visualizations that use multiple informational dimensions such as classification, clustering, derivation, and computable metrics to determine visualization element characteristics, breaking the conventional association with frequency of occurrence, allowing for enhanced data manipulation and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional tag clouds represent textual data randomly with size proportional only to frequency of occurrence, then the visualization is simple to generate, but the information conveyed becomes very difficult to read and analyze when the number of documents becomes large
Solution Approach 1:
The patent introduces multiple informational dimensions beyond frequency of occurrence, including classification, clustering, derivation, and computable metrics. These additional dimensions are mapped to various visualization characteristics such as color, shape, position, and size, transforming the tag cloud from a single-dimension frequency representation into a multi-dimensional information display that conveys much more analytical value while remaining visually manageable.
Solution Approach 2:
The patent applies different visualization characteristics to different aspects of the data. Each tag element can have its size, color, shape, and position determined by different informational dimensions appropriate to that specific aspect. For example, size might represent frequency while color represents classification category, allowing local optimization of information presentation for different data attributes.
2Ease of manufacture
If conventional tag clouds use only frequency of occurrence to determine text string size, then the visualization method is simple and easy to implement, but it becomes unsuitable for complex data sources in big data analytics contexts
Solution Approach 1:
The patent creates a universal visualization framework that can handle multiple types of data sources and analytical requirements through a single system. The analytic data focus representations serve as an intermediate layer that can process various data types (textual data, structured data, unstructured data) and transform them into standardized visual elements. This multi-functional approach allows the same visualization system to adapt to different data sources and analytical contexts without requiring separate implementation methods for each case.
Solution Approach 2:
By introducing multiple informational dimensions (classification, clustering, derivation, computable metrics) beyond simple frequency counting, the system gains versatility to handle complex data sources. These additional dimensions provide the analytical depth needed for big data applications while maintaining the visual accessibility of tag cloud format.
3Productivity
If conventional tag clouds display multiple words simultaneously with size proportional to frequency, then the visualization provides quick overview, but it conveys little information and becomes very difficult to read when the number of documents becomes large
Solution Approach 1:
The patent maps multiple informational dimensions to different visual characteristics of tag elements. Size continues to represent frequency for quick overview, but additional dimensions such as classification are mapped to color, clustering to position or grouping, and computable metrics to shape or other visual attributes. This multi-dimensional mapping allows the visualization to maintain quick overview capabilities while simultaneously conveying rich analytical information that would be impossible to communicate through size alone.
Solution Approach 2:
Different visual characteristics are optimized for different information needs: size for frequency perception, color for classification differentiation, position for clustering relationships. This local optimization of visual properties for specific information types allows the tag cloud to convey multiple layers of analytical information simultaneously without sacrificing readability or overview efficiency.
Data Source
AI summary
An analytics controller is configured for communication with one or more data sources. The analytics controller comprises an analytic data focus representation module and a visualization generator coupled to the analytic data focus representation module, with the analytic data focus representation module being configured to derive a plurality of analytic data focus representations from the one or more data sources, and the visualization generator being configured to generate visualizations based at least in part on the analytic data focus representations. At least one of the analytic data focus representation module and the visualization generator may be further configured to establish a plurality of linkages with each such linkage associating one or more of the representations with one or more of the visualizations. The analytics controller may be part of a data management system implemented using one or more processing devices of a processing platform.