Nested Icosahedral Grid for Big Data Visualization
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Solution Overview
Problem
Existing data processing systems are inadequate for handling and analyzing large and complex data sets, known as 'big data,' which often lead to inefficiencies in decision-making due to the lack of effective tools for data visualization and correlation across multiple datasets.
Innovation Solution
A system and method for generating and viewing a nested icosahedral grid, which includes a mesh engine that maps data points onto partitions, allowing for the creation of a nested icosahedral grid structure with an outer and inner set of partitions, enabling efficient organization and visualization of complex data sets through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing applications are used, then data processing can be performed, but they are inadequate for handling large and complex data sets
Solution Approach 1:
The patent segments the data processing system into a nested icosahedral grid structure with multiple hierarchical levels. Each level divides the spherical surface into triangular partitions, allowing big data to be organized into manageable segments that can be processed independently and then integrated, thus enabling traditional applications to handle large-scale data effectively.
Solution Approach 2:
The patent implements a nested grid structure where an inner icosahedral grid is embedded within an outer icosahedral grid. This nesting allows data to be organized at multiple scales simultaneously, with coarse-grained overview data at outer levels and fine-grained detail data at inner levels, enabling efficient processing of complex data sets across different resolutions.
2Productivity
If data points are mapped onto a nested icosahedral grid, then efficient organization and visualization of complex data sets is achieved, but the system complexity increases
Solution Approach 1:
The nested icosahedral grid structure serves multiple functions simultaneously: it organizes data spatially, enables hierarchical navigation, supports multi-scale visualization, and facilitates data correlation across different levels. This multi-functionality justifies the increased structural complexity by delivering comprehensive data processing capabilities that simpler systems cannot provide.
3Loss of information
If conventional data visualization methods are used, then simple data can be displayed, but correlation and comparison across multiple data sets is not possible
Solution Approach 1:
The patent adds a spatial dimension to data visualization by mapping data points onto the surface of nested icosahedral grids. This spherical spatial arrangement allows data from multiple data sets to be visualized concurrently in a unified spatial framework, enabling intuitive correlation and comparison that conventional flat visualization methods cannot achieve.
Data Source
AI summary
A mesh engine can generate a nested icosahedral grid that maps data points of a plurality of data sets onto partitions on the nested icosahedral grid. The nested icosahedral grid can include an outer icosahedral grid with a first set of partition and an inner icosahedral grid with a second set of partitions. A first partition of the first set of partitions can be linked to the inner icosahedral grid.