Sparse Multi-Dimensional Data Visualization Using Cascaded Planes
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Solution Overview
Problem
Current graphical user interfaces for analyzing multi-dimensional datasets do not allow users to start analysis from the dense area of the data, leading to excessive time spent by users and computational resources in highly sparse cubes, where data is only present at a small subset of dimensions.
Innovation Solution
The method involves converting a multi-dimensional dataset into a three-dimensional cascaded plane architecture, partitioning by the most sparse dimension, and aligning remaining dimensions into two-dimensional planes, with opacity and color assignments based on sparsity and density quantum, positioning the darkest and least translucent sections at the center for intuitive analysis initiation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users analyze multi-dimensional datasets using current graphical user interfaces, then data analysis can be performed, but users cannot start analysis from the dense area of data, leading to excessive time consumption
Solution Approach 1:
The patent introduces a new visualization dimension by mapping data density to spatial positioning in the graphical interface. Dense data regions are projected to the center of the display area, while sparse regions are positioned at the periphery. This dimensional transformation allows users to immediately identify and start analysis from dense data areas without time-consuming manual exploration.
Solution Approach 2:
The patent applies color coding to different regions of the multi-dimensional dataset visualization, where color intensity or hue represents data density. This visual encoding enables users to quickly distinguish dense from sparse regions and navigate to dense areas for analysis, significantly reducing the time required to locate meaningful data.
2Reliability
If users analyze highly sparse cubes where data exists at only a small subset of dimensions, then complete data coverage is maintained, but excessive computational resources are consumed
Solution Approach 1:
The patent extracts and highlights only the dense data regions from the multi-dimensional dataset, separating them from sparse regions. By visually isolating and emphasizing dense areas through positioning and color coding, the system allows users to focus computational and analytical resources on relevant data subsets while maintaining awareness of the complete data structure.
Solution Approach 2:
The patent segments the multi-dimensional dataset visualization into distinct dense and sparse regions. This segmentation enables users to selectively analyze dense regions without processing the entire sparse dataset, thereby reducing computational resource consumption while maintaining data coverage through the ability to navigate to any segment as needed.
3Stability of the object's composition
If traditional visualization methods are used for multi-dimensional data, then data structure is preserved, but user efficiency in data exploration is reduced
Solution Approach 1:
The patent adds a visual dimension to traditional multi-dimensional data representation by mapping data density to spatial position on the display. This additional visual layer enhances user efficiency in exploring dense data regions while the underlying data structure remains intact and can be accessed through the visualization interface.
Solution Approach 2:
The patent overlays color information on the traditional data structure visualization, where color encoding indicates data density without altering the underlying data organization. This visual enhancement allows users to quickly identify dense regions for efficient exploration while maintaining the integrity and accessibility of the complete data structure.
Data Source
AI summary
A computer-implemented method, system and computer program product for visualizing sparse multi-dimensional data. A multi-dimensional dataset (“dataset”) is converted into a three-dimensional architecture and the remaining dimensions, if any, are arranged into one or more planes. The sparse numeric data of the dataset is converted into multiple planes based on partitioning the three-dimensional architecture by the most sparse dimension and aligning the remaining two-dimensions as two-dimensional planes. Colors or shades of colors are assigned to these planes based on the density quantum of the data present in the planes. Furthermore, planes of the dataset are constructed using the assigned colors or shades of color and the defined opacity values of the planes. The constructed planes are mapped to the dataset in the form of a cube(s) and possibly two-dimensional planes, where the darkest color and the least translucent section(s) of the dataset are positioned in the center of the cube(s).