Single-Dimensional Data Set Rendering for Large Tree Structures
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Solution Overview
Problem
Existing methods for rendering large, multi-dimensional data sets in a tree structure face challenges in efficiently displaying and updating the data, particularly when dealing with collapsible nodes, as they often require rendering all data and struggle with calculating which parts should be visible within a viewport, leading to performance issues and difficulties in navigation.
Innovation Solution
Converting the multi-dimensional data set into a single-dimensional data set that maintains structural information, where each node is assigned a reference, allowing for incremental rendering and updating based on viewport changes, and using these references to determine which nodes to render, thereby optimizing the display of only the visible portion of the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all data is rendered in a tree structure, then complete data visibility is achieved, but rendering performance deteriorates and navigation becomes difficult
Solution Approach 1:
The patent segments the multi-dimensional data set into multiple one-dimensional data sets, each representing a different view or dimension of the data. This allows the system to render only relevant portions of the data at any given time, improving rendering performance while maintaining the ability to access complete data through navigation between segments.
Solution Approach 2:
The patent transforms the multi-dimensional data structure into one-dimensional representations, changing the dimensional perspective to simplify rendering. By projecting multi-dimensional data onto one-dimensional views, the system achieves better rendering performance while preserving data relationships through structured organization.
2Ease of operation
If collapsible nodes are implemented in tree structure, then user navigation is improved, but calculation complexity increases for determining visible nodes
Solution Approach 1:
Instead of calculating which nodes should be collapsed based on complex hierarchical relationships, the patent inverts the approach by directly assigning reference values to nodes that should remain visible. This simplifies the calculation by focusing on visible nodes rather than computing collapse states from parent-child relationships.
Solution Approach 2:
The patent introduces reference values as an intermediary mechanism to track visible nodes. These reference values act as mediators between the data structure and the rendering system, eliminating the need for complex real-time calculations to determine node visibility and simplifying the navigation logic.
3Loss of information
If traditional HTML mapping is used for tree structure rendering, then data completeness is maintained, but memory consumption and rendering overhead increase
Solution Approach 1:
The patent extracts only the essential structural information needed for rendering from the complete multi-dimensional data set. By creating simplified one-dimensional representations that contain only necessary data for each view, the system reduces memory consumption while maintaining data completeness through the ability to navigate between different extracted views.
Data Source
AI summary
In one example, a multi-dimensional data set is converted into a single-dimensional data set that includes structural information associated with the multi-dimensional data set, such that root nodes and each node in the single-dimensional node for which each ancestor node of the node is an expanded node is assigned a reference. A view is caused to be displayed, including using the single-dimensional data set to render at least a selected portion of the single-dimensional data set.