Visualization Data Reduction for Large-Scale Simulation Phenomena
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Solution Overview
Problem
Large-scale simulation data visualization is hindered by excessive data size, making it difficult to achieve accurate analysis and efficient data reduction while maintaining visualization quality.
Innovation Solution
The method involves extracting cross-section shape and texture information relevant to a phenomenon, generating visualization data, and displaying the line segment in a three-dimensional image using selected cross-section shape and texture information, allowing for data reduction and effective visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If large-scale simulation data is visualized using conventional methods, then comprehensive phenomenon information is preserved, but data size becomes excessively large (petabyte order) making processing and display inefficient
Solution Approach 1:
The patent extracts only the essential phenomenon information from large-scale simulation data by identifying phenomenon portions based on specific criteria (such as regions where physical quantities exceed thresholds or exhibit significant changes). This extraction process isolates critical data elements while discarding redundant information, thereby reducing data size from petabyte order to manageable levels while preserving key phenomenon characteristics for effective visualization.
Solution Approach 2:
The patent applies local quality by differentiating data processing based on spatial and functional characteristics. Different regions of the simulation data are processed differently: phenomenon portions are extracted and visualized with high detail, while non-phenomenon regions are either omitted or represented with lower fidelity. This selective processing maintains visualization quality for critical areas while reducing overall data requirements.
2Productivity
If data reduction is applied to large-scale simulation data, then processing efficiency improves, but visualization accuracy may deteriorate
Solution Approach 1:
The patent incorporates feedback mechanisms where the visualization system continuously evaluates the extracted phenomenon information against original simulation data. This feedback loop ensures that extracted features accurately represent the underlying physical phenomena, allowing the system to adjust extraction parameters and maintain visualization accuracy while achieving significant data reduction. The feedback process validates that reduced data sets preserve essential phenomenon characteristics.
3Loss of information
If conventional visualization methods are used for large-scale data, then complete phenomenon coverage is achieved, but analysis efficiency decreases due to excessive data volume
Solution Approach 1:
The patent segments the large-scale simulation data into distinct phenomenon portions and non-phenomenon portions based on predefined criteria. This segmentation divides the overwhelming data set into manageable units, where only phenomenon portions requiring analysis are processed and visualized in detail. This segmentation approach maintains comprehensive phenomenon coverage while dramatically reducing the time required for analysis by focusing computational resources on relevant data segments.
Data Source
AI summary
An information processing apparatus is disclosed. A processor selects cross-section shape information and texture information corresponding to a view direction from a memory. The memory stores the cross-section shape information representing a cross-section shape and the texture information representing a texture of a cross-section for each of cross-sections in a vicinity of a line segment pertinent to a phenomenon portion. The processor generates visualization data used to visualize the line segment in a three dimensional image by using the cross-section shape information and the texture information being selected and displays the line segment based on the visualization data on a display part.