Browser Data Visualization Rendering with QuadTree Point Simplification
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Solution Overview
Problem
Visualizing large datasets in a browser-based environment leads to browser crashes and sluggish performance due to memory constraints and processing demands, hindering effective data analysis.
Innovation Solution
Customizing the Douglas-Peucker algorithm with a QuadTree data structure, incorporating incremental sampling, parallel processing, and dynamic runtime tolerance to optimize rendering of large datasets, reducing data complexity and processing time while improving resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional browser rendering methods are used to display large datasets, then data visualization can be performed, but browser crashes and sluggish performance occur due to memory constraints and processing demands
Solution Approach 1:
The patent extracts and removes redundant data points from the dataset using the Douglas-Peucker algorithm. By calculating the maximum deviation of each point from the line segment connecting its neighbors, the algorithm identifies and eliminates points that do not significantly contribute to the visual representation, thereby reducing the number of data points rendered while maintaining visualization accuracy and preventing browser crashes
Solution Approach 2:
The patent segments the rendering process into multiple stages: data processing (where the Douglas-Peucker algorithm simplifies the dataset), spatial organization (using QuadTree data structures to partition the visualization space), and rendering (where only the essential data points are displayed). This segmentation allows the system to handle large datasets by processing and displaying only the most critical information at each stage
2Quantity of substance
If traditional browser rendering methods are used to display large datasets, then data visualization can be performed, but processing speed becomes sluggish due to the time taken to render each data point
Solution Approach 1:
The patent extracts and removes redundant data points using the Douglas-Peucker algorithm, which calculates the maximum deviation of each point from the line segment connecting its neighbors. By eliminating points with minimal deviation, the system reduces the number of rendering operations required while maintaining the essential visual characteristics of the data, thereby significantly improving rendering speed
Solution Approach 2:
The patent performs preliminary data simplification before rendering by applying the Douglas-Peucker algorithm to reduce the dataset size. This pre-processing step occurs outside the main rendering loop, allowing the actual rendering operation to work with a reduced set of essential data points, thereby accelerating the rendering process without compromising visual accuracy
3Measurement precision
If all data points are rendered to maintain visual accuracy, then visualization precision is maintained, but memory usage increases dramatically
Solution Approach 1:
The patent extracts and removes redundant data points using the Douglas-Peucker algorithm, which calculates the maximum deviation of each point from the line segment connecting its neighbors. By eliminating points with minimal deviation, the system maintains visualization accuracy while significantly reducing the number of data points stored and rendered, thereby decreasing JavaScript memory usage
Solution Approach 2:
The patent changes the parameter of data point density by dynamically adjusting which points are retained based on their spatial distribution and visual importance. The Douglas-Peucker algorithm evaluates each point's contribution to the overall shape and retains only those with maximum deviation, effectively changing the density parameter to balance accuracy and memory efficiency
Data Source
AI summary
A computing device executing a browser application obtains a dataset for rendering a data visualization, the dataset including a plurality of data points. The device selects, from the plurality of data points, a first subset of data points according to a statistical data distribution of the dataset. The device recursively applies a first algorithm to the first subset of data points to obtain a final subset of data points. Each of first subset of data points and the final subset of data points has a fewer number of data points than the plurality of data points. The device renders a data visualization using the browser application. The data visualization has a plurality of data marks corresponding to the final subset of data points. The device displays, on the browser application, the data visualization including the plurality of data marks.