Blended Data Model for Interactive 3D Visualization
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Solution Overview
Problem
Conventional data analysis tools are limited in their ability to blend and aggregate multiple related datasets, leading to independent analysis of each dataset, which hinders the detection of patterns and correlations across multiple datasets, and lacks interactive and real-time visualization capabilities necessary for rapid iteration in data analysis.
Innovation Solution
A method for transforming multiple related datasets into a blended data model (BDM) that enables interactive three-dimensional (3D) landscape visualizations, allowing users to manipulate and iteratively adjust visualizations in real-time, representing multiple parameters synergistically with color and motion to reveal hidden patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional data analysis tools are used to analyze multiple datasets, then each dataset can be analyzed independently with simple relationships, but the tools cannot blend and aggregate multiple related datasets with complex relationships concurrently
Solution Approach 1:
The patent introduces a blended data model as an intermediary layer between multiple source datasets and the visualization interface. This blended data model integrates data from multiple datasets with complex relationships through defined blending rules and aggregation functions, enabling the system to handle complex data relationships while maintaining analytical capabilities.
Solution Approach 2:
The patent segments the data integration process into distinct components: source datasets, blending rules, aggregation functions, and visualization parameters. This segmentation allows each component to be configured and managed independently, making the system adaptable to complex data relationships while maintaining operational simplicity.
2Productivity
If separate analysis of each dataset is performed, then analysis of each dataset can be done independently, but patterns and correlations across multiple datasets cannot be detected
Solution Approach 1:
The patent merges multiple datasets into a unified blended data model that preserves the unique characteristics of each source dataset while enabling cross-dataset analysis. The blending process combines data from multiple sources with complex relationships, allowing patterns and correlations to be detected across datasets without sacrificing the independence of individual dataset analysis.
Solution Approach 2:
The blended data model serves multiple functions simultaneously: it maintains the structure of individual datasets for independent analysis, integrates data across datasets for correlation detection, and provides a unified interface for visualization. This multi-functionality enables both rapid analysis and comprehensive pattern detection.
3Loss of time
If manual combination of analysis results is used, then results from separate analyses can be combined, but the process is not rapid or interactive
Solution Approach 1:
The patent performs preliminary actions by pre-configuring blending rules and aggregation functions that automatically combine data from multiple datasets before analysis. This preliminary integration eliminates the need for manual combination of results, enabling rapid and interactive analysis while maintaining the ability to handle complex data relationships.
Solution Approach 2:
The blended data model enables self-service data integration where the system automatically blends and aggregates data from multiple sources based on predefined rules, without requiring manual intervention. This self-service capability reduces time loss while maintaining ease of operation through automated processes.
4Ease of operation
If conventional visualization tools are used, then simple 2D charts can be generated, but interactive real-time manipulation and pattern revelation are limited
Solution Approach 1:
The patent transitions from traditional 2D chart visualizations to three-dimensional landscape visualizations. This dimensional change enables the representation of additional data parameters and relationships that cannot be effectively displayed in 2D, revealing hidden patterns while maintaining ease of interactive manipulation through the enhanced visual space.
Solution Approach 2:
The visualization system implements dynamic, interactive manipulation of the blended data model in real-time. Users can interactively adjust parameters, filters, and viewing angles, and the system responds dynamically to reveal patterns as they emerge from the data, enhancing both ease of operation and information discovery.
Data Source
AI summary
Systems and methods that enable blending and aggregating multiple related datasets to a blended data model (BDM), manipulation of the resulting data model, and the representation of multiple parameters in a single visualization. The BDM and each visualization can be iteratively manipulated, in real-time, using a user-directed question-answer-question response so patterns can be revealed that are not obvious.